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ORCIDMLHC 2025, Rochester, MN, USA
- Monica Agrawal, Kaivalya Deshpande, Matthew Engelhard, Shalmali Joshi, Shengpu Tang, Iñigo Urteaga:
Proceedings of the Machine Learning for Healthcare Conference (MLHC 2025), 15-16 August 2025, Mayo Clinic, Rochester, MN, USA. Proceedings of Machine Learning Research 298, PMLR 2025 - Yuankang Zhao, Matthew M. Engelhard:
Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model. 1-26 - Wenbo Zhang, Junyu Chen, Christopher Kanan:
INSIGHT: Explainable Weakly-Supervised Medical Image Analysis. 1-28 - Sihang Zeng, Lucas Jing Liu, Jun Wen, Meliha Yetisgen, Ruth Etzioni, Gang Luo:
TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction. 1-27 - Han Yu, Huiyuan Yang, Akane Sano:
LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning. 1-24 - Eric Yang, Jonathan Amar, Jong Ha Lee, Bhawesh Kumar, Yugang Jia:
The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA. 1-27 - Shashank Yadav, Vignesh Subbian:
Monte Carlo ExtremalMask: Uncertainty Aware Time Series Model Interpretability For Critical Care Applications. 1-26 - Asim Ukaye, Numan Saeed, Karthik Nandakumar:
FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation. 1-17 - Ilya Tyagin, Saeideh Valipour, Aliaksandra Sikirzhytskaya, Michael Shtutman, Ilya Safro:
Biomedical Hypothesis Explainability with Graph-Based Context Retrieval. 1-30 - Austin Talbot, Alex V. Kotlar, Lavanya Rishishwar, Yue Ke:
Classifying Copy Number Variations Using State Space Modeling of Targeted Sequencing Data: A Case Study in Thalassemia. 1-21 - Minghui Sun, Matthew M. Engelhard, Benjamin Goldstein:
Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning. 1-21 - Arnold Su, Anna Wong, Fareed Sheriff, Ardavan Saeedi, Li-Wei H. Lehman:
Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction. 1-25 - Eric V. Strobl:
Predicting the Predictable in the Psychiatric High Risk. 1-29 - Xiaobin Shen, Jonathan Elmer, George H. Chen:
Stepwise Fine and Gray: Subject-Specific Variable Selection Shows When Hemodynamic Data Improves Prognostication of Comatose Post-Cardiac Arrest Patients. 1-29 - Sahil Sethi, David Chen, Thomas Statchen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett K. Beaulieu-Jones:
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning. 1-30 - Elliot Schumacher, Dhruv Naik, Anitha Kannan:
Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson’s Disease. 1-19 - Tabea E. Röber, Rob Goedhart, S. Ilker Birbil:
Clinicians’ Voice: Fundamental Considerations for XAI in Healthcare. 1-27 - Fahmida Liza Piya, Rahmatollah Beheshti:
ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs. 1-30 - Anish Narain, Ritam Majumdar, Nikita Narayanan, Dominic C. Marshall, Sonali Parbhoo:
Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models. 1-32 - Monica Munnangi, Akshay Swaminathan, Jason Alan Fries, Jenelle A. Jindal, Sanjana Narayanan, Ivan Lopez, Lucia Tu, Philip Chung, Jesutofunmi A. Omiye, Mehr Kashyap, Nigam Shah:
FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs. 1-36 - Malvern Madondo, Yuan Shao, Yingzi Liu, Jun Zhou, Xiaofeng Yang, Zhen Tian:
Patient-Specific Deep Reinforcement Learning for Automatic Replanning in Head-and-Neck Cancer Proton Therapy. 1-24 - Xuefeng Liu, Songhao Jiang, Ian T. Foster, Jinbo Xu, Rick L. Stevens:
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization. 1-23 - Xuefeng Liu, Songhao Jiang, Chih-chan Tien, Jinbo Xu, Rick L. Stevens:
Bidirectional Hierarchical Protein Multi-Modal Representation Learning. 1-20 - Dariia Kucheruk, Sam Osia, Pouria Mashouri, Elizaveta Rybnikova, Sergey Protserov, Jaryd Hunter, Maksym Muzychenko, Jessie Ting Guo, Michael Brudno:
