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 highvalue 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. When developing these models, we encounter several challenges: (1) How to better capture infrequent clinical patterns, such as rare subtypes of diseases; (2) How to make the models generalize well on unseen patients? (3) How to promote the interpretability of the decisions? (4) How to improve the timeliness of decision-making without sacrificing its quality? (5) How to efficiently discover massive clinical patterns from large-scale data?

To address challenges (1-4), we systematically study diversity-promoting learning, which encourages the components in ML models (1) to diversely spread out to give infrequent patterns a broader coverage, (2) to be imposed with structured constraints for better generalization performance, (3) to be mutually complementary for more compact representation of information, and (4) to be less redundant for better interpretability. The study is performed in both frequentist statistics and Bayesian statistics. In the former, we develop diversity-promoting regularizers that are empirically effective, theoretically analyzable, and computationally efficient, and propose a rich set of optimization algorithms to solve the regularized problems. In the latter, we propose Bayesian priors that can effectively entail an inductive bias of "diversity" among a finite or infinite number of components and develop efficient posterior inference algorithms. We provide theoretical analysis on why promoting diversity can better capture infrequent betters and improve generalization. The developed regularizers and priors are demonstrated to be effective in a wide range of ML models.

To address challenge (5), we study large-scale learning. Specifically, we design efficient distributed ML systems by exploiting a system-algorithm co-design approach. Inspired by a sufficient factor property of many ML models, we design a peer-to-peer system – Orpheus – that significantly reduces communication and fault tolerance costs. We also provide theoretical analysis showing that algorithms executed on Orpheus are guaranteed to converge. The efficiency of our system is demonstrated in several large-scale applications.

We apply the proposed diversity-promoting learning (DPL) techniques and the distributed ML system to solve healthcare problems. In a similar-patient retrieval application, DPL shows great effectiveness in improving retrieval performance on infrequent diseases, enabling fast and accurate retrieval, and reducing overfitting. In a medical-topic discovery task, our Orpheus system is able to extract tens of thousands of topics from millions of documents in a few hours. Besides these two applications, we also design effective ML models for hierarchical multi-label tagging of medical images and automated ICD coding.

258 pages

Thesis Committee:
Eric P. Xing (Chair)
Ruslan Salakhutdinov
Pradeep Ravikumar
Ryan Adam (Princeton University/Google Brain)
David Sontag (Massachusetts Institute of Technology)

Roni Rosenfeld, Head, Machine Learning Department
Andrew W. Moore, Dean, School of Computer Science