D
David C. Page
Researcher at Massachusetts Institute of Technology
Publications - 523
Citations - 47344
David C. Page is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Y chromosome & X chromosome. The author has an hindex of 110, co-authored 509 publications receiving 44119 citations. Previous affiliations of David C. Page include Hennepin County Medical Center & University of California, Los Angeles.
Papers
More filters
Proceedings ArticleDOI
An integrated approach to feature invention and model construction for drug activity prediction
TL;DR: A new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure, which uses the "Score As You Use" (SAYU) method to select substructures for their ability to improve the regression model.
Proceedings Article
Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.
TL;DR: This work introduces novel machine learning algorithms to improve diagnostic accuracy of breast cancer in aging populations, and develops a novel algorithm, Logical Differential Prediction Bayes Net, that calculates the risk of breast disease based on mammography findings.
Proceedings Article
Integrating machine learning and physician knowledge to improve the accuracy of breast biopsy.
Inês Dutra,Houssam Nassif,David C. Page,Jude W. Shavlik,Roberta M. Strigel,Yirong Wu,M. E. Elezaby,Elizabeth S. Burnside +7 more
TL;DR: This work shows how advice in the form of logical rules, derived by a sub-specialty, i.e. fellowship trained breast radiologists, can guide the search in an inductive logic programming system, and improve the performance of a learned classifier.
Using Machine Learning Algorithms to Predict Risk for Development of Calciphylaxis in Patients with Chronic Kidney Disease.
TL;DR: This work focuses on the use of machine learning to both predict disease risk and model the contributing factors learned from an electronic health record data set, and finds that modeling calciphylaxis risk with random forests learned from binary feature data produces strong models.
Journal ArticleDOI
Implementation of a mechanical chest compression device as standard equipment in a large metropolitan ambulance service.
Paul A. Satterlee,Lori L. Boland,Pamela Jo Johnson,Steve G. Hagstrom,David C. Page,Charles Lick +5 more
TL;DR: In an applied setting, the LUCAS™1 device fits most patients and was well received by prehospital providers, and this has important implications for evaluating the association between device use and ROSC in observational settings.