Optimizing Segmentation of Neonatal Brain MRIs with Partially Annotated Multi-Label Data. 1-21 - Jaesik Kim, Byounghan Lee, Kyung-Ah Sohn, Dokyoon Kim, Young Chan Lee:
Evaluation of Multi-Agent LLMs in Multidisciplinary Team Decision-Making for Challenging Cancer Cases. 1-31 - Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva, Shalmali Joshi, Noémie Elhadad:
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression. 1-35 - Karine Karine, Benjamin M. Marlin:
Enhancing Adaptive Behavioral Interventions with LLM Inference from Participant Described States. 1-21 - Baraa Al Jorf, Farah E. Shamout:
MedPatch: Confidence-Guided Multi-Stage Fusion for Multimodal Clinical Data. 1-29 - Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta:
Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding. 1-21 - Hongtao Hao, Vivek Prabhakaran, Veena A. Nair, Nagesh Adluru, Joseph Austerweil:
Stage-Aware Event-Based Modeling (SA-EBM) for Disease Progression. 1-45 - William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, Ding Zhao:
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling. 1-38 - Chase Fensore, Rodrigo M. Carrillo-Larco, Megha Shah, Joyce C. Ho:
Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions? 1-33 - Jeffrey Feng, Al Rahrooh, Alex Bui:
Error Profiling of Machine Learning Models: An Exploratory Visualization. 1-43 - Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora, Benjamin Shickel:
Uncertainty-Aware Prediction of Parkinson’s Disease Medication Needs: A Two-Stage Conformal Prediction Approach. 1-30 - Arpan Dasgupta, Mizhaan Maniyar, Awadhesh Srivastava, Sanat Kumar, Amrita Mahale, Aparna Hegde, Arun Suggala, Karthikeyan Shanmugam, Milind Tambe, Aparna Taneja:
Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Optimizing Call Timing in Mobile Maternal Health. 1-15 - Mouath Abu Daoud, Chaimae Abouzahir, Leen Kharouf, Walid Al-Eisawi, Nizar Habash, Farah E. Shamout:
MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks. 1-42 - Daphné Chopard, Sonia Laguna, Kieran Chin-Cheong, Annika Dietz, Anna Badura, Sven Wellmann, Julia E. Vogt:
Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings. 1-22 - Seyyed Saeid Cheshmi, Buyao Lyu, Thomas Lisko, Rajesh Rajamani, Robert A. McGovern, Yogatheesan Varatharajah:
Improving Out-of-distribution Human Activity Recognition via IMU-Video Cross-modal Representation Learning. 1-17 - Liangyu Chen, James Burgess, Jeffrey J. Nirschl, Orr Zohar, Serena Yeung-Levy:
The Impact of Image Resolution on Biomedical Multimodal Large Language Models. 1-18 - Piyush Borole, Tongjie Wang, Antonio Vergari, Ajitha Rajan:
Can interpretability and accuracy coexist in cancer survival analysis? 1-33 - Marc Berndt, Andrea Agostini, Beatrice Stocker, Maria Padrutt, Silvio Daniel Brugger, D Sean Froese, Daphné Chopard, Julia E. Vogt:
PhenoRAG: Retrieval-Augmented Generation for Efficient Zero-Shot Phenotype Identification in Clinical Reports. 1-29 - Mohammed Baharoon, Luyang Luo, Michael Moritz, Abhinav Kumar, Sung Eun Kim, Xiaoman Zhang, Miao Zhu, Kent Kleinschmidt, Sri Sai Dinesh Jaliparthi, Sathvik Suryadevara, Rithvik Akula, Mark David Marino, Wenhui Lei, Ibrahim Ethem Hamamci, Pranav Rajpurkar:
State-of-the-Art Text-Prompted Medical Segmentation Models Struggle to Ground Chest CT Findings. 1-30 - Mohd Ashhad, Ricardo Henao:
Generating Accurate Synthetic Survival Data by Conditioning on Outcomes. 1-23 - Mosbah Aouad, Anirudh Choudhary, Awais Farooq, Steven Nevers, Lusine Demirkhanyan, Bhrandon Harris, Suguna Pappu, Christopher Gondi, Ravishankar K. Iyer:
Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records. 1-22 - Guilherme Seidyo Imai Aldeia, Daniel S. Herman, William G. La Cava:
Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models. 1-31
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