Developing a Learning Electronic Medical Record (LEMR) system

Electronic medical records (EMRs) are capturing increasing amounts of patient data that can be leveraged by machine-learning methods for computerized decision support. This project focuses on the development of intelligent EMRs that contain adaptive and learning components to provide decision support using the right data, at the right time.

This work is funded by a R01 grant from the NLM, NIH.

Outlier-based monitoring and alerting

Statistical anomalies in patient management actions may correspond to medical errors. In this project machine-learning methods are used to identify patient-management actions that are unusual with respect to actions used to manage comparable patients in the past. And an alert is raised if such an action is deemed a statistical outlier. This method complements existing knowledge-based detection and alerting methods that are clinically precise, but costly to build. PIs of the project are Milos Hauskrecht, Gilles Clermont and Gregory F. Cooper.

This work is funded by a R01 grant from the NIGMS, NIH.

Predicting catheter salvage in central line-associated bloodstream infections

Central-line associated bloodstream infections (CLABSI) are the most common pediatric healthcare-associated infection, and are associated with significant morbidity, mortality and cost. CLABSI treatment requires a choice between removal of the central venous catheter and attempted central venous catheter salvage through antimicrobial treatment including long-dwell, high-concentration antibiotic lock therapy. This presents a clinical conundrum when continued central venous catheter use is medically important. Unsuccessful salvage exposes patients to the risk of infection recurrence or subsequent line failure and removal, whereas preemptive central venous catheter removal incurs the risk of invasive procedures and loss of potential future central venous catheter sites. The ability to produce individual predictions of clinically-relevant outcomes in attempted central venous catheter salvage can help the clinician in taking the appropriate action.

Twitter surveillance of vaping

Thus, there is a strong need to measure and understand the risks, sentiments, and behavior related to vaping especially adolescents among adolescents. In this project, we are building a Twitter surveillance system that will use machine learning to analyze vaping-related tweets for inferring behaviors and changes in attitudes in response to policy changes.

This work is funded by a R01 grant from the NCI, NIH.

Predicting new uses and adverse effects of drugs

New machine learning representations of drugs provides the foundation for a variety of applications in clinical pharmacology. In this project, we are developing computational representations of drugs for predicting new uses (drug repositioning) and as-yet-unknown adverse effects of drugs.

Realtime identification of brain ischemia during surgery

Brain ischemia and stroke that occur during surgery is a devastating complication and with increasing number of surgical procedures this complication is likely to increase. Currently, continuous intraoperative monitoring of brain activity for ischemia and stroke is done using electroencephalogram (EEG) for high risk surgeries. The EEG signals are monitored visually by a trained physician which is tiring, error prone and expensive. In this project, we are developing a software system that will use machine learning to analyze the EEG signals in realtime, and alert the monitoring physician if ischemia or stroke is detected. Such an automated monitoring and alerting system will make high risk surgeries safer and furthermore will enable the automated brain monitoring of all surgeries in the future.

This work is funded by a grant from UPMC Enterprises.