Biomedical & Clinical Informatics Lab

Department of Computational Medicine & Bioinformatics

Research Projects

In this project, a computer-aided system for colonoscopy video analysis is under development, which will facilitate disease severity measurement and aid in treatment selection and clinical outcome prediction.

The project will develop an innovative, real-time clinical decision support (DSS) platform. The platform will utilize Big Data analytic methods, novel algorithms, and software tools to integrate and analyze disparate sources of continuous and non-continuous patient data.

The DSS platform will be able to understand and predict potential complications and recovery trends for patients following cardiac surgery, other open vascular operations in the chest and abdomen, and other trauma surgeries involving the cardiovascular system.

In this proposal, the PIs develop novel theoretical foundations and scalable algorithms for tensor decomposition and apply them to some major Big Data tasks such as noise removal, data imputation, information integration and dimension reduction. To evaluate the performance of the proposed tensor methods, they will be applied to early detection of sepsis, which represents a spectrum of complex problems in medicine and science.

This project has two aims. First, the construction of AngioAid, a fully automated computer-based platform to assist with the interpretation of coronary angiogram videos. Secondly, the creation of a curated dataset of coronary angiogram videos, made publicly available via Amazon Web Services, that will spur development of new algorithms to advance the field of angiogram interpretation.

The proposed system will accurately predict or detect the onset of severe cardiac events (e.g., hemodynamic instability) prior to complications experienced by the driver in order to ensure driver safety.

We have developed wound segmentation and deep neural network models that can accurately predict infection and wound healing. The image segmentation methods provide an accurate “hands-free” measurement of wound surface area. Our objective here is to refine our algorithms and test the suitability of image analysis as a biomarker for healing or infection. We will do this by performing image analyses of DFU treated by total contact cast (TCC) and by comparing the image features to those not treated by TCC. Our working hypothesis is there will be unique features from image analysis that can accurately predict infection or healing beyond surface area changes over time, known clinical predictors (Brownrigg, Hinchliffe et al. 2016) or candidate biomarkers.

In this project, we aim to develop an automated decision-making system that can identify patients eligible for an HT/MCS that would facilitate primary care physicians or general cardiologists referring those patients for consideration of advanced therapies.

The Center will focus on three main areas of unmet/underserved research needs within the (bio)pharmaceutical sector, with the goal of significantly accelerating the pace of drug discovery while reducing research costs

Among other image modalities used for TBI assessment, a number of recent studies reported that hand-held optic nerve ultrasound (ONUS) may help identify elevated intracranial pressure (ICP). In the largest study of ICP prediction using ONUS to date, we found that optic nerve sheath diameter can accurately predict ICP > 25 mmHg in a mixed population of patients in a neurosurgical ICU. Our preliminary work has demonstrated the feasibility of fully automated algorithms to measure optic nerve sheath diameter. In this project we will further develop these algorithms to predict ICP from measuring the optic nerve sheath diameter.

In this project, we will identify quantitative imaging, biochemical and clinical markers and design predictive models of TMJ OA progression. We develop image processing and machine learning methods and integrate all clinical, imaging and biological to classify patients and to make a prediction on the progress of the disease.

Trauma is the leading cause of death among Americans under 44. The mortality rate for patients with pelvic injuries is increased by the risk of further complications, especially severe hemorrhage. However, even a single trauma patient generates large volumes of information, including vital signs, injury severity scores, demographic details, lab reports, and in particular, complex medical images such as CT scans and X-rays—all of which impact diagnosis and treatment, as well as costs. A computer-assisted decision support system (DSS) capable of rapidly analyzing large volumes of patient information to generate accurate treatment recommendations and outcome predictions has the potential to improve both patient care/survival and resource utilization. The proposed Decision Support System (DSS) technology is envisioned to significantly improve pelvic/abdominal trauma decision-making using facilitated and prompt analysis of complex and heterogeneous patient medical data.

The current project will extend the algorithms of our DDS technology to:

  1. Integrate our algorithms and methods for analysis of data for pelvic/abdominal injuries with those of TBI such that the resulting DDS software can be used in polytrauma cases.
  2. Further develop the planned software implementation of our technology as a user-friendly software tool to be used in the clinical setting.

This project aims to create novel computational methodologies that synthesize and integrate longitudinal electronic health data streams for real-time and continuous health status monitoring and early detection of disease. We will utilize these technologies to address the problem of monitoring patients with lung disease to detect the presence of the acute respiratory distress syndrome (ARDS) at early stages. The project combines the capabilities of two emerging fields of machine learning, privileged learning and learning from uncertain data, both highly relevant to healthcare applications. While the current project focuses on improving diagnosis of ARDS, the proposed learning methods will generalize across healthcare settings, allowing for better characterization of patient health status in both in-hospital and in-home settings via portable electronic monitoring devices.

Hypertension is the number one cause of racial disparities in mortality in the U.S. Understanding the mechanisms by which race is linked with the cardiovascular system is key for potentially reducing race disparities in hypertension and hypertension related mortality. The present study is guided by existing theories of racial health disparities, which suggest that racial health disparities are due to variations in long-term exposure to stress that are moderated by social relations and age.

This project has three aims:

  1. Test links between long-term stress exposure and short-term stress reactivity among Eurpoean American (EA) and AA adults.
  2. Examine age differences in long-term stress exposure and short-term reactivity by race.
  3. Determine how long-term social relationships moderate individual differences in stress exposure and reactivity.