Nathanael R. Fillmore
Topics / Machine Learning
Topic

Machine Learning

Fillmore has focused on the development and validation of machine learning and deep learning architectures to improve clinical risk stratification and data extraction. His work includes the creation of dynamic prediction tools for cardiovascular disease, thromboembolism, and pancreatic cancer, as well as the use of natural language processing to automate the retrieval of biomarker data from electronic health records. These efforts aim to transition static clinical assessments toward personalized, real-time predictive models that facilitate early intervention and precision oncology.

U.S. Veterans and VA Data 7 Predictive Modeling 5 External Validation 4 Real World Evidence 4 Electronic Health Records 4 Risk Stratification 3 Model Validation 3 Natural Language Processing 3 Cardiovascular Disease 2 Cox Proportional Hazards Model 2 Patient Outcomes 2 Observational Study 2

Publications (11)

2026

Development and External Validation of a Machine Learning-Based Risk Classification Score for Prostate Cancer-Specific Cardiovascular Risk.

Nickolas Stabellini, Tarek Nahle, Viraj Shah, Sai S. Kollapaneni, Omar M. Makram, Jennifer Cullen, Neal L. Weintraub, Harikrishnan H. Kunhiraman, Umang Swami, Morteza Mahmoudi, Nathanael Fillmore, Isla P. Garraway, Darryl Leong, Jehonathan Pinthus, John Shanahan, Alberto J. Montero, Neeraj Agarwal, Avirup Guha

JCO Oncology PracticeOP2600131
Prostate Cancer Cardiovascular Disease Cardio-Oncology Machine Learning Predictive Modeling
2026

A deep learning model to dynamically predict cancer-associated thromboembolism in large-scale healthcare systems.

Tianshe He†, Chunlei Zheng†, Jennifer La, Dang Pham, Omid Jafari, Jamie Strampe, Karlynn Dulberger, Jaime Ramos-Cejudo, J. Michael Gaziano, Nhan V. Do, Vipul Chitalia, Katya Ravid, Ang Li‡, Nathanael R. Fillmore‡† Equal first authorship · ‡ Equal senior authorship

npj Digital Medicine
U.S. Veterans and VA Data Electronic Health Records Cancer-Associated Thrombosis Predictive Modeling External Validation
2025

Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool.

Kaitlin Swinnerton†, Nathanael R. Fillmore†, Austin Vo, Jennifer La, Danne Elbers, Mary Brophy, Nhan V. Do, Paul A. Monach‡, Westyn Branch-Elliman‡† Equal first authorship · ‡ Equal senior authorship

eClinicalMedicine81, 103114
U.S. Veterans and VA Data Retrospective Cohort Study COVID-19 Severe COVID-19 Predictive Modeling
2023

Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

Eric Lin†, Robert Zwolinski†, Julie Tsu-Yu Wu, Jennifer La, Sergey Goryachev, Linden Huhmann, Cenk Yildrim, David P. Tuck, Danne C. Elbers, Mary T. Brophy, Nhan V. Do‡, Nathanael R. Fillmore‡† Equal first authorship · ‡ Equal senior authorship

Health Informatics Journal29(3), 14604582231198021
U.S. Veterans and VA Data Electronic Health Records Natural Language Processing Machine Learning PD-L1 Expression
2023

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.

Davide Placido†, Bo Yuan†, Jessica X. Hjaltelin†, Chunlei Zheng†, Amalie D. Haue, Piotr J. Chmura, Chen Yuan, Jihye Kim, Renato Umeton, Gregory Antell, Alexander Chowdhury, Alexandra Franz, Lauren Brais, Elizabeth Andrews, Debora S. Marks, Aviv Regev, Siamack Ayandeh, Mary T. Brophy, Nhan V. Do, Peter Kraft, Brian M. Wolpin, Michael H. Rosenthal, Nathanael R. Fillmore, Søren Brunak‡, Chris Sander‡† Equal first authorship · ‡ Equal senior authorship

Nature Medicine29(5), 1113-1122
U.S. Veterans and VA Data Electronic Health Records Pancreatic Cancer Predictive Modeling Deep Learning
2021

The Neutrophil to Lymphocyte Ratio Is Associated With the Risk of Subsequent Dementia in the Framingham Heart Study.

Jaime Ramos-Cejudo, Andrew D. Johnson, Alexa Beiser, Sudha Seshadri, Joel Salinas, Jeffrey S. Berger, Nathanael R. Fillmore, Nhan Do, Chunlei Zheng, Zanetta Kovbasyuk, Babak A. Ardekani, Omonigho M. Bubu, Ankit Parekh, Antonio Convit, Rebecca A. Betensky, Thomas M. Wisniewski, Ricardo S. Osorio

Frontiers in Aging Neuroscience13, 773984
Alzheimer Disease and Dementia Framingham Heart Study Neutrophil-to-Lymphocyte Ratio Cox Proportional Hazards Model Inverse Probability Weighting
2019

Machine Learning Methods to Predict Lung Cancer Survival Using the Veterans Affairs Research Precision Oncology Data Commons.

Nhan V. Do, Jaime C. Ramos, Nathanael R. Fillmore, Robert L. Grossman, Michael Fitzsimons, Danne C. Elbers, Frank Meng, Brett R. Johnson, Samuel Ajjarapu, Corri L. DeDomenico, Karen E. Pierce-Murray, Robert B. Hall, Andrew F. Do, Kelly Gaynor, Peter L. Elkin, Mary T. Brophy

Studies in Health Technology and Informatics264, 1453
U.S. Veterans and VA Data Lung Cancer Machine Learning Predictive Modeling Overall Survival