Martin G. Seneviratne

发表

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer

Selen Bozkurt, James D. Brooks, Tina Hernandez-Boussard, 2019, MedInfo.

Identifying cases of metastatic prostate cancer using machine learning on electronic health records

Nigam H. Shah, Juan M. Banda, James D. Brooks, 2018, AMIA.

Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning

Martin G. Seneviratne, T. Hernandez-Boussard, E. Cramer, 2019, EGEMS.

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction

Martin G. Seneviratne, A. Mottram, Nan Du, 2020 .

Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer

Tina Hernandez-Boussard, Douglas W. Blayney, Tina Seto, 2018, EGEMS.

Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment

Tina Hernandez-Boussard, Martin Seneviratne, Imon Banerjee, 2019, JAMIA open.

Developing Continuous Risk Models for Adverse Event Prediction in Electronic Health Records using Deep Learning

Suman V. Ravuri, Jack W. Rae, Thomas F. Osborne, 2020 .

Development and Implementation of a Real-time Bundle-adherence Dashboard for Central Line-associated Bloodstream Infections

Martin G. Seneviratne, D. Scheinker, Andrew Ward, 2021, Pediatric quality & safety.

Digital Health in Low- and Middle-Income Countries

Martin G. Seneviratne, D. Peiris, 2019, Revolutionizing Tropical Medicine.

Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage

Martin G. Seneviratne, J. Brooks, T. Hernandez-Boussard, 2022, Frontiers in Digital Health.

Grains of sand to clinical pearls: Realizing the potential of wearable data.

Martin G. Seneviratne, S. Martin, K. Parakh, 2022, The American journal of medicine.

Large language models encode clinical knowledge

Hyung Won Chung, Martin G. Seneviratne, Vivek Natarajan, 2022, Nature.

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Suman V. Ravuri, Jack W. Rae, Ivan V. Protsyuk, 2021, Nature Protocols.

Underspecification Presents Challenges for Credibility in Modern Machine Learning pdf

Alexander D'Amour, Andrea Montanari, Xiaohua Zhai, 2020, J. Mach. Learn. Res..

Bridging the implementation gap of machine learning in healthcare

Nigam H. Shah, Martin G. Seneviratne, Larry F. Chu, 2019, BMJ Innovations.

Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.

Tina Hernandez-Boussard, Juan M Banda, Martin Seneviratne, 2018, Annual review of biomedical data science.

Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer

Martin G. Seneviratne, J. Brooks, T. Hernandez-Boussard, 2018, Cancer.

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction

Andrew M. Dai, Yuan Xue, Nan Du, 2020, NeurIPS.

BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark pdf

Andrew M. Dai, Anand Avati, Balaji Lakshminarayanan, 2021, ArXiv.

Reporting of demographic data and representativeness in machine learning models using electronic health records

Selen Bozkurt, Eli M Cahan, Martin G Seneviratne, 2020, J. Am. Medical Informatics Assoc..

Concept-based model explanations for electronic health records pdf

Shaobo Hou, Martin G Seneviratne, Nenad Tomasev, 2020, CHIL.

Merging heterogeneous clinical data to enable knowledge discovery

Michael G. Kahn, Tina Hernandez-Boussard, Martin G. Seneviratne, 2018, PSB.

HealthNavigator: a mobile application for chronic disease screening and linkage to services at an urban Primary Health Network.

Martin G. Seneviratne, D. Peiris, F. Hersch, 2018, Australian journal of primary health.

The Association Between Central Line-Associated Bloodstream Infection and Central Line Access*

Martin G. Seneviratne, D. Scheinker, Andrew Ward, 2023, Critical care medicine.

Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing

Subhrajit Roy, Martin Seneviratne, Nenad Tomasev, 2021, J. Am. Medical Informatics Assoc..

Assessment of a Clinical Trial–Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

Martin G. Seneviratne, J. Brooks, T. Hernandez-Boussard, 2021, JAMA network open.

Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network

George Hripcsak, Borim Ryu, Sooyoung Yoo, 2020, J. Am. Medical Informatics Assoc..

Automated LOINC Standardization Using Pre-trained Large Language Models

Martin G. Seneviratne, Á. Lelkes, Tao Tu, 2022, ML4H@NeurIPS.

User-centred design for machine learning in health care: a case study from care management

Ron C. Li, Martin G. Seneviratne, Laura M. Vardoulakis, 2022, BMJ Health & Care Informatics.