Current Projects

PICTURE: Predicting Intensive Care Transfers and other UnfoReseen Events

Early detection of patient deterioration has been found to lead to reduced mortality risk, reduced length-of-stay and decreased hospital costs, yet identifying patient deterioration is a challenge for clinicians. PICTURE’s General Floor Analytic for adult and pediatric populations is a combination of machine learning algorithms utilizing electronic health record (EHR) data to passively and accurately predict ICU transfer or death as a proxy for patient deterioration. 

For further information, visit here for the adult model and here for the pediatric model.

Model Performance Diagnostic Suite (MPD)

The Model Performance Diagnostics  detect data shift and model degradation by comparing the distribution of the real-time data that is fed to the model to the data on which the model was trained.  MPD also estimates the performance of the model post-deployment, a unique feature that is currently not offered by any other tool, all via an interactive visual interface.

For further information, visit here.

Environment for Model Maintenance, Integration and Tuning (EMMIT)

Integrating machine learning models and predictive analytics with a hospital environment is difficult.  Data flowing in and out of the model can become a black box with no way to monitor if the models are operating or data transfer is effective.  Our Data Operations Team has designed EMMIT, a platform for hosting any number of unique models and helping to deploy them within an EHR system.  EMMIT also provides operational monitoring (uptime, alerts, error reporting)  to teams who would otherwise have no insight on the status of their software. 

For further information, visit here.

Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables

The aim of this project is to apply machine learning and signal processing techniques to multiple types of data (electronic health records, electrocardiography, and heart rate variability) to predict heart failure onset, and to use wearable devices to improve the predictions and expand their impact to a wider population. The resulting tools will help prevent delayed diagnosis of heart failure, leading to improved medical outcomes, enhanced patient experience, and reduction in healthcare costs.

DETECT-ARDS - Analytic for Detecting Acute Respiratory Distress Syndrome

DETECT-ARDS is a new approach for identifying ARDS findings on chest x-rays.  With ARDS often missed or under-diagnosed, DETECT-ARDS has the potential to transform patient outcomes for the better.  In collaboration with Dr. Michael Sjoding, our team has trained a powerful deep convolutional neural network model that can identify findings consistent with ARDS with high accuracy.

For further information, visit here.

Breath Analysis Device for Disease Detection

This automated, portable breath analysis device utilizes gas chromatography and a corresponding algorithm to detect and monitor breath biomarkers indicative of Acute Respiratory Distress Syndrome (ARDS), COVID-19, and other lung injury.  Identifying the onset of these illnesses early and monitoring their trajectory over time can help stratify patients and better allocate resources within the hospital. The Ansari Lab, in collaboration with Dr. Xudong Fan's team from Biomedical Engineering, has been designing the automated algorithms to analyze the signals that are generated by the device.

For further information, visit here.

Wearable body odor sensing for disease detection and monitoring

Many diseases, both internal and cutaneous, have distinct odors associated with them, and their identification can provide unique diagnostic clues, guide laboratory evaluation, and facilitate and expedite treatment. Current body odor analysis relies on benchtop instruments, but they are too bulky for use at point-of-care, home or workplace. E-nose technologies provide a simple, light, and low cost alternative for body odor analysis, but they are highly susceptible to environmental changes (e.g., temperature and humidity). In collaboration with Dr. Xudong Fan's team, the Ansari Lab has been developing analytical tools to process the data that is generated by the sensors and use them to predict 20 different medical conditions.

Vascular Tone Monitoring System (VATMOS)

By continuously assessing peripheral vascular tone, this piezoelectric-optical wearable ring provides real time insights into how the cardiovascular system is responding to illness or injury before changes in traditional vital signs like blood pressure occur. This sensor provides almost immediate feedback to clinicians on the physiologic response to treatment. This work has been a collaboration with Dr. Kenn Oldham in Mechanical Engineering.

For further information, visit here.