14.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Columbia UniversityUS16 papers
- Harvard UniversityUS13 papers
- Stanford UniversityUS12 papers
- Cornell UniversityUS11 papers
- National Institutes of HealthUS7 papers
- New York UniversityUS7 papers
- University of California, San FranciscoUS7 papers
- Emory UniversityUS6 papers
- Georgia Institute of TechnologyUS6 papers
- Hebrew University of JerusalemIL6 papers
- Imperial College LondonGB6 papers
- Mayo ClinicUS6 papers
8 papers · 1 filter
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Arian Salahi-Niri +39
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimension…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Active learning for medical code assignment
Martha Dais Ferreira, Michal Malyska, Nicola Sahar +3
Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-m…
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Tingyi Wanyan, Jing Zhang, Ying Ding +3
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, o…
Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework
Ana Stanescu, Gaurav Pandey
Heterogeneous ensembles that can aggregate an unrestricted number and variety of base predictors can effectively address challenging prediction problems. In particular, accurate en…
Federated Learning for Healthcare Informatics
Jie Xu, Benjamin S. Glicksberg, Chang Su +3
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insura…
Deep density ratio estimation for change point detection
Haidar Khan, Lara Marcuse, Bülent Yener
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use…
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne, Gowtham Atluri, James Faghmous +6
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guide…