Available to mentor
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Center MemberCenter for Integrative Research in Critical Care
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Center MemberInstitute for Healthcare Policy and Innovation
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Center MemberCaswell Diabetes Institute
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Center Membere-Health and Artificial Intelligence Initiative
Dr. Najarian, as the director of the Biomedical and Clinical Informatics Lab at the University of Michigan, is a distinguished researcher in the field of computational medicine, with a primary focus on leveraging artificial intelligence (AI) and machine learning (ML) for medical applications through clinical decision support systems and drug discovery and assessment. Resulting AI-based technologies for clinical decision support systems, which have been funded by agencies such as NSF, NIH, and DoD, feature advanced signal/image processing and ML techniques. In addition, he has collaborated with industry giants including J&J, BMS, and Toyota aimed at developing AI-based medical decision support systems for cardiovascular and gastrointestinal diseases. The success of these endeavors is evident in the licensing of these technologies to private companies, highlighting the real-world impact in advancing healthcare through computational medicine. Dr. Najarian is an Associate Director of the Michigan Institute for Data Science (MIDAS), focusing specifically on the integration of AI in healthcare and an Associate Director of the Weil Institute where he directs efforts toward AI applications in critical care. These roles signify Dr. Najarian’s commitment to driving innovation and improving patient outcomes through the implementation of cutting-edge AI technologies. Furthermore, as the Director of the Center for Data-Driven Drug Development and Treatment Assessment (DATA), an NSF IUCRC, Dr. Najarian coordinates all activities in this national center towards advancing the frontier of AI in drug development and contributes significantly to research in this field.
Najarian Lab
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Zhang Y, Golbus JR, Wittrup E, Aaronson KD, Najarian K. BMC Med Inform Decis Mak, 2024 Feb 14; 24 (1): 53Journal ArticleEnhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data.
DOI:10.1186/s12911-024-02453-y PMID: 38355512 -
Zare A, Wittrup E, Najarian K. Sensors (Basel), 2024 Mar 29; 24 (7):Journal ArticleMechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors.
DOI:10.3390/s24072189 PMID: 38610400 -
Ren X, Mayhew K, Rozwadowski M, Khan S, Rao A, Kumar R, Najarian K, Paludo J, Binder AF, Sung AD, Choi SW, Tewari M. Transplantation and Cellular Therapy, 2024 Feb; 30 (2): s119 - s120.Journal ArticleDistinguishing Infection- Vs. Non-Infection-Related Febrile Neutropenia in HCT Patients By Machine Learning Analysis of Continuous Body Temperature Data Collected Using a Wearable Sensor
DOI:10.1016/j.jtct.2024.01.006 -
Gao Z, Wittrup E, Najarian K. Bioengineering (Basel), 2024 Jan 29; 11 (2):Journal ArticleLeveraging Multi-Annotator Label Uncertainties as Privileged Information for Acute Respiratory Distress Syndrome Detection in Chest X-ray Images.
DOI:10.3390/bioengineering11020133 PMID: 38391619 -
Gryak J, Zhang J, Najarian K, Ravikumar R, Sanders G, Schuler C.Journal ArticlePrediction of Pediatric Peanut Oral Food Challenge Outcomes using Machine Learning
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Alge OP, Gryak J, VanEpps JS, Najarian K. Diagnostics (Basel), 2024 Jan 23; 14 (3):Journal ArticleSepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals.
DOI:10.3390/diagnostics14030234 PMID: 38337750 -
Stidham RW, Cai L, Cheng S, Rajaei F, Hiatt T, Wittrup E, Rice MD, Bishu S, Wehkamp J, Schultz W, Khan N, Stojmirovic A, Ghanem LR, Najarian K. Gastroenterology, 2024 Jan; 166 (1): 155 - 167.e2.Journal ArticleUsing Computer Vision to Improve Endoscopic Disease Quantification in Therapeutic Clinical Trials of Ulcerative Colitis.
DOI:10.1053/j.gastro.2023.09.049 PMID: 37832924 -
Zhang Y, Aaronson KD, Gryak J, Wittrup E, Minoccheri C, Golbus JR, Najarian K. PLoS One, 2023 18 (11): e0295016Journal ArticlePredicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network.
DOI:10.1371/journal.pone.0295016 PMID: 38015947