Healthy Play for Teens by Teens
The Biomedical Informatics Center is collaborating with T.C. Williams High School students to create a suite of video games that promote healthy behavior among teens. By promoting learning tools which are created by teens for their peers, this project aims to provide information that is relatable, credible, and engaging to the learners. The goal of these games is to not only increase healthy behaviors among teens, but to reduce the disparity in health behaviors among teens from varying backgrounds (e.g. socioeconomic, racial, cultural, religious).
Informatics - CTSI at CNMC
Research Initiative whose final purpose is to develop a dynamic risk assessment of pediatric risk of mortality that is able to quantify physiological status and can be used for earlier interventions to significantly increase survival rate of patients and to improve the efficiency of the resources. The Biomedical Informatics Center has access to the Health Facts database and uses statistical and machine learning methods for the development of the desired dynamic risk assessment.
Use Frailty Status to Predict Postoperative Outcomes in Elderly Patients
Frailty is an age-related state of increased vulnerability to stressors. It is an important predictor for many health outcomes, but is rarely collected in a quantitative and systematic fashion in routine health care. This project aims to extract frailty status from free-text clinical notes using natural language processing tools. The extracted frailty status will be mapped into an ontology and then applied to the prediction of major cardiovascular procedure outcomes on elder patients.
Protect Patient Safety Through Herb-Drug Disease Interaction Detection and Alert
Doctors and pharmacists routinely check for potentially harmful drug interactions when prescribing medications for patients. However, herbal supplements have been found to have interactions with some drugs. The Biomedical Informatics Center is collaborating with a cardiologist and a team of pharmacists with specialty in herbal supplements to develop a tool for detection of possible herb-drug interactions. An online survey is being developed to allow patients to enter their medications and herbal supplements in order to alert them of any potentially harmful interactions.
Data Mining on Veterans with Severe Mental Illnesses
The goal of this project is to identify patients at high risk of developing adverse outcomes (i.e., death, hospitalization, readmission, etc.) among the US veterans with severe mental illnesses (focusing on bipolar disorders). Different from the traditional studies that usually use patient baseline characteristics as predictors, this study uses patient temporal phenotypic features to predict outcomes. Multiple data mining techniques such as topic modeling and data visualization are applied to process temporal data from the US national VA databases including free-text medical notes. The pilot study has evidenced data mining techniques with temporal phenotypic features would dramatically improve the predictive performance.
NLP Support for Digital Pathology
Surgical pathology reports can vary greatly in appearance and format, and tend to lack a uniform style across institutions or clinicians. Traditionally, this has created a need for report information to be manually extracted in order to accurately capture machine-readable information. However, the accuracy rates of this approach is still far from perfect, leading to statistical skewing of any inferences generated from the use of such data. The goal of this project is to implement an automated lexical analysis of surgical pathology reports which attains a high degree of accuracy.