I investigate a wide range of applications of data science to health, ranging from analysis of medical imaging to diagnostics. My research with the Alfred Hospital led to a revision to Medical Emergency Team protocols that saves $500,000 per annum while improving clinical outcomes.
Publications
Tan, George S. Q.; Miller, Lynn; Wade, Sam; Ilomäki, Jenni; Lukose, Dickson; Rong, Jia; Webb, Geoffrey I.
Clinical Pharmacology and Therapeutics, vol. 119, no. 1, pp. 228-240, 2026, ISSN: 1532-6535.
@article{Tan2025a,
title = {Association Discovery Approach in Healthcare Big Data to Identify Drug Safety and Drug Repurposing Signals},
author = {George S. Q. Tan and Lynn Miller and Sam Wade and Jenni Ilom\"{a}ki and Dickson Lukose and Jia Rong and Geoffrey I. Webb},
doi = {10.1002/cpt.70080},
issn = {1532-6535},
year = {2026},
date = {2026-10-01},
journal = {Clinical Pharmacology and Therapeutics},
volume = {119},
number = {1},
pages = {228-240},
publisher = {Wiley},
abstract = {Data science approaches have been increasingly implemented in healthcare big data to evaluate the safety and effectiveness of drugs. Association discovery is a data mining approach that finds potentially associated elements in high-dimensional data. We present a novel implementation of the association discovery approach in longitudinal healthcare data to identify drug safety and drug repurposing signals from positive and inverse associations between drug use and clinical outcomes, respectively. We used the 10% sample data from the Australian Pharmaceutical Benefits Scheme (2014\textendash2024), which comprises prescription claims records. Using the Magnum Opus association discovery tool, we identified associations between a wide range of drugs and three common chronic medical conditions (i.e., coronary artery disease, type 2 diabetes, epilepsy). Cases with the conditions were identified using the supply of indicator drug(s) as a proxy for the conditions and matched to controls not supplied the indicator drug(s). Drug use was defined using Anatomical Therapeutic Chemical (ATC) codes supplied during a one-year lookback period before the supply of the indicator drug(s). We also evaluated combinations of up to four drugs and identified associations at the ATC level of drug class(es). In this study, we reproduced several known adverse drug events and protective drug effects, while some associations were attributable to confounding, mutual indications, or reverse causation. The remaining associations may represent previously uncharacterized drug safety and drug repurposing signals, necessitating further validation. We also discussed methodological differences between this association discovery approach and other similar data science approaches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Data science approaches have been increasingly implemented in healthcare big data to evaluate the safety and effectiveness of drugs. Association discovery is a data mining approach that finds potentially associated elements in high-dimensional data. We present a novel implementation of the association discovery approach in longitudinal healthcare data to identify drug safety and drug repurposing signals from positive and inverse associations between drug use and clinical outcomes, respectively. We used the 10% sample data from the Australian Pharmaceutical Benefits Scheme (2014–2024), which comprises prescription claims records. Using the Magnum Opus association discovery tool, we identified associations between a wide range of drugs and three common chronic medical conditions (i.e., coronary artery disease, type 2 diabetes, epilepsy). Cases with the conditions were identified using the supply of indicator drug(s) as a proxy for the conditions and matched to controls not supplied the indicator drug(s). Drug use was defined using Anatomical Therapeutic Chemical (ATC) codes supplied during a one-year lookback period before the supply of the indicator drug(s). We also evaluated combinations of up to four drugs and identified associations at the ATC level of drug class(es). In this study, we reproduced several known adverse drug events and protective drug effects, while some associations were attributable to confounding, mutual indications, or reverse causation. The remaining associations may represent previously uncharacterized drug safety and drug repurposing signals, necessitating further validation. We also discussed methodological differences between this association discovery approach and other similar data science approaches.
Wang, Xiaoyu; Pan, Tong; Chen, Sihan; Webb, Geoffrey I.; Jiang, Yunzhe; Rozowsky, Joel; Gerstein, Mark; Song, Jiangning
Genome Biology, vol. 27, 2026, ISSN: 1474-760X.
@article{Wang2026,
title = {Predicting disease-specific histone modifications and functional effects of non-coding variants by leveraging DNA language models},
author = {Xiaoyu Wang and Tong Pan and Sihan Chen and Geoffrey I. Webb and Yunzhe Jiang and Joel Rozowsky and Mark Gerstein and Jiangning Song},
doi = {10.1186/s13059-026-04003-3},
issn = {1474-760X},
year = {2026},
date = {2026-02-01},
journal = {Genome Biology},
volume = {27},
publisher = {Springer Science and Business Media LLC},
abstract = {Background
Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimer's disease, where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to Alzheimer's disease pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures.
Results
To bridge this gap, we develop a novel large language model-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on Alzheimer's disease as a case study, we integrate epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn Alzheimer's disease -associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of Alzheimer's disease -relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing Alzheimer's disease-associated genetic variants, which show significant enrichment in disease-relevant pathways.
Conclusions
Our framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimer's disease, where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to Alzheimer's disease pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures.
Results
To bridge this gap, we develop a novel large language model-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on Alzheimer's disease as a case study, we integrate epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn Alzheimer's disease -associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of Alzheimer's disease -relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing Alzheimer's disease-associated genetic variants, which show significant enrichment in disease-relevant pathways.
Conclusions
Our framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.
Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimer's disease, where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to Alzheimer's disease pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures.
