Validating Data-Driven Clinical Fingerprints as an Input Feature Representation. - Marco Barbero Mota; Jorge L. Gamboa; John M. Still; Charles M. Stein; Vivian K. Kawai; Thomas A. Lasko (AMIA, 2022)

Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures from Routine EHRs for Pulmonary Nodule Classification. - Thomas Z. Li; John M. Still; Kaiwen Xu; Ho Hin Lee; Leon Y. Cai; Aravind R. Krishnan; Riqiang Gao; Mirza S. Khan; Sanja Antic; Michael N. Kammer; Kim L. Sandler; Fabien Maldonado; et al. (MICCAI (2), 2023)

Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence. - Thomas A. Lasko; John M. Still; Thomas Z. Li; Marco Barbero Mota; William W. Stead; Eric V. Strobl; Bennett A. Landman; Fabien Maldonado (CoRR, 2024)

Unsupervised discovery of clinical disease signatures using probabilistic independence. - Thomas A. Lasko; William W. Stead; John M. Still; Thomas Z. Li; Michael N. Kammer; Marco Barbero Mota; Eric V. Strobl; Bennett A. Landman; Fabien Maldonado (J. Biomed. Informatics, 2025)

A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records. - Marco Barbero Mota; John M. Still; Jorge L. Gamboa; Eric V. Strobl; Charles M. Stein; Vivian K. Kawai; Thomas A. Lasko (CoRR, 2025)

Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record. - Joshua W. Betts; John M. Still; Thomas A. Lasko (CoRR, 2025)