Machine Learning in Healthcare

Últimos artículos

• Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps

  Michael Vasilakakis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece), Dimitris K. Iakovidis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece) · 2 de octubre de 2026

  Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical data is restricted due to privacy, ethical, or data availability limitations. Existing probabilistic and deep generative models often lack interpreta…

• From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning

  Jialu Pi, Yanan Ma, Weijie Chen, Owen Crystal, Shubham Trivedi, Stephen Xie, Anna Silverman, Matthew Stib, Chadi Ayoub, Reza Arsanjani, Imon Banerjee · 1 de octubre de 2026

  Major adverse cardiovascular events (MACE) remain the leading cause of mortality worldwide. Opportunistic screening using routinely acquired clinical data offers a scalable approach for identifying high-risk individuals before acute events occur. Although chest X-rays (CXRs) capture latent cardiovas…

• Personalized State-Transition-Aware Memory for Clinical Agents

  Maryam Haghifam, Zahra Rajabi, Yizhou Sun, Carlos Morato · 1 de octubre de 2026

  Large language model (LLM) agents that reason over clinical records must track changes in a patient's state while preserving the history needed to understand them. Simply accumulating memories leaves it unclear which information still applies, whereas overwriting earlier memories can erase evidence …

• EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents

  Xinye Yang, Yuli Wang, Cheng Ting Lin, Harrison Bai · 1 de octubre de 2026

  Patient world models and clinical agents aim to predict changes in patients' health and support clinical work. Developing these systems requires reliable histories of patient conditions, treatments, and the information available at each decision. Electronic health records (EHRs) contain these histor…

• PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration

  Dannong Wang, Yuran Zhang, Bian Sun, Alex Stinard, Yuzhang Shang, Song Wang, Yu Tian · 1 de octubre de 2026

  Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Privacy-conscious delegation places disclosure decisions with a local agent, yet removing explicit identifiers is insufficient: quasi-identifiers can a…

• CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations

  Yusuf Kesmen, Aniruddha Mukherjee, Yena Chang, David Sasu, Trevor Brokowski, Alexandra V. Kulinkina, Kristina Keitel, Akhil Arora, Lars Henning Klein, Mary-Anne Hartley · 30 de septiembre de 2026

  Patients often have several co-occurring clinical conditions, and the findings needed to identify and disambiguate them emerge over the course of a consultation. Evaluating clinical reasoning in this setting requires both multi-turn interaction and multi-label diagnosis. We introduce CLIMB, a benchm…

• Explainable and Generalisable LLM-based Cognitive Decline Detection with Spontaneous Speech

  Ziyun Cui, Wen Wu, Chuan Shi, Shuguang Yang, Xueying Gui, Yan Zheng, Qiong Yang, Haiyan Zhao, Wei-Qiang Zhang, Ji Wu, Yelei Li, Nan Li, Chao Zhang · 30 de septiembre de 2026

  Alzheimer's disease (AD) and mild cognitive impairment (MCI), which may precede AD, manifest early through subtle linguistic and acoustic alterations. Traditional diagnostics, however, are often resource-intensive and lack scalability for mass screening. To address these challenges, we introduce a n…

• InfiMed2: A Generalist Medical Multimodal Foundation Model from Contextual Evidence and Stability-Aware Supervision

  Guanghao Zhu, Zeyu Liu, Zhitian Hou, Pengkai Wang, Zhijie Sang, Shuo Cai, Yang Yu, Yuanyi Wang, Yanggan Gu, Congkai Xie, Jianmin Wu, Hongxia Yang · 30 de septiembre de 2026

  Recent medical multimodal models have benefited from larger corpora, broader modality coverage, and stronger reasoning-oriented training, yet effective data design across continued pretraining (CPT) and post-training remains challenging. Medical sources vary substantially in structure, granularity, …

• ReLMem: Learning Recurrent Memory for Longitudinal EHR Modeling

  Zijie Meng, Xiwei Dai, Yingying Zhang, Jian Wu, Xian Wu, Zuozhu Liu · 30 de septiembre de 2026

  Longitudinal electronic health record (EHR) modeling requires integrating new visits with an expanding patient history. Yet the continual accumulation of clinical information imposes increasing computational and memory costs on large language models (LLMs) when they process and retain complete patie…

• Reliability Testing of Medical Model Performance under Distributed Deployment

  Yifei Wang, Xiaohan Zhang, Youtao Ding, Tianlin Li, Xiaoyu Zhang, Yida Yang, Li Pan · 30 de septiembre de 2026

  Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Although modern frameworks improve serving efficiency through tensor parallelism, mixed precision, kernel fusion, and multi-device communication, they are g…

• Calibration-First Cross-Cohort Multimodal Temporal Learning for Transferable Asthma-Risk Forecasting

  Taimoor Ahmad · 30 de septiembre de 2026

  Asthma deterioration forecasting must remain reli- able when patient populations, sensor ecosystems, and available modalities change across cohorts. Existing models commonly optimize within-cohort discrimination and may produce poorly calibrated probabilities after transfer. We present CALIBRA, a ca…

• DynamicDx: Evaluating Evidence Acquisition in Video-Based Diagnosis

  Jiahui Li, Yutong Guo, Nan Yang, Wenzhan Song, Jin Lu, Fei Dou · 29 de septiembre de 2026