Results
To bridge this gap, we develop a novel large language model-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on Alzheimer's disease as a case study, we integrate epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn Alzheimer's disease -associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of Alzheimer's disease -relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing Alzheimer's disease-associated genetic variants, which show significant enrichment in disease-relevant pathways.
Conclusions
Our framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.
Yao, Shunhan; Huang, Yuanxiang; Wang, Xiaoyu; Zhang, Yiwen; Paixao, Ian Costa; Wang, Zhikang; Chai, Charla Lu; Wang, Hongtao; Lu, Dinggui; Webb, Geoffrey I; Li, Shanshan; Guo, Yuming; Chen, Qingfeng; Song, Jiangning
A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors
Scientific Data, vol. 12, 2025, ISSN: 2052-4463.
@article{Yao2025,
title = {A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors},
author = {Shunhan Yao and Yuanxiang Huang and Xiaoyu Wang and Yiwen Zhang and Ian Costa Paixao and Zhikang Wang and Charla Lu Chai and Hongtao Wang and Dinggui Lu and Geoffrey I Webb and Shanshan Li and Yuming Guo and Qingfeng Chen and Jiangning Song},
doi = {10.1038/s41597-024-04311-y},
issn = {2052-4463},
year = {2025},
date = {2025-01-01},
journal = {Scientific Data},
volume = {12},
publisher = {Springer Science and Business Media LLC},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramakrishnaiah, Yashpal; Macesic, Nenad; Webb, Geoffrey I.; Peleg, Anton Y.; Tyagi, Sonika
International Journal of Medical Informatics, pp. 105816, 2025, ISSN: 1386-5056.
@article{Ramakrishnaiah2025,
title = {EHR-ML: A Data-Driven Framework for Designing Machine Learning Applications with Electronic Health Records},
author = {Yashpal Ramakrishnaiah and Nenad Macesic and Geoffrey I. Webb and Anton Y. Peleg and Sonika Tyagi},
url = {https://www.sciencedirect.com/science/article/pii/S1386505625000334},
doi = {https://doi.org/10.1016/j.ijmedinf.2025.105816},
issn = {1386-5056},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Medical Informatics},
pages = {105816},
abstract = {Objective
The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions. 2) ad-hoc data preparation and design of machine learning strategies. 3) manual subjective adjustment of design parameters resulting in sub-optimal performance. 4) EHR specific challenges regarding data biases affecting the model outcomes and unique intermittent temporal nature of the data necessitating specialised handling 5) lack of cross-institutional data validations.
Methods
To address these challenges, EHR-ML, provides an easy to use structured framework for designing optimum machine learning applications in a data-driven manner. The framework supports ingestion of local institutional electronic health records (EHRs) and process standardisation. The study design and parameter optimisisation is done in a fully data-driven evidence-based approach. It seamlessly integrating with existing quality control tools. To handle the unique characteristics of the EHR data, it offers customisable ensemble models. It enables the acquisition of EHR data from diverse systems and harmonise them into common formats following international standards.
Results
The effectiveness of the EHR-ML is demonstrated through a series of case studies. These studies highlight its capability to develop high-performance models in a fully automated manner, consistently surpassing the performance of traditional methodologies. Furthermore, they exhibited strong generalisability across diverse healthcare settings.
Discussion and Conclusion
EHR-ML enhances the clinical relevance and accuracy of predictive models by incorporating local context into machine learning applications. Additionally, by providing an user-friendly fully-automated framework, it facilitates rapid hypothesis testing aimed to generate localised biomedical knowledge.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions. 2) ad-hoc data preparation and design of machine learning strategies. 3) manual subjective adjustment of design parameters resulting in sub-optimal performance. 4) EHR specific challenges regarding data biases affecting the model outcomes and unique intermittent temporal nature of the data necessitating specialised handling 5) lack of cross-institutional data validations.
Methods
To address these challenges, EHR-ML, provides an easy to use structured framework for designing optimum machine learning applications in a data-driven manner. The framework supports ingestion of local institutional electronic health records (EHRs) and process standardisation. The study design and parameter optimisisation is done in a fully data-driven evidence-based approach. It seamlessly integrating with existing quality control tools. To handle the unique characteristics of the EHR data, it offers customisable ensemble models. It enables the acquisition of EHR data from diverse systems and harmonise them into common formats following international standards.
Results
The effectiveness of the EHR-ML is demonstrated through a series of case studies. These studies highlight its capability to develop high-performance models in a fully automated manner, consistently surpassing the performance of traditional methodologies. Furthermore, they exhibited strong generalisability across diverse healthcare settings.
Discussion and Conclusion
EHR-ML enhances the clinical relevance and accuracy of predictive models by incorporating local context into machine learning applications. Additionally, by providing an user-friendly fully-automated framework, it facilitates rapid hypothesis testing aimed to generate localised biomedical knowledge.
The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions. 2) ad-hoc data preparation and design of machine learning strategies. 3) manual subjective adjustment of design parameters resulting in sub-optimal performance. 4) EHR specific challenges regarding data biases affecting the model outcomes and unique intermittent temporal nature of the data necessitating specialised handling 5) lack of cross-institutional data validations.
Methods
To address these challenges, EHR-ML, provides an easy to use structured framework for designing optimum machine learning applications in a data-driven manner. The framework supports ingestion of local institutional electronic health records (EHRs) and process standardisation. The study design and parameter optimisisation is done in a fully data-driven evidence-based approach. It seamlessly integrating with existing quality control tools. To handle the unique characteristics of the EHR data, it offers customisable ensemble models. It enables the acquisition of EHR data from diverse systems and harmonise them into common formats following international standards.
