Accession Number:



Diversity-Promoting and Large-Scale Machine Learning for Healthcare

Descriptive Note:

[Technical Report, Doctoral Thesis]

Corporate Author:

Carnegie Mellon University

Personal Author(s):

Report Date:


Pagination or Media Count:



In healthcare, a tsunami of medical data has emerged, including electronic health records, images, literature, etc. These data are heterogeneous and noisy, which renders clinical decision-makings time-consuming, error-prone, and suboptimal. In this thesis, we develop machine learning ML models and systems for distilling high value patterns from unstructured clinical data and making informed and real-time medical predictions and recommendations, to aid physicians in improving the efficiency of workflow and the quality of patient care.

Subject Categories:

  • Numerical Mathematics
  • Administration and Management
  • Cybernetics

Distribution Statement:

[A, Approved For Public Release]