Dligach, Dmitriy

The TRIPOD-LLM reporting guideline for studies using large language models

Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G., Demner-Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 615

Large-scale identification of patients with cerebral aneurysms using natural language processing

Castro, V. M., Dligach, D., Finan, S., Yu, S., Can, A., Abd-El-Barr, M., Gainer, V., Shadick, N. A., Murphy, S., Cai, T., et al. (2017) · Neurology

Machine Learning in HealthcareIntracranial Aneurysms: Treatment and Complications

Cited by 98

Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation

Afshar, M., Phillips, A., Karnik, N., Mueller, J., To, D., Gonzalez, R., Price, R., Cooper, R., Joyce, C., & Dligach, D. (2019) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 94

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups

Thompson, H. M., Sharma, B., Bhalla, S., Boley, R., McCluskey, C., Dligach, D., Churpek, M. M., Karnik, N. S., & Afshar, M. (2021) · Journal of the American Medical Informatics Association

Ethics and Social Impacts of AIMachine Learning in Healthcare

Cited by 78

Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): A retrospective deep learning study

Afshar, M., Sharma, B., Dligach, D., Oguss, M., Brown, R., Chhabra, N., Thompson, H. M., Markossian, T., Joyce, C., Churpek, M. M., et al. (2022) · The Lancet Digital Health

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 55

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults

Afshar, M., Adelaine, S., Resnik, F., Mundt, M. P., Long, J., Leaf, M., Ampian, T., Wills, G. J., Schnapp, B., Chao, M., et al. (2023) · JMIR Medical Informatics

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 44

A scoping review of publicly available language tasks in clinical natural language processing

Gao, Y., Dligach, D., Christensen, L., Tesch, S., Laffin, R., Xu, D., Miller, T., Uzuner, O., Churpek, M. M., & Afshar, M. (2022) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 28

Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge

Croxford, E., Gao, Y., First, E., Pellegrino, N., Schnier, M., Caskey, J., Oguss, M., Wills, G., Chen, G., Dligach, D., et al. (2025) · medRxiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 25

Learning Patient Representations from Text

Dligach, D., & Miller, T. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 13

Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI

Koyner, J. L., Martin, J., Carey, K. A., Caskey, J., Edelson, D. P., Mayampurath, A., Dligach, D., Afshar, M., & Churpek, M. M. (2025) · Clinical Journal of the American Society of Nephrology

Acute Kidney Injury ResearchMachine Learning in Healthcare

Cited by 10

When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?

Gao, Y., Myers, S., Chen, S., Dligach, D., Miller, T. A., Bitterman, D., Churpek, M., & Afshar, M. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 8

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models

Gao, J., Chen, G., O’Rourke, A. P., Caskey, J., Carey, K. A., Oguss, M., Stey, A., Dligach, D., Miller, T., Mayampurath, A., et al. (2024) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 5