Results
The effectiveness of the EHR-ML is demonstrated through a series of case studies. These studies highlight its capability to develop high-performance models in a fully automated manner, consistently surpassing the performance of traditional methodologies. Furthermore, they exhibited strong generalisability across diverse healthcare settings.
Discussion and Conclusion
EHR-ML enhances the clinical relevance and accuracy of predictive models by incorporating local context into machine learning applications. Additionally, by providing an user-friendly fully-automated framework, it facilitates rapid hypothesis testing aimed to generate localised biomedical knowledge.
Nguyen, Hoai-An; Peleg, Anton Y.; Song, Jiangning; Antony, Bhavna; Webb, Geoffrey I.; Wisniewski, Jessica A.; Blakeway, Luke V.; Badoordeen, Gnei Z.; Theegala, Ravali; Zisis, Helen; Dowe, David L.; Macesic, Nenad
mSystems, 2024, ISSN: 2379-5077.
@article{Nguyen2024,
title = {Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra},
author = {Hoai-An Nguyen and Anton Y. Peleg and Jiangning Song and Bhavna Antony and Geoffrey I. Webb and Jessica A. Wisniewski and Luke V. Blakeway and Gnei Z. Badoordeen and Ravali Theegala and Helen Zisis and David L. Dowe and Nenad Macesic},
editor = {Yu-Liang Yang},
doi = {10.1128/msystems.00789-24},
issn = {2379-5077},
year = {2024},
date = {2024-08-01},
journal = {mSystems},
publisher = {American Society for Microbiology},
abstract = {Matrix-assisted laser desorption/ionization\textendashtime of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.
Nguyen, Anh T. N.; Nguyen, Diep T. N.; Koh, Huan Yee; Toskov, Jason; MacLean, William; Xu, Andrew; Zhang, Daokun; Webb, Geoffrey I.; May, Lauren T.; Halls, Michelle L.
The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery
British Journal of Pharmacology, vol. 181, no. 14, pp. 2371-2384, 2024.
@article{Nguyen,
title = {The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery},
author = {Anh T. N. Nguyen and Diep T. N. Nguyen and Huan Yee Koh and Jason Toskov and William MacLean and Andrew Xu and Daokun Zhang and Geoffrey I. Webb and Lauren T. May and Michelle L. Halls},
doi = {10.1111/bph.16140},
year = {2024},
date = {2024-01-01},
journal = {British Journal of Pharmacology},
volume = {181},
number = {14},
pages = {2371-2384},
abstract = {The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process faster, smarter and cheaper, we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process faster, smarter and cheaper, we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery.
Jung, Monica; Lukose, Dickson; Nielsen, Suzanne; Bell, J. Simon; Webb, Geoffrey I.; Ilomaki, Jenni
British Journal of Clinical Pharmacology, vol. 89, no. 2, pp. 914-920, 2023.
@article{Jung,
title = {COVID-19 restrictions and the incidence and prevalence of prescription opioid use in Australia - a nation-wide study},
author = {Monica Jung and Dickson Lukose and Suzanne Nielsen and J. Simon Bell and Geoffrey I. Webb and Jenni Ilomaki},
doi = {10.1111/bcp.15577},
year = {2023},
date = {2023-01-01},
journal = {British Journal of Clinical Pharmacology},
volume = {89},
number = {2},
pages = {914-920},
abstract = {The COVID-19 pandemic has disrupted seeking and delivery of healthcare. Different Australian jurisdictions implemented different COVID-19 restrictions. We used Australian national pharmacy dispensing data to conduct interrupted time series analyses to examine the incidence and prevalence of opioid dispensing in different jurisdictions. Following nationwide COVID-19 restrictions, the incidence dropped by -0.40 [-0.50, -0.31], -0.33 [-0.46, -0.21] and -0.21 [-0.37, -0.04] /1000 people/week and prevalence dropped by -0.85 [-1.39, -0.31], -0.54 [-1.01, -0.07] and -0.62 [-0.99, -0.25] /1000 people/week in Victoria, New South Wales and other jurisdictions, respectively. Incidence and prevalence increased by 0.29 [0.13, 0.44] and 0.72 [0.11, 1.33] /1000 people/week, respectively in Victoria post-lockdown; no significant changes were observed in other jurisdictions. No significant changes were observed in the initiation of long-term opioid use in any jurisdictions. More stringent restrictions coincided with more pronounced reductions in overall opioid initiation, but initiation of long-term opioid use did not change.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The COVID-19 pandemic has disrupted seeking and delivery of healthcare. Different Australian jurisdictions implemented different COVID-19 restrictions. We used Australian national pharmacy dispensing data to conduct interrupted time series analyses to examine the incidence and prevalence of opioid dispensing in different jurisdictions. Following nationwide COVID-19 restrictions, the incidence dropped by -0.40 [-0.50, -0.31], -0.33 [-0.46, -0.21] and -0.21 [-0.37, -0.04] /1000 people/week and prevalence dropped by -0.85 [-1.39, -0.31], -0.54 [-1.01, -0.07] and -0.62 [-0.99, -0.25] /1000 people/week in Victoria, New South Wales and other jurisdictions, respectively. Incidence and prevalence increased by 0.29 [0.13, 0.44] and 0.72 [0.11, 1.33] /1000 people/week, respectively in Victoria post-lockdown; no significant changes were observed in other jurisdictions. No significant changes were observed in the initiation of long-term opioid use in any jurisdictions. More stringent restrictions coincided with more pronounced reductions in overall opioid initiation, but initiation of long-term opioid use did not change.
