Joel T. Dudley

Research Statement

AI generated · 1d ago

Joel T. Dudley's indexed work centers on applying machine learning and network analysis to clinical and biomedical data. His senior-authored research includes Deep Patient, an unsupervised representation of electronic health records used to predict future patient outcomes, and deep learning for healthcare, a widely cited review of opportunities and challenges in the field. He has also extended these methods to artificial intelligence in cardiology and to topological analysis of patient similarity for type 2 diabetes subgroups, while earlier first-author work addressed computational drug repositioning through drug-disease relationships. Beyond these, Dudley contributed as a middle author to MEGA, the molecular evolutionary genetics analysis software, and to personal omics profiling linking dynamic molecular and medical phenotypes, collaborations that connect him with Eric E. Schadt and Christopher E. Mason. No software, datasets, patents, or grants beyond publications are indexed for him. Taken together, the named works trace a consistent line: from evolutionary and omics tooling toward machine-learning systems that reframe diagnosis, risk prediction, and drug repurposing around patient data.

Most Cited Works

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T Dudley, Alberto Lavelli, Fabio Rinaldi, Venet Osmani

JMIR Medical Informatics2019

558Citations

Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications

Sherry Bhalla, David T Melnekoff, Adolfo Aleman, Violetta Leshchenko, Paula Restrepo, Jonathan Keats, Kenan Onel, Jeffrey R Sawyer, Deepu Madduri,

Research Areas

Genetic Associations and EpidemiologyBioinformatics and Genomic NetworksComputational Drug Discovery MethodsMachine Learning in HealthcareGenomics and Rare Diseases

Loading ORCID activity...

Sources

Research Statement

Most Cited Works

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications

Research Areas

Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept

Building a Secure Biomedical Data Sharing Decentralized App (DApp): Tutorial

Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study