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Artificial Intelligence

Comparison of an artificial intelligence-enabled patient decision aid vs educational material on decision quality, shared decision-making, patient experience, and functional outcomes in adults with knee osteoarthritis: A randomized clinical trial.

Citation
Jayakumar P, Moore MG, Furlough KA, Uhler LM, Andrawis JP, Koenig KM, Aksan N, Rathouz PJ, Bozic KJ. Comparison of an artificial intelligence-enabled patient decision aid vs educational material on decision quality, shared decision-making, patient experience, and functional outcomes in adults with knee osteoarthritis: A randomized clinical trial. JAMA Netw Open. 2021 Feb 1;4(2):e2037107. doi: 10.1001/jamanetworkopen.2020.37107. PMID: 33599773; PMCID: PMC7893500.
Principal Investigator
Bozic, Kevin John
Tsevat, Joel
Project Name
Incorporating Patient-Reported Outcomes into Shared Decision Making with Patients with Osteoarthritis of the Knee

Advancing motivational interviewing training with artificial intelligence: ReadMI.

Citation
Hershberger PJ, Pei Y, Bricker DA, Crawford TN, Shivakumar A, Vasoya M, Medaramitta R, Rechtin M, Bositty A, Wilson JF. Advancing motivational interviewing training with artificial intelligence: ReadMI. Adv Med Educ Pract. 2021 Jun 4;12:613-618. doi: 10.2147/AMEP.S312373. PMID: 34113205; PMCID: PMC8186935.
Principal Investigator
Hershberger, Paul J.
Project Name
Real-time Assessment of Dialogue in Motivational Interviewing Training (ReadMI)

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Citation
Wissel BD, Greiner HM, Glauser TA, Pestian JP, Kemme AJ, Santel D, Ficker DM, Mangano FT, Szczesniak RD, Dexheimer JW. Early identification of epilepsy surgery candidates: A multicenter, machine learning study. Acta Neurol Scand. 2021 Jul;144(1):41-50. doi: 10.1111/ane.13418. Epub 2021 Mar 26. PMID: 33769560; PMCID: PMC8178229.
Principal Investigator
Dexheimer, Judith W.
Project Name
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients

Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications.

Citation
Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240. PMID: 33783520; PMCID: PMC8010590.
Principal Investigator
Avidan, Michael
Project Name
Anesthesiology Control Tower: Feedback Alerts to Supplement Treatment (ACTFAST)

Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department.

Citation
Korach ZT, Gradwohl S, Messinger A, Bookman K, Cohen K, Zhou L, Goss F. Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department. Int J Med Inform. 2021 May;149:104410. doi: 10.1016/j.ijmedinf.2021.104410. Epub 2021 Feb 5. PMID: 33621793.
Principal Investigator
Goss, Foster R.
Project Name
Natural Language Processing To Identify and Rank Clinically Relevant Information for EHRs in the Emergency Department

Vancomycin hypersensitivity reactions documented in electronic health records.

Citation
Alvarez-Arango S, Yerneni S, Tang O, Zhou L, Mancini CM, Blackley SV, Keet CA, Blumenthal KG. Vancomycin hypersensitivity reactions documented in electronic health records. J Allergy Clin Immunol Pract. 2021 Feb;9(2):906-912. doi: 10.1016/j.jaip.2020.09.027. Epub 2020 Oct 1. PMID: 33011300; PMCID: PMC7870516.
Principal Investigator
Zhou, Li
Project Name
Improving Allergy Documentation and Clinical Decision Support in the Electronic Health Record

Annual Conferences on Health IT & Analytics 2021-2023

Description

The annual Conference on Health IT and Analytics (CHITA) facilitates the development of research and implementation lessons; fosters relationships among academics, policymakers, and practitioners; and helps disseminate research and train the next generation of health IT and analytics researchers with diverse backgrounds to improve healthcare quality, efficiency, and equity.

Grant Number
R13 HS028541
Principal Investigator(s)
Agarwal, Ritu

Optimizing corpus creation for training word embedding in low resource domains: a case study in Autism Spectrum Disorder (ASD).

Citation
Gu Y, Leroy G, Pettygrove S, Galindo MK, Kurzius-Spencer M. Optimizing corpus creation for training word embedding in low resource domains: a case study in Autism Spectrum Disorder (ASD). AMIA Annu Symp Proc. 2018 Dec 5;2018:508-517. PMID: 30815091.
Principal Investigator
Leroy, Gondy
Project Name
Enabling Large-Scale Research on Autism Spectrum Disorders Through Automated Processing of EHR Using Natural Language Understanding