Huynh, Viet; Say, Buser; Vogel, Peter; Cao, Lucy; Webb, Geoffrey I; Aleti, Aldeida
Rapid Identification of Protein Formulations with Bayesian Optimisation
2023 International Conference on Machine Learning and Applications (ICMLA), pp. 776-781, 2023.
@inproceedings{Huynh2023,
title = {Rapid Identification of Protein Formulations with Bayesian Optimisation},
author = {Viet Huynh and Buser Say and Peter Vogel and Lucy Cao and Geoffrey I Webb and Aldeida Aleti},
doi = {10.1109/ICMLA58977.2023.00113},
year = {2023},
date = {2023-01-01},
booktitle = {2023 International Conference on Machine Learning and Applications (ICMLA)},
pages = {776-781},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Wang, Yanan; Wang, Yu Guang; Hu, Changyuan; Li, Ming; Fan, Yanan; Otter, Nina; Sam, Ikuan; Gou, Hongquan; Hu, Yiqun; Kwok, Terry; Zalcberg, John; Boussioutas, Alex; Daly, Roger J.; Montfar, Guido; Li, Pietro; Xu, Dakang; Webb, Geoffrey I.; Song, Jiangning
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer
npj Precision Oncology, vol. 6, no. 1, 2022, ISSN: 2397-768X.
@article{Wang2022,
title = {Cell graph neural networks enable the precise prediction of patient survival in gastric cancer},
author = {Yanan Wang and Yu Guang Wang and Changyuan Hu and Ming Li and Yanan Fan and Nina Otter and Ikuan Sam and Hongquan Gou and Yiqun Hu and Terry Kwok and John Zalcberg and Alex Boussioutas and Roger J. Daly and Guido Montfar and Pietro Li and Dakang Xu and Geoffrey I. Webb and Jiangning Song},
url = {https://rdcu.be/cQeFD},
doi = {10.1038/s41698-022-00285-5},
issn = {2397-768X},
year = {2022},
date = {2022-01-01},
journal = {npj Precision Oncology},
volume = {6},
number = {1},
abstract = {Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960+/-0.01, and 0.771+/-0.024 to 0.904+/-0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the 'digital grade' cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value \<=0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960+/-0.01, and 0.771+/-0.024 to 0.904+/-0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the 'digital grade' cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value <=0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.
Livori, Adam C; Lukose, Dickson; Bell, J Simon; Webb, Geoffrey I; Ilomaki, Jenni
Did Australia's COVID-19 restrictions impact statin incidence, prevalence or adherence?
Current Problems in Cardiology, pp. 101576, 2022, ISSN: 0146-2806.
@article{Livori2022,
title = {Did Australia's COVID-19 restrictions impact statin incidence, prevalence or adherence?},
author = {Adam C Livori and Dickson Lukose and J Simon Bell and Geoffrey I Webb and Jenni Ilomaki},
doi = {10.1016/j.cpcardiol.2022.101576},
issn = {0146-2806},
year = {2022},
date = {2022-01-01},
journal = {Current Problems in Cardiology},
pages = {101576},
abstract = {Objective
COVID-19 restrictions may have an unintended consequence of limiting access to cardiovascular care. Australia implemented adaptive interventions (e.g. telehealth consultations, digital image prescriptions, continued dispensing, medication delivery) to maintain medication access. This study investigated whether COVID-19 restrictions in different jurisdictions coincided with changes in statin incidence, prevalence and adherence.
Methods Analysis of a 10% random sample of national medication claims data from January 2018 to December 2020 was conducted across three Australian jurisdictions. Weekly incidence and prevalence were estimated by dividing the number statin initiations and any statin dispensing by the Australian population aged 18-99 years. Statin adherence was analysed across the jurisdictions and years, with adherence categorised as \<40%, 40-79% and \>=80% based on dispensings per calendar year.
Results Overall, 309,123, 315,703 and 324,906 people were dispensed and 39029, 39816, and 44979 initiated statins in 2018, 2019 and 2020 respectively. Two waves of COVID-19 restrictions in 2020 coincided with no meaningful change in statin incidence or prevalence per week when compared to 2018 and 2019. Incidence increased 0.3% from 23.7 to 26.2 per 1000 people across jurisdictions in 2020 compared to 2019. Prevalence increased 0.14% from 158.5 to 159.9 per 1000 people across jurisdictions in 2020 compared to 2019. The proportion of adults with \>=80% adherence increased by 3.3% in Victoria, 1.4% in NSW and 1.8% in other states and territories between 2019 and 2020.
Conclusions
COVID-19 restrictions did not coincide with meaningful changes in the incidence, prevalence or adherence to statins suggesting adaptive interventions succeeded in maintaining access to cardiovascular medications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
COVID-19 restrictions may have an unintended consequence of limiting access to cardiovascular care. Australia implemented adaptive interventions (e.g. telehealth consultations, digital image prescriptions, continued dispensing, medication delivery) to maintain medication access. This study investigated whether COVID-19 restrictions in different jurisdictions coincided with changes in statin incidence, prevalence and adherence.
