Machine Learning

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Citation
Henry K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Most A, Sikora A; MRC‐ICU Investigator Team. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. Pharmacotherapy. 2025 Feb;45(2):76-86. doi: 10.1002/phar.4642. Epub 2025 Jan 3. PMID: 39749877; PMCID: PMC11834896.
Principal Investigator
Sikora, Andrea
Project Name
Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction

The impact of an adaptive mHealth intervention on improving patient-provider health care communication: Secondary analysis of the DIAMANTE trial.

Citation
Leng L, Arévalo Avalos MR, Aguilera A, Lyles CR. The impact of an adaptive mHealth intervention on improving patient-provider health care communication: Secondary analysis of the DIAMANTE trial. JMIR Mhealth Uhealth. 2025 Jul 17;13:e64296. doi: 10.2196/64296. PMID: 40674692; PMCID: PMC12289220.
Principal Investigator
Aguilera, Adrian
Project Name
Improving Diabetes and Depression Self-Management Via Adaptive Mobile Messaging

The PICU data collaborative: A novel, multi-institutional, pediatric critical care dataset.

Citation
Farris RWD, Shah SS, Bennett TD, Brown SR, Cornell TT, Dziorny AC, Flynn A, Grunwell J, Heneghan JA, Kennedy CE, Kretsu A, Mendonca E, Nishisaki A, Rogerson C, Tawfik DS, Wetzel RC, Sanchez-Pinto LN; PICU Data Collaborative. The PICU data collaborative: A novel, multi-institutional, pediatric critical care dataset. Pediatr Crit Care Med. 2025 Jul 1;26(7):e941-e951. doi: 10.1097/PCC.0000000000003758. Epub 2025 Apr 30. PMID: 40304542; PMCID: PMC12221772.
Principal Investigator
Dziorny, Adam C.
Shah, Sareen S.
Project Name
ML-ROVER: Machine Learning to Reduce Laboratory Test Overutilization

Harnessing health information technology to promote equitable care for patients with limited English proficiency and complex care needs.

Citation
Strechen I, Wilson P, Eltalhi T, Piche K, Tschida-Reuter D, Howard D, Sutor B, Tiong I, Herasevich S, Pickering B, Barwise A. Harnessing health information technology to promote equitable care for patients with limited English proficiency and complex care needs. Trials. 2024 Jul 4;25(1):450. doi: 10.1186/s13063-024-08254-y. PMID: 38961501; PMCID: PMC11223355.
Principal Investigator
Barwise, Amelia K.
Project Name
Harnessing Health Information Technology to Promote Equitable Care for Patients with Limited English Proficiency and Complex Care Needs

External validation and update of the pediatric asthma risk score as a passive digital marker for childhood asthma using integrated electronic health records.

Citation
Owora AH, Jiang B, Shah Y, Gaston B, Boustani M. External validation and update of the pediatric asthma risk score as a passive digital marker for childhood asthma using integrated electronic health records. EClinicalMedicine. 2025 May 20;84:103254. doi: 10.1016/j.eclinm.2025.103254. PMID: 40496879; PMCID: PMC12148615.
Principal Investigator
Owora, Arthur Hamie
Project Name
Developing a Passive Digital Marker for the Prediction of Childhood Asthma Treatment Response

Preliminary analysis of the impact of lab results on large language model generated differential diagnoses.

Citation
Bhasuran B, Jin Q, Xie Y, Yang C, Hanna K, Costa J, Shavor C, Han W, Lu Z, He Z. Preliminary analysis of the impact of lab results on large language model generated differential diagnoses. NPJ Digit Med. 2025 Mar 18;8(1):166. doi: 10.1038/s41746-025-01556-8. PMID: 40102561; PMCID: PMC11920259.
Principal Investigator
He, Zhe
Lustria, Mia Liza A.
Project Name
LabGenie: A Patient-Engagement Tool to Aid Older Adults' Understanding of Lab Test Results

Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology

Description

This research develops and evaluates an artificial intelligence-enhanced pretreatment peer-review process in radiation oncology, aiming to improve patient safety by reducing variability among providers in treatment planning, minimizing clinical errors, and enhancing overall treatment outcomes.

Grant Number
R18 HS029474
Principal Investigator(s)
Mazur, Lukasz
Marks, Lawrence B

Complexity, Incidence, and Costs Related to Delayed Diagnosis of Venous Thromboembolism in Urban and Rural Primary and Urgent Care Settings

Description

This research aims to improve the early detection of venous thromboembolism in primary and urgent care by using mixed methods (stakeholder interviews and surveys, electronic health records, and machine learning) to better understand missed and delayed diagnoses, identify risk factors, and develop tools to enhance patient safety.

Grant Number
R01 HS030221
Principal Investigator(s)
Dykes, Patricia
Chen, Jin