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.

Project Number:  1 K01 HL155404-01A1

Name of PD/PI:  Ansari, Sardar

Source of Support: NIH/NHLBI

Project Dates: 9/1/2021 – 8/31/2026

Total Award Amount: $817,069

This project aims to build tools that allow for finding patients who suffer from hypertrophic cardiomyopathy. Currently, many patients with this disease are not identified enough for treatment. The study team will use several sources of data that can be used to identify the disease earlier in a federated learning framework in collaboration with AHA and multiple other institutions. This approach protects the patient privacy better than other approaches that are used today. In addition, the study team will explore the implications of using this system in clinics.

Project Number:  24HCMFLP1291285

Name of PD/PI:  Ansari, Sardar

Source of Support: American Heart Association

Project Dates: 2/15/2024 – 2/14/2026

Total Award Amount: $498,184

Aim 1: Refine the portable GC device and add VIC detection ability; Aim 2: Using gas chromatography identified breath biomarkers to develop and validate an algorithm based on machine learning and artificial intelligence to detect ARDS and to monitor its trajectory.

Status of Support: Awarded - Pending

Name of PD/PI:  Fan, Xudong; Sjoding, Michael; Ward, Kevin

Source of Support:  NIH

Project Dates:  01/01/2024-12/31/2028

Total Award Amount: $4,902,534 (pending)

The goal of this project is to reduce patients’ risk of experiencing adverse events during their hospital stay by implementing an early warning system called PICTURE (Predicting Inpatient acute Care Transfers and other UnfoReseen Events). We will design and evaluate an efficient, effective, and user-friendly system interface to deliver PICTURE risk scores and their explanations to providers.

Name of PD/PI: Ansari, Sardar

Source of Support: The Doctor’s Company Foundation 

Project Dates:  11/1/2021-10/31/2024

Total Award Amount:  $100,000

Name of PD/PI:  Mazumder, Nikhilesh Ray

Source of Support:  American College of Gastroenterology

Project Dates:  07/01/2022-06/30/2025

Total Award Amount: $450,000

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).

Project Number:  1-U01-TR-004066-01

Name of PD/PI:  Fan, Xudong; Gunnerson, Kyle; Huang, Yvonne; Mahajan, Prashant; Gudjonsson, Johann

Source of Support:  NIH

Project Dates:  08/10/2022-05/31/2027

Total Award Amount: $5,505,413

This study aims to develop and test new computational techniques for acute respiratory distress syndrome (ARDS) detection, incorporating two key factors: (1) training models to account for and note uncertainty and (2) leveraging anatomical information.

Name of PD/PI: Negar Farzaneh

Source of Support: Michigan Institute for Clinical & Health Research (MICHR) K12

Project Dates: 05/01/2022 - 09/30/2025

Total Award Amount: $302,962

The main objective of this project is to build a tool for real-time monitoring of clinical decision support systems after deployment into the clinical practice to identify when the performance of the system is declining.

Name of PD/PI:  Ansari, Sardar

Source of Support:  The Michigan Translational Research and Commercialization for Life Sciences

Project Dates:  04/01/2022-06/30/2023

Total Award Amount: $121,563

The goal of this project is to develop a suit of predictive analytics for unexpected patient deterioration on the hospital general wards, as well the tools that enable their deployment.

Name of PD/PI:  Ansari, Sardar

Source of Support:  Airstrip Technologies, Inc.

Project Dates:  04/01/2022-06/30/2023

Total Award Amount: $1,386,773

The objective of this project is to develop a multi-label classifier that captures (1) the dependency between different output labels as well as (2) the uncertainty about the ground truth labels in the context of electrocardiogram (ECG) classification.

Name of PD/PI: Negar Farzaneh, Hamid Ghanbari

Source of Support: Michigan Institute for Data Science (MIDAS) 

Project Dates: 07/30/2022 - 07/29/2023

Total Award Amount: $29,900

The aim of this project is to develop computer vision technologies powered by deep convolutional neural networks to automatically identify chest x-ray findings consistent with ARDS with expert level accuracy. This technology will be a fundamental leap forward for ARDS care and will address a critical limitation in the current diagnosis of ARDS.

Name of PD/PI:  Sjoding, Michael

Project Dates:  08/01/2020-01/31/2023

Total Award Amount: $545,326

Name of PD/PI: Admon, Andrew; Gillies, Chris

Source of Support: Michigan Institute for Data Science, University of Michigan

Project Dates: 06/13/2020 – 12/31/2020

Total Award Amount:  $30,000