The increasingly large amounts of Electronic Medical Record (EMR) data offer unprecedented opportunities for EMR data mining to enhance health care experiences for personalized intervention, improve different diseases risk stratifications, and facilitate understanding about disease and appropriate treatment. To solve the key and challenging problems in mining such large-scale heterogeneous EMRs, the investigators aim to develop: (i) new computational tools to automate the EMRs processing, including new techniques for filling in missing values using a new robust rank-k matrix completion method; (ii) annotation of unstructured free-text EMRs using multi-label multi-instance learning; (iii) a new sparse multi-view learning model to integrate heterogeneous EMRs to predict the readmission risk of Heart Failure (HF) patients and to support personalized intervention; (iv) novel methods for identifying the longitudinal patterns using high-order multi-task learning; (v) a nonparametric Bayesian model for predicting the event time outcomes of the HF patients readmission.
The sparse multi-view feature learning and robust multi-task longitudinal pattern finding algorithms have a broad range of applications beyond EMR data mining. Free dissemination of source implementations of the algorithms enable other researchers to further develop and apply the resulting techniques. In particular, the methods and tools are expected to impact other EMR and public health research. This project offers enhanced opportunities for research-based advanced training of students (including members of minorities and under-served populations) and integration of research results into curricula at the University of Texas at Arlington, the University of Texas Southwestern Medical Center at Dallas, and Southern Methodist University. For further information see the web site at: http://ranger.uta.edu/~heng/NSF-III-1302675.html