Methods Analysis of a 10% random sample of national medication claims data from January 2018 to December 2020 was conducted across three Australian jurisdictions. Weekly incidence and prevalence were estimated by dividing the number statin initiations and any statin dispensing by the Australian population aged 18-99 years. Statin adherence was analysed across the jurisdictions and years, with adherence categorised as <40%, 40-79% and >=80% based on dispensings per calendar year.
Results Overall, 309,123, 315,703 and 324,906 people were dispensed and 39029, 39816, and 44979 initiated statins in 2018, 2019 and 2020 respectively. Two waves of COVID-19 restrictions in 2020 coincided with no meaningful change in statin incidence or prevalence per week when compared to 2018 and 2019. Incidence increased 0.3% from 23.7 to 26.2 per 1000 people across jurisdictions in 2020 compared to 2019. Prevalence increased 0.14% from 158.5 to 159.9 per 1000 people across jurisdictions in 2020 compared to 2019. The proportion of adults with >=80% adherence increased by 3.3% in Victoria, 1.4% in NSW and 1.8% in other states and territories between 2019 and 2020.
Conclusions
COVID-19 restrictions did not coincide with meaningful changes in the incidence, prevalence or adherence to statins suggesting adaptive interventions succeeded in maintaining access to cardiovascular medications.
COVID-19 restrictions may have an unintended consequence of limiting access to cardiovascular care. Australia implemented adaptive interventions (e.g. telehealth consultations, digital image prescriptions, continued dispensing, medication delivery) to maintain medication access. This study investigated whether COVID-19 restrictions in different jurisdictions coincided with changes in statin incidence, prevalence and adherence.
Methods Analysis of a 10% random sample of national medication claims data from January 2018 to December 2020 was conducted across three Australian jurisdictions. Weekly incidence and prevalence were estimated by dividing the number statin initiations and any statin dispensing by the Australian population aged 18-99 years. Statin adherence was analysed across the jurisdictions and years, with adherence categorised as <40%, 40-79% and >=80% based on dispensings per calendar year.
Results Overall, 309,123, 315,703 and 324,906 people were dispensed and 39029, 39816, and 44979 initiated statins in 2018, 2019 and 2020 respectively. Two waves of COVID-19 restrictions in 2020 coincided with no meaningful change in statin incidence or prevalence per week when compared to 2018 and 2019. Incidence increased 0.3% from 23.7 to 26.2 per 1000 people across jurisdictions in 2020 compared to 2019. Prevalence increased 0.14% from 158.5 to 159.9 per 1000 people across jurisdictions in 2020 compared to 2019. The proportion of adults with >=80% adherence increased by 3.3% in Victoria, 1.4% in NSW and 1.8% in other states and territories between 2019 and 2020.
Conclusions
COVID-19 restrictions did not coincide with meaningful changes in the incidence, prevalence or adherence to statins suggesting adaptive interventions succeeded in maintaining access to cardiovascular medications.
Abourayya, Amr; Kamp, Michael; Ayday, Erman; Kleesiek, Jens; Rao, Kanishka; Webb, Geoffrey I.; Rao, Bharat
AIMHI: Protecting Sensitive Data through Federated Co-Training
Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022), 2022.
@inproceedings{Abourayya2022,
title = {AIMHI: Protecting Sensitive Data through Federated Co-Training},
author = {Amr Abourayya and Michael Kamp and Erman Ayday and Jens Kleesiek and Kanishka Rao and Geoffrey I. Webb and Bharat Rao},
url = {https://openreview.net/forum?id=6uJo_mWAmHZ},
year = {2022},
date = {2022-01-01},
booktitle = {Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Wang, Yanan; Coudray, Nicolas; Zhao, Yun; Li, Fuyi; Hu, Changyuan; Zhang, Yao-Zhong; Imoto, Seiya; Tsirigos, Aristotelis; Webb, Geoffrey I; Daly, Roger J; Song, Jiangning
HEAL: an automated deep learning framework for cancer histopathology image analysis
Bioinformatics, vol. 37, no. 22, pp. 4291-4295, 2021.
@article{Wang2021,
title = {HEAL: an automated deep learning framework for cancer histopathology image analysis},
author = {Yanan Wang and Nicolas Coudray and Yun Zhao and Fuyi Li and Changyuan Hu and Yao-Zhong Zhang and Seiya Imoto and Aristotelis Tsirigos and Geoffrey I Webb and Roger J Daly and Jiangning Song},
doi = {10.1093/bioinformatics/btab380},
year = {2021},
date = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {22},
pages = {4291-4295},
publisher = {Oxford University Press (OUP)},
abstract = {Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational environment and of hyperparameter optimization, which hinder deployment and reduce reproducibility.Here, we propose HEAL, a deep learning-based automated framework for easy, flexible, and multi-faceted histopathological image analysis. We demonstrate its utility and functionality by performing two case studies on lung cancer and one on colon cancer. Leveraging the capability of Docker, HEAL represents an ideal end-to-end tool to conduct complex histopathological analysis and enables deep learning in a broad range of applications for cancer image analysis.Supplementary data are available at Bioinformatics online.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational environment and of hyperparameter optimization, which hinder deployment and reduce reproducibility.Here, we propose HEAL, a deep learning-based automated framework for easy, flexible, and multi-faceted histopathological image analysis. We demonstrate its utility and functionality by performing two case studies on lung cancer and one on colon cancer. Leveraging the capability of Docker, HEAL represents an ideal end-to-end tool to conduct complex histopathological analysis and enables deep learning in a broad range of applications for cancer image analysis.Supplementary data are available at Bioinformatics online.
