Mary M. Lucas

Mary M. Lucas

Doctoral candidate at Drexel University

NIH AIM-AHEAD Research Fellow

Publications

2024

Mary M. Lucas, Justin Yang, Jon K. Pomeroy, Christopher C. Yang, “Reasoning with Large Language Models for Medical Question Answering,” Journal of the American Medical Informatics Association (2024). https://doi.org/10.1093/jamia/ocae131

Julia A. Haller, Maurizio Tomaiuolo, Mary M. Lucas, Christopher C. Yang, Leslie Hyman, IRIS Registry Analytic Center Consortium (2024). “Disparities in Retinal Vein Occlusion Presentation and Initiation of Anti-VEGF Therapy: an Academy IRIS® Registry Analysis.” Ophthalmology Retina, 8(7), 657–665. https://doi.org/10.1016/j.oret.2024.01.017

Landry, L.G., Lucas, M., Andy, A., Nwafor, E., “Artificial Intelligence Assisted Curation of Population Groups in Biomedical Literature.” International Journal of Digital Curation (2024) (Accepted).

Chang, CH., Lucas, M.M., Lee, Y., Yang, C.C., Lu-Yao, G. (2024). “Beyond Self-consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging”. In: Finkelstein, J., Moskovitch, R., Parimbelli, E. (eds) Artificial Intelligence in Medicine. AIME 2024. Lecture Notes in Computer Science(), vol 14844. Springer, Cham. doi: 10.1007/978-3-031-66538-7_23

Mary M. Lucas, Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang, Jacqueline E. Braughton, Quyen M. Ngo (2024). “An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion”. Proceedings of the 12th IEEE International Conference on Health Informatics (IEEE ICHI 2024), Orlando, FL, June 3-6, 2024. doi: 10.48550/arXiv.2404.03833

Chia-Hsuan Chang, Mary M. Lucas, Grace Lu-Yao, Christopher C. Yang (2024). “Classifying Cancer Stage with Open-Source Clinical Large Language Models”. . Proceedings of the 12th IEEE International Conference on Health Informatics (IEEE ICHI 2024), Orlando, FL, June 3-6, 2024. doi: 10.48550/arXiv.2404.01589

2023

Mary M. Lucas, Chia-Hsuan Chang, and Christopher C. Yang (2023). “Resampling for Mitigating Bias in Predictive Model for Substance Use Disorder Treatment Completion”, 11th IEEE International Conference on Healthcare Informatics (IEEE ICHI), Houston, TX, USA, pp. 709-711, doi: 10.1109/ICHI57859.2023.00128

Mary M. Lucas, Christopher C. Yang, and Mario Schootman. “Investigating health disparities and AI bias in models to predict development of chronic kidney disease in patients with type II diabetes”, presented at NIH AIM-AHEAD Annual Meeting 2023. Rockville, MD. https://doi.org/10.6084/m9.figshare.23929128.v1

2022

Ji, L., Li, Z., Lucas, M., Vodenska, I., Chitkushev, L., Zhang, G.L. (2022). “Pregnancy Outcomes in Women with Pregestational Diabetes”, In: Zlateva, T., Goleva, R. (eds) Computer Science and Education in Computer Science. CSECS 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-031-17292-2_10

2020 and prior

Prachiti Aras, Guanglan Zhang, Mary Lucas, Reza Rawassizadeh, Irena Vodenska, and Lou Chitkushev. “Quality Assessment of Inpatient Medical Claim Data”, presented at IEEE International Conference on Bioinformatics and Biomedicine 2020 (BIBM 2020).

Sakwa, W., Khanna, K., Mueni, M., Rotich, S., & Torongey, P. (2004). “Four-level approximation in disordered medium”, Indian journal of pure & applied physics, 42(5), 355-360.

Sakwa, W., Khanna, K., Mueni, M., Rotich, S., & Torongey, P. (2004). “Transition temperature for 4He liquid adsorbed in disordered media”, Indian journal of pure & applied physics, 42(5), 351-354.