To view this live defense via Zoom, visit: https://umass-amherst.zoom.us/j/9276430378
Meeting ID: 927 643 0378
Abstract: Patient-centered care has been established as a fundamental approach to improve the quality of health care. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records.
First, we design a framework to automatically generate health literacy instruments to measure a patient's literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients.
The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric.
Committee Members:
Hong Yu (Chair)
Bruce Croft (Member)
Ben Marlin (Member)
Felfan Liu (Outside Member)