Wang, Yanan; Yang, Litao; Webb, Geoffrey I; Ge, Zongyuan; Song, Jiangning
OCTID: a one-class learning-based Python package for tumor image detection
Bioinformatics, vol. 37, no. 21, pp. 3986-3988, 2021, ISSN: 1367-4803.
@article{10.1093/bioinformatics/btab416,
title = {OCTID: a one-class learning-based Python package for tumor image detection},
author = {Yanan Wang and Litao Yang and Geoffrey I Webb and Zongyuan Ge and Jiangning Song},
doi = {10.1093/bioinformatics/btab416},
issn = {1367-4803},
year = {2021},
date = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {21},
pages = {3986-3988},
abstract = {Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not. As all tiles within a tumor free slide are tumor free, these can be used to capture tumor-free patterns using the one-class learning strategy. We present a Python package, termed OCTID, which combines a pretrained convolutional neural network (CNN) model, Uniform Manifold Approximation and Projection (UMAP) and one-class support vector machine to achieve accurate tumor tile classification using a training set of tumor free tiles. Benchmarking experiments on four H\&E image datasets achieved remarkable performance in terms of F1-score (0.90?+/-0.06), Matthews correlation coefficient (0.93?+/-0.05) and accuracy (0.94?+/-0.03).Detailed information can be found in the Supplementary File.Supplementary data are available at Bioinformatics online.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not. As all tiles within a tumor free slide are tumor free, these can be used to capture tumor-free patterns using the one-class learning strategy. We present a Python package, termed OCTID, which combines a pretrained convolutional neural network (CNN) model, Uniform Manifold Approximation and Projection (UMAP) and one-class support vector machine to achieve accurate tumor tile classification using a training set of tumor free tiles. Benchmarking experiments on four H&E image datasets achieved remarkable performance in terms of F1-score (0.90?+/-0.06), Matthews correlation coefficient (0.93?+/-0.05) and accuracy (0.94?+/-0.03).Detailed information can be found in the Supplementary File.Supplementary data are available at Bioinformatics online.
Ananda-Rajah, Michelle R.; Bergmeir, Christoph; Petitjean, Francois; Slavin, Monica A.; Thursky, Karin A.; Webb, Geoffrey I.
JCO Clinical Cancer Informatics, no. 1, pp. 1-10, 2017.
@article{Ananda-RajahEtAl17,
title = {Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data},
author = {Michelle R. Ananda-Rajah and Christoph Bergmeir and Francois Petitjean and Monica A. Slavin and Karin A. Thursky and Geoffrey I. Webb},
doi = {10.1200/CCI.17.00011},
year = {2017},
date = {2017-01-01},
journal = {JCO Clinical Cancer Informatics},
number = {1},
pages = {1-10},
abstract = {Prospective epidemiologic surveillance of invasive mold disease (IMD) in hematology patients is hampered by the absence of a reliable laboratory prompt. This study develops an expert system for electronic surveillance of IMD that combines probabilities using natural language processing (NLP) of computed tomography (CT) reports with microbiology and antifungal drug data to improve prediction of IMD.MethodsMicrobiology indicators and antifungal drug dispensing data were extracted from hospital information systems at three tertiary hospitals for 123 hematology-oncology patients. Of this group, 64 case patients had 26 probable/proven IMD according to international definitions, and 59 patients were uninfected controls. Derived probabilities from NLP combined with medical expertise identified patients at high likelihood of IMD, with remaining patients processed by a machine-learning classifier trained on all available features. Results Compared with the baseline text classifier, the expert system that incorporated the best performing algorithm (naive Bayes) improved specificity from 50.8% (95% CI, 37.5% to 64.1%) to 74.6% (95% CI, 61.6% to 85.0%), reducing false positives by 48% from 29 to 15; improved sensitivity slightly from 96.9% (95% CI, 89.2% to 99.6%) to 98.4% (95% CI, 91.6% to 100%); and improved receiver operating characteristic area from 73.9% (95% CI, 67.1% to 80.6%) to 92.8% (95% CI, 88% to 97.5%). Conclusion An expert system that uses multiple sources of data (CT reports, microbiology, antifungal drug dispensing) is a promising approach to continuous prospective surveillance of IMD in the hospital, and demonstrates reduced false notifications (positives) compared with NLP of CT reports alone. Our expert system could provide decision support for IMD surveillance, which is critical to antifungal stewardship and improving supportive care in cancer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Prospective epidemiologic surveillance of invasive mold disease (IMD) in hematology patients is hampered by the absence of a reliable laboratory prompt. This study develops an expert system for electronic surveillance of IMD that combines probabilities using natural language processing (NLP) of computed tomography (CT) reports with microbiology and antifungal drug data to improve prediction of IMD.MethodsMicrobiology indicators and antifungal drug dispensing data were extracted from hospital information systems at three tertiary hospitals for 123 hematology-oncology patients. Of this group, 64 case patients had 26 probable/proven IMD according to international definitions, and 59 patients were uninfected controls. Derived probabilities from NLP combined with medical expertise identified patients at high likelihood of IMD, with remaining patients processed by a machine-learning classifier trained on all available features. Results Compared with the baseline text classifier, the expert system that incorporated the best performing algorithm (naive Bayes) improved specificity from 50.8% (95% CI, 37.5% to 64.1%) to 74.6% (95% CI, 61.6% to 85.0%), reducing false positives by 48% from 29 to 15; improved sensitivity slightly from 96.9% (95% CI, 89.2% to 99.6%) to 98.4% (95% CI, 91.6% to 100%); and improved receiver operating characteristic area from 73.9% (95% CI, 67.1% to 80.6%) to 92.8% (95% CI, 88% to 97.5%). Conclusion An expert system that uses multiple sources of data (CT reports, microbiology, antifungal drug dispensing) is a promising approach to continuous prospective surveillance of IMD in the hospital, and demonstrates reduced false notifications (positives) compared with NLP of CT reports alone. Our expert system could provide decision support for IMD surveillance, which is critical to antifungal stewardship and improving supportive care in cancer.
