Improving Acute Decision Making

Stroke is a leading cause of mortality and morbidity in the United States, with approximately 795,000 Americans experiencing a new or recurrent stroke each year. In this project, we aim to: 1) develop a machine learning framework for classifying treatment eligibility, 2) develop a deep convolutional autoencoder to generate novel multimodal image representations from MR and CT to improve classification, and 3) implement visualization techniques that elucidate the relationship between deep features and pathophysiological stroke processes.

NIH National Institute of Neurological Disorders and Stroke, R01 NS100806

Thyroid Ca

Reducing Unnecessary Biopsies

The thyroid cancer incidence rate has tripled in the past thirty years, with an estimated cost of $18-21 billon in 2019. US is the imaging modality of choice, which consists of multiple 2D images of different locations and orientations. US readings are often vague and subjective in nature, which has resulted in a steady increase in the number of biopsies performed over the past 20 years. It is estimated that about one-third of all thyroid biopsy procedures performed in the United States are medically unnecessary, leading to the unmet need for noninvasive diagnostic tests that can reliably identify which nodules require a biopsy. The research objective of this R21 is to develop a new graph-based approach to leverage spatial information contained within imaging studies that will be combined with biomarkers and other known risk factors.

NIH National Institute of Biomedical Imaging and Bioengineering, R21 EB030691


Predicting Exacerbations

Heart failure (HF) is a debilitating disease that affects over five million people in the United States and in 2012 had a direct cost of over $30.7 billion annually. Home monitoring of HF patients has the potential to reduce costs and improve quality of life by reducing preventable hospital readmissions. The goals of this R01 are to: 1) demonstrate that patients are adherent to a home monitoring regimen when using minimally-invasive monitoring technologies; 2) combine the minimally-invasive home monitoring regimen with predictive algorithms to forecast hospital readmission; 3) develop models using electronic health record (EHR) data and a baseline survey to predict levels of adherence to the home monitoring regimen; and 4) explore the pragmatic feasibility of using a mobile app for communicating with patients in prospective pilot study

NIH National Heart, Lung, and Blood Institute, R01 HL141773

Prostate Ca

Multi-modal Modeling

Prostate cancer is the second leading cause of cancer death in American men, accounting for 26% of new cancer diagnoses and 9% of cancer deaths in men. The research objective of this R21 is to develop novel techniques using multiparametric magnetic resonance imaging (mp-MRI) and MRI-ultrasound (US) fusion guided biopsy data that provide discriminatory power in distinguishing indolent versus clinically significant prostatic adenocarcinoma based on radiology imaging and digital histology.

NIH National Cancer Institute, R21 CA220352


Expediting Chart Review

Primary care physicians (PCPs) are responsible for reviewing and understanding a wide spectrum of a patient’s medical history in order to make informed decisions regarding care. However, a variety of factors impede this process, including: the increasing complexity and number of diagnostic tests and treatments, health information exchange standards that may add more information to the medical record, and the need to efficiently see more patients in less time. These obstructions can lead to an inhibition of dialogue between patients and providers, and possibly even medical errors. New methods are required to help expedite a healthcare provider’s understanding of a patient’s medical history, summarizing key information. This proposal seeks to develop a topic model and ensuing visualization system for automatically summarizing medical records to support PCPs.

NIH National Library of Medicine, R21 LM011937