Deep Learning Applications in Healthcare

Displaying 1 - 21 of 21CSV
Mansoor, M., Ibrahim, A. F., Grindem, D., & Baig, A. (2025). Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance. JMIRx Med, 6, e65263–e65263. https://doi.org/10.2196/65263
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Oelsner, E. C., Krishnaswamy, A., Rustamov, R., Balte, P. P., Ali, T., Allen, N. B., Andrews, H. F., Anugu, P., Arynchyn, A., Bateman, L. A., Cai, J., Chang, H., Chen, L., Elkind, M. S. V., Floyd, J. S., Gabriel, K. P., Gharib, S. A., Gutierrez, J. D., Stukovsky, K. H., … Post, W. S. (2025). Classifying COVID-19 hospitalizations in epidemiology cohort studies: The C4R study. PLOS ONE, 20(2), e0316198. https://doi.org/10.1371/journal.pone.0316198
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Schuemie, M. J., Ostropolets, A., Zhuk, A., Korsik, U., Seo, S. I., Suchard, M. A., Hripcsak, G., & Ryan, P. B. (2025). Standardized patient profile review using large language models for case adjudication in observational research. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01433-4
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Zolnoori, M., Zolnour, A., Vergez, S., Sridharan, S., Spens, I., Topaz, M., Noble, J. M., Bakken, S., Hirschberg, J., Bowles, K., Onorato, N., & McDonald, M. V. (2024). Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae300
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Scroggins, J. K., Hulchafo, I. I., Harkins, S., Scharp, D., Moen, H., Davoudi, A., Cato, K., Tadiello, M., Topaz, M., & Barcelona, V. (2024). Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae290
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Campbell, E. A., Bose, S., & Masino, A. J. (2024). Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier. PLOS Digital Health, 3(10), e0000642. https://doi.org/10.1371/journal.pdig.0000642
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Wen, A., Wang, L., He, H., Fu, S., Liu, S., Hanauer, D. A., Harris, D. R., Kavuluru, R., Zhang, R., Natarajan, K., Pavinkurve, N. P., Hajagos, J., Rajupet, S., Lingam, V., Saltz, M., Elowsky, C., Moffitt, R. A., Koraishy, F. M., … Palchuk, M. B. (2024). A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation. JMIR Medical Informatics, 12, e49997. https://doi.org/10.2196/49997
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Xue, C., Kowshik, S. S., Lteif, D., Puducheri, S., Jasodanand, V. H., Zhou, O. T., Walia, A. S., Guney, O. B., Zhang, J. D., Pham, S. T., Kaliaev, A., Andreu-Arasa, V. C., Dwyer, B. C., Farris, C. W., Hao, H., Kedar, S., Mian, A. Z., Murman, D. L., O’Shea, S. A., … Kolachalama, V. B. (2024). AI-based differential diagnosis of dementia etiologies on multimodal data. Nature Medicine. https://doi.org/10.1038/s41591-024-03118-z
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Clelland, C. L., Moss, S., & Clelland, J. D. (2024). Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references. Exploration of Digital Health Technologies, 1–6. Portico. https://doi.org/10.37349/edht.2024.00006
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Ostropolets, A., Hripcsak, G., Husain, S. A., Richter, L. R., Spotnitz, M., Elhussein, A., & Ryan, P. B. (2023). Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records. Journal of the American Medical Informatics Association, 31(1), 119–129. https://doi.org/10.1093/jamia/ocad202
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Silverman, E. K., Kim, A. Y., Make, B. J., Regan, E. A., Morrow, J. D., Hersh, C. P., O’Brien, J., Crapo, J. D., Hansel, N. N., Criner, G., Flenaugh, E. L., Conrad, D., Casaburi, R., Bowler, R. P., Hanania, N. A., Barr, R. G., Bhatt, S. P., Sciurba, F. C., Anzueto, A., … O’Rourke, P. P. (2023). Returning incidentally discovered Hepatitis C RNA-seq results to COPDGene study participants. Npj Genomic Medicine, 8(1). https://doi.org/10.1038/s41525-023-00379-4
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Keloth, V. K., Zhou, S., Lindemann, L., Zheng, L., Elhanan, G., Einstein, A. J., Geller, J., & Perl, Y. (2023). Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients. BMC Medical Informatics and Decision Making, 23(S1). https://doi.org/10.1186/s12911-023-02136-0
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Azevedo, T., Bethlehem, R. A. I., Whiteside, D. J., Swaddiwudhipong, N., Rowe, J. B., Lió, P., Rittman, T., Silbert, L. C., Lind, B., Crissey, R., Kaye, J. A., Carter, R., Dolen, S., Quinn, J., Schneider, L. S., Pawluczyk, S., Becerra, M., Teodoro, L., … Li, G. (2023). Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank. Communications Medicine, 3(1). https://doi.org/10.1038/s43856-023-00313-w
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Keloth, V. K., Banda, J. M., Gurley, M., Heider, P. M., Kennedy, G., Liu, H., Liu, F., Miller, T., Natarajan, K., V Patterson, O., Peng, Y., Raja, K., Reeves, R. M., Rouhizadeh, M., Shi, J., Wang, X., Wang, Y., Wei, W.-Q., Williams, A. E., … Xu, H. (2023). Representing and utilizing clinical textual data for real world studies: An OHDSI approach. Journal of Biomedical Informatics, 142, 104343. https://doi.org/10.1016/j.jbi.2023.104343
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Wang, K., Shi, Q., Sun, C., Liu, W., Yau, V., Xu, C., Liu, H., Sun, C., Yin, C., Wei, X., Li, W., & Rong, L. (2023). A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study. Frontiers in Neuroscience, 17. https://doi.org/10.3389/fnins.2023.1130831
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Park, J., Artin, M. G., Lee, K. E., Pumpalova, Y. S., Ingram, M. A., May, B. L., Park, M., Hur, C., & Tatonetti, N. P. (2022). Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer. Journal of Biomedical Informatics, 131, 104095. https://doi.org/10.1016/j.jbi.2022.104095
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Song, J., Hobensack, M., Bowles, K. H., McDonald, M. V., Cato, K., Rossetti, S. C., Chae, S., Kennedy, E., Barrón, Y., Sridharan, S., & Topaz, M. (2022). Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care. Journal of Biomedical Informatics, 128, 104039. https://doi.org/10.1016/j.jbi.2022.104039
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Kostka, K., Duarte-Salles, T., Prats-Uribe, A., Sena, A. G., Pistillo, A., Khalid, S., Lai, L. Y., Golozar, A., Alshammari, T. M., Dawoud, D. M., Nyberg, F., Wilcox, A. B., Andryc, A., Williams, A., Ostropolets, A., Areia, C., Jung, C. Y., Harle, C. A., Reich, C. G., … Prieto-Alhambra, D. (2022). Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS. Clinical Epidemiology, Volume 14, 369–384. https://doi.org/10.2147/clep.s323292
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