Bergmeir, Christoph; Bilgrami, Irma; Bain, Christopher; Webb, Geoffrey I; Orosz, Judit; Pilcher, David
PLoS ONE, vol. 12, no. 12, 2017.
@article{BergmeirEtAl2017,
title = {Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis},
author = {Christoph Bergmeir and Irma Bilgrami and Christopher Bain and Geoffrey I Webb and Judit Orosz and David Pilcher},
doi = {10.1371/journal.pone.0188688},
year = {2017},
date = {2017-01-01},
journal = {PLoS ONE},
volume = {12},
number = {12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Siu, K. K. W.; Butler, S. M.; Beveridge, T.; Gillam, J. E.; Hall, C. J.; Kaye, A. H.; Lewis, R. A.; Mannan, K.; McLoughlin, G.; Pearson, S.; Round, A. R.; E., Schultke; Webb, G. I.; Wilkinson, S. J.
Identifying markers of pathology in SAXS data of malignant tissues of the brain
Nuclear Instruments and Methods in Physics Research A, vol. 548, pp. 140-146, 2005.
@article{SiuEtAl05,
title = {Identifying markers of pathology in SAXS data of malignant tissues of the brain},
author = {K. K. W. Siu and S. M. Butler and T. Beveridge and J. E. Gillam and C. J. Hall and A. H. Kaye and R. A. Lewis and K. Mannan and G. McLoughlin and S. Pearson and A. R. Round and Schultke E. and G. I. Webb and S. J. Wilkinson},
doi = {10.1016/j.nima.2005.03.081},
year = {2005},
date = {2005-01-01},
journal = {Nuclear Instruments and Methods in Physics Research A},
volume = {548},
pages = {140-146},
publisher = {Elsevier},
abstract = {Conventional neuropathological analysis for brain malignancies is heavily reliant on the observation of morphological abnormalities, observed in thin, stained sections of tissue. Small Angle X-ray Scattering (SAXS) data provide an alternative means of distinguishing pathology by examining the ultra-structural (nanometer length scales) characteristics of tissue. To evaluate the diagnostic potential of SAXS for brain tumors, data was collected from normal, malignant and benign tissues of the human brain at station 2.1 of the Daresbury Laboratory Synchrotron Radiation Source and subjected to data mining and multivariate statistical analysis. The results suggest SAXS data may be an effective classi.er of malignancy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Conventional neuropathological analysis for brain malignancies is heavily reliant on the observation of morphological abnormalities, observed in thin, stained sections of tissue. Small Angle X-ray Scattering (SAXS) data provide an alternative means of distinguishing pathology by examining the ultra-structural (nanometer length scales) characteristics of tissue. To evaluate the diagnostic potential of SAXS for brain tumors, data was collected from normal, malignant and benign tissues of the human brain at station 2.1 of the Daresbury Laboratory Synchrotron Radiation Source and subjected to data mining and multivariate statistical analysis. The results suggest SAXS data may be an effective classi.er of malignancy.
Butler, S. M.; Webb, G. I.; Lewis, R. A.
A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images
Gedeon, T. D.; Fung, L. C. C. (Ed.): Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03), pp. 677-685, Springer, Berlin/Heidelberg, 2003.
@inproceedings{ButlerWebbLewis03,
title = {A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images},
author = {S. M. Butler and G. I. Webb and R. A. Lewis},
editor = {T. D. Gedeon and L. C. C. Fung},
doi = {10.1007/978-3-540-24581-0_58},
year = {2003},
date = {2003-01-01},
booktitle = {Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03)},
pages = {677-685},
publisher = {Springer},
address = {Berlin/Heidelberg},
abstract = {X-ray mammography is the current method for screening for breast cancer, and like any technique, has its limitations. Several groups have reported differences in the X-ray scattering patterns of normal and tumour tissue from the breast. This gives rise to the hope that X-ray scatter analysis techniques may lead to a more accurate and cost effective method of diagnosing beast cancer which lends itself to automation. This is a particularly challenging exercise due to the inherent complexity of the information content in X-ray scatter patterns from complex heterogenous tissue samples. We use a simple naive Bayes classier, coupled with Equal Frequency Discretization (EFD) as our classification system. High-level features are extracted from the low-level pixel data. This paper reports some preliminary results in the ongoing development of this classification method that can distinguish between the diffraction patterns of normal and cancerous tissue, with particular emphasis on the invention of features for classification.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
X-ray mammography is the current method for screening for breast cancer, and like any technique, has its limitations. Several groups have reported differences in the X-ray scattering patterns of normal and tumour tissue from the breast. This gives rise to the hope that X-ray scatter analysis techniques may lead to a more accurate and cost effective method of diagnosing beast cancer which lends itself to automation. This is a particularly challenging exercise due to the inherent complexity of the information content in X-ray scatter patterns from complex heterogenous tissue samples. We use a simple naive Bayes classier, coupled with Equal Frequency Discretization (EFD) as our classification system. High-level features are extracted from the low-level pixel data. This paper reports some preliminary results in the ongoing development of this classification method that can distinguish between the diffraction patterns of normal and cancerous tissue, with particular emphasis on the invention of features for classification.
