Publications
- Under Preparation or Review
- Mungle, T., Andrews, C.A., An, H.S.,et.al., Ensemble Learning to Enhance Identification of Patients with Phenotypes of Interest in Real World Datasets, JAMIA (under review)
- Mungle, T., Andrews, C.A., An, H.S., et.al., Leveraging structured and unstructured data to accurately identify patients with glaucoma for studies involving real-world data.
- Mungle, T., Charu, V., and Okafor, P.N., Inflammatory bowel disease outcomes among hospitalized homeless individuals; Digestive Diseases and Sciences (under review)
- Aruljyothi, L., Mungle, T., Woodward, M., et.al., Evaluating the efficacy of zero-shot LLMs in the extraction of microbial keratitis descriptors.
- Al Garadi, M., Sarker, A., Mungle, T., et.al., Large language models in healthcare
- Conferences
- Mungle, T., Andrews, C.A., Pershing, S., et.al., 2025. Large language models using free text clinical notes outperform ICD coding in properly identifying patients with and without glaucoma. In: 2025 ARVO Annual Meeting, May 4-8, Salt Lake City, Utah. (Accepted)
- Mungle, T., Andrews, C.A., An, H.S., et.al., A multimodal machine learning approach to accurately identify patients with glaucoma in real world data repositories. American Glaucoma Society, Feb 26-March 2; Washington, DC (Accepted as Top 5 Plenary Paper Presentations)
- Aruljyothi L, Mungle T, Woodward MA, Prajna VN, Nallasamy N., 2025. A Comparison of Zero-Shot LLM and Standard NLP Approaches for the Extraction of Microbial Keratitis Descriptors from Clinical Records, In: 2025 ARVO Annual Meeting, May 4-8, Salt Lake City, Utah. (Accepted)
- Full list of Publications