  Diagnosing a patient from video requires more than recognizing the sign: a vision-language model must turn what it sees into hypotheses, questions and tests. DynamicDx evaluates each step in 71 neurological consultations across 11 sign categories, linking authentic patient videos to confirmed diagno…

• What Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators

  Zhen Xuen Brandon Low · 29 de septiembre de 2026

  Clinical trajectory models are usually evaluated by next-event accuracy on observed histories. Simulation is different: models must condition on their own generated events, allowing errors to compound. Although this problem is well known in sequence modelling, it has not been systematically quantifi…

• Training-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework

  Surajit Das · 29 de septiembre de 2026

  Most clinical prediction systems learn patient-variable-outcome associations; we investigate a training-free diagnostic paradigm mapping patient observations to explicit medical knowledge. CKG Reasoner integrates candidate-specific Evidence Feature Nodes, patient-reference matching, a bounded Inform…

• EHRAdapt: Adapting Pretrained Language Models to Electronic Health Records with Semantic Priors for Rare Clinical Events

  Andre R Goncalves, Vincent Liu, Priyadip Ray · 29 de septiembre de 2026

  Electronic health records (EHRs) encode clinical histories as (time, modality, code) tuples, whereas pretrained language models expect text tokens. Serializing them as text inflates sequence length and redundantly encodes structure. We introduce EHRAdapt, an adapter that maps tuples directly into a …

• SMARtCARE: Privacy-Preserving Agentic AI Systems for Bounded-Autonomy Clinical Decision Support

  Srini Ramaswamy, Deveeshree Nayak · 29 de septiembre de 2026

  Long-context clinical AI systems can miss relevant patient history when prior admissions fall outside the active reasoning context. In ICU monitoring, this can cause early vital-sign drift to appear nonspecific even when it resembles a prior deterioration pattern. SMARtCARE addresses this gap throug…

• ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMs

  Junyi Gao, Yu Shi, Pingzhao Hu, Ewen M Harrison · 28 de septiembre de 2026

  Clinical prediction models estimate risk from patient measurements, while large language models support medical text understanding and question answering. Yet their language capabilities do not ensure accurate prediction from structured, high-dimensional clinical time series. Improving this ability …

• HCOE: Hyperbolic Clinical Ontology Embeddings from Biomedical Language Models

  Yixuan Li, Weihao Li, Ziyang Song · 28 de septiembre de 2026

  Biomedical language models (LMs) encode textual semantics but do not explicitly preserve medical code hierarchies. We present Hyperbolic Clinical Ontology Embeddings (HCOE) for hierarchy-aware clinical concept representation. HCOE maps frozen BioBERT embeddings into a Poincare ball, combining parent…

• Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark

  Alexander Apartsin, Yehudit Aperstein · 25 de septiembre de 2026

  Prospective clinical actions, the follow-ups, orders, referrals, and instructions that deter-mine what happens to a patient next, are annotated today in thin fragments across incom-patible corpora: each records a text span and one coarse category. We introduce Clinical Intent Extraction (CIE), the t…

• Synthetic Hospital: An Open, Verifiable, Physician-Validated Longitudinal EHR Benchmark

  Christine Park, Valerie Chen, Tim Dettmers · 25 de septiembre de 2026

  Frontier language models are rarely used in clinical workflows because the realistic, longitudinal benchmarks needed to develop them are scarce. Real electronic health record (EHR) data cannot be openly shared due to privacy, ethics or data use issues and it does not contain verifiable ground truth …

• AI-based detection of worsening heart failure from low-resolution telemonitoring data

  Erik Aerts, Yinan Yu, Annika Rosengren, Michael Fu, Martin Lindgren, Falk Dippel, Martin Adiels, Helen Sj\"oland · 25 de septiembre de 2026

  Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to…

• From Prediction to Explainable Provider Behavior Profiles for Fraud, Waste, and Abuse Review

  Yubin Park, Evan Brociner · 25 de septiembre de 2026

  Claims data can show that provider behavior changed but cannot by itself explain why. FWA (fraud, waste, and abuse) review requires identifying material behavior, locating the codes and dollars driving it, and testing plausible explanations. A common alternative, predictive modeling, flags deviation…

• Transferable Evidence Reconstruction for Longitudinal Glucose Representations

  Tian Zhou, Bingqing Peng, Linxiao Yang, Wenwei Wang, Mengni Ye, Beverly Jin, Zuyi Zhu, Jinjie Gu, Liang Sun · 24 de septiembre de 2026

  Long physiological recordings contain many routine measurements, while predictive information is often concentrated in rare events, sustained burden, and recurring temporal patterns. Masked autoencoding recovers measurements; contrastive learning aligns views. We study self-supervision that explicit…

• Prediction Is Not Detection: Evaluating Pre-Recognition Claims in Longitudinal Clinical AI

  Jing Yang, Long R. Jiao, Xiujun Cai, Zongjiu Zhang · 23 de septiembre de 2026

  Clinically useful early detection requires validated pre-recognition lead time. Yet event-based evaluations of longitudinal clinical AI can treat recognition-mediated care-process signals as shortcuts and recognition-dependent endpoints as reference standards, inflating apparent performance and lead…

• Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization

  M. Sajid, Pinki Khatun, M. Tanveer · 23 de septiembre de 2026

  Tabular foundation models have recently shown strong potential for structured biomedical data analysis. Among them, TabPFN has emerged as an effective approach for low-data tabular classification tasks. However, the impact of optimization and preconditioning strategies on biomedical fine-tuning rema…