Agar, J.; Webb, G. I.
Application Of Machine Learning To A Renal Biopsy Data-Base
Nephrology, Dialysis and Transplantation, vol. 7, pp. 472-478, 1992.
@article{AgarWebb92,
title = {Application Of Machine Learning To A Renal Biopsy Data-Base},
author = {J. Agar and G. I. Webb},
url = {http://ndt.oxfordjournals.org/content/7/6/472.abstract},
year = {1992},
date = {1992-01-01},
journal = {Nephrology, Dialysis and Transplantation},
volume = {7},
pages = {472-478},
publisher = {Oxford University Press},
address = {Oxford UK},
abstract = {This pilot study has applied machine learning (artificial intelligence derived qualitative analysis procedures) to yield non-invasive techniques for the assessment and interpretation of clinical and laboratory data in glomerular disease. To evaluate the appropriateness of these techniques, they were applied to subsets of a small database of 284 case histories and the resulting procedures evaluated against the remaining cases. Over such evaluations, the following average diagnostic accuracies were obtained: microscopic polyarteritis, 95.37%; minimal lesion nephrotic syndrome, 96.50%; immunoglobulin A nephropathy, 81.26%; minor changes, 93.66%; lupus nephritis, 96.27%; focal glomerulosclerosis, 92.06%; mesangial proliferative glomerulonephritis, 92.56%; and membranous nephropathy, 92.56%. Although in general the new diagnostic system is not yet as accurate as the histological evaluation of renal biopsy specimens, it shows promise of adding a further dimension to the diagnostic process. When the machine learning techniques are applied to a larger database, greater diagnostic accuracy should be obtained. It may allow accurate non- invasive diagnosis of some cases of glomerular disease without the need for renal biopsy. This may reduce both the cost and the morbidity of the investigation of glomerular disease and may be of particular value in situations where renal biopsy is considered hazardous or contraindicated.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This pilot study has applied machine learning (artificial intelligence derived qualitative analysis procedures) to yield non-invasive techniques for the assessment and interpretation of clinical and laboratory data in glomerular disease. To evaluate the appropriateness of these techniques, they were applied to subsets of a small database of 284 case histories and the resulting procedures evaluated against the remaining cases. Over such evaluations, the following average diagnostic accuracies were obtained: microscopic polyarteritis, 95.37%; minimal lesion nephrotic syndrome, 96.50%; immunoglobulin A nephropathy, 81.26%; minor changes, 93.66%; lupus nephritis, 96.27%; focal glomerulosclerosis, 92.06%; mesangial proliferative glomerulonephritis, 92.56%; and membranous nephropathy, 92.56%. Although in general the new diagnostic system is not yet as accurate as the histological evaluation of renal biopsy specimens, it shows promise of adding a further dimension to the diagnostic process. When the machine learning techniques are applied to a larger database, greater diagnostic accuracy should be obtained. It may allow accurate non- invasive diagnosis of some cases of glomerular disease without the need for renal biopsy. This may reduce both the cost and the morbidity of the investigation of glomerular disease and may be of particular value in situations where renal biopsy is considered hazardous or contraindicated.
Webb, G. I.; Agar, J.
The Application of Machine Learning to the Diagnosis of Glomerular Disease
Sarmeinto, C. (Ed.): Proceedings of the IJCAI Workshop W.15: Representing Knowledge in Medical Decision Support Systems, pp. 8.1-8.8, Sydney, Australia, 1991.
@inproceedings{WebbAgar91,
title = {The Application of Machine Learning to the Diagnosis of Glomerular Disease},
author = {G. I. Webb and J. Agar},
editor = {C. Sarmeinto},
year = {1991},
date = {1991-01-01},
booktitle = {Proceedings of the IJCAI Workshop W.15: Representing Knowledge in Medical Decision Support Systems},
pages = {8.1-8.8},
address = {Sydney, Australia},
abstract = {A pilot study has applied the DLG machine learning algorithm to create expert systems for the assessment and interpretation of clinical and laboratory data in glomerular disease. Despite the limited size of the data-set and major deficiencies in the information recorded therein, for one of the conditions examined in this study, microscopic polyarteritis, a consistent diagnostic accuracy of 100% was obtained. With expansion of the data base, it is possible that techniques will be derived that provide accurate non-invasive diagnosis of some cases of glomerular disease, thus obviating the need for renal biopsy. Success in this project will result in significant reductions in both the cost and the morbidity associated with the investigation of glomerular disease.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
A pilot study has applied the DLG machine learning algorithm to create expert systems for the assessment and interpretation of clinical and laboratory data in glomerular disease. Despite the limited size of the data-set and major deficiencies in the information recorded therein, for one of the conditions examined in this study, microscopic polyarteritis, a consistent diagnostic accuracy of 100% was obtained. With expansion of the data base, it is possible that techniques will be derived that provide accurate non-invasive diagnosis of some cases of glomerular disease, thus obviating the need for renal biopsy. Success in this project will result in significant reductions in both the cost and the morbidity associated with the investigation of glomerular disease.