Machine Learning

Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department.

Citation
Mazurenko O, Hirsh AT, Harle CA, Shen J, McNamee C, Vest JR. Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department. PLoS One. 2024 Nov 20;19(11):e0312193. doi: 10.1371/journal.pone.0312193. PMID: 39565746; PMCID: PMC11578524.
Principal Investigator
Vest, Joshua Ryan
Project Name
Predictive Modeling for Social Needs in Emergency Department Settings

Ethical concerns for remote computer perception in cardiology: New stages for digital health technologies, artificial intelligence, and machine learning.

Citation
Kostick-Quenet K, Estep J, Blumenthal-Barby JS. Ethical concerns for remote computer perception in cardiology: New stages for digital health technologies, artificial intelligence, and machine learning. Circ Cardiovasc Qual Outcomes. 2024 May;17(5):e010717. doi: 10.1161/CIRCOUTCOMES.123.010717. Epub 2024 May 21. PMID: 38771912; PMCID: PMC11115370.
Principal Investigator
Blumenthal-Barby, Jennifer
Project Name
A Multi-Site Trial to Test Benefits of Adding a Personalized Risk Calculator to an Online Decision Aid for Left Ventricular Assist Device

Research on the clinical translation of health care machine learning: Ethicists experiences on lessons learned.

Citation
Blumenthal-Barby J, Lang B, Dorfman N, Kaplan H, Hooper WB, Kostick-Quenet K. Research on the clinical translation of health care machine learning: Ethicists experiences on lessons learned. Am J Bioeth. 2022 May;22(5):1-3. doi: 10.1080/15265161.2022.2059199. PMID: 35475968; PMCID: PMC10281027.
Principal Investigator
Blumenthal-Barby, Jennifer
Project Name
A Multi-Site Trial to Test Benefits of Adding a Personalized Risk Calculator to an Online Decision Aid for Left Ventricular Assist Device

Mitigating racial bias in machine learning.

Citation
Kostick-Quenet KM, Cohen IG, Gerke S, Lo B, Antaki J, Movahedi F, Njah H, Schoen L, Estep JE, Blumenthal-Barby JS. Mitigating racial bias in machine learning. J Law Med Ethics. 2022;50(1):92-100. doi: 10.1017/jme.2022.13. PMID: 35243993; PMCID: PMC12140104.
Principal Investigator
Blumenthal-Barby, Jennifer
Project Name
A Multi-Site Trial to Test Benefits of Adding a Personalized Risk Calculator to an Online Decision Aid for Left Ventricular Assist Device

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