Diversity-Promoting and Large-Scale Machine Learning for Healthcare
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2018
- Accession Number
- AD1168004
Entities
People
- Pengtao Xie
Organizations
- Carnegie Mellon University