Can Physician Expertise Improve Machine Learning Identification of Delirium?

• URL: http://arxiv.org/abs/2606.30651v1
• Date: Tue, 16 Jun 2026 01:12:47 GMT
• Title: Can Physician Expertise Improve Machine Learning Identification of Delirium?
• Abstract summary: Delirium is common in hospitalized patients and is often missed in routine care.<n>We present a user-centered interactive machine learning (UC-iML) framework for delirium detection support.
• Score: 3.023141540283701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Delirium is common in hospitalized patients and is often missed in routine care. We present a user-centered interactive machine learning (UC-iML) framework for delirium detection support that combines physician-guided feature refinement with interpretable modeling. Using 3,862 labeled admissions from six Toronto hospitals in the General Medicine Inpatient Initiative (GEMINI), we integrate administrative variables, laboratory results, medications, and a radiology-derived text indicator. Physicians guide feature refinement and model evaluation, and Shapley Additive exPlanations (SHAP) are used to summarize feature attribution. We evaluate standard supervised classifiers with temporally separated holdout testing and a later-phase validation cohort. Compared with automated and baseline variants, the proposed framework shows better overall discrimination and stronger temporal robustness, while the explanations highlight clinically meaningful signals. These results support UC-iML as a practical human-in-the-loop framework for clinically relevant delirium modeling.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training [26.518178155362634]
  We propose a principled vision-language pre-training framework for fine-grained medical volumetric representation learning.<n>ASAP integrates three key components: (1) an anatomy-aware knowledge injection module that incorporates organ-level structural priors; (2) a semantically-adaptive selective alignment mechanism that associates sentence-level findings with localized regions; and (3) a semantically-adaptive fusion module for effective interaction between anatomically informed visual features and grounded textual cues.
  arXiv  Detail & Related papers  (2026-05-30T07:59:21Z)
• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval [12.265116154395434]
  We present MED-COPILOT, an interactive clinical decision-support system designed for clinicians and medical trainees.<n>The system builds a structured knowledge graph from WHO and NICE guidelines, applies community-level summarization for efficient retrieval, and maintains a 36,000-case similar-patient database.
  arXiv  Detail & Related papers  (2026-02-28T04:32:03Z)
• Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models [51.91760712805404]
  We introduce VivaBench, a benchmark for evaluating sequential clinical reasoning in large language models (LLMs)<n>Our dataset consists of 1762 physician-curated clinical vignettes structured as interactive scenarios that simulate a (oral) examination in medical training.<n>Our analysis identified several failure modes that mirror common cognitive errors in clinical practice.
  arXiv  Detail & Related papers  (2025-10-11T16:24:35Z)
• Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture [8.072932739333309]
  We introduce a collaborative multi-agent system (MAS) that models a clinical consultation team to address this gap.<n>The system is tasked with identifying clinical problems by analyzing only the Subjective (S) and Objective (O) sections of SOAP notes.<n>A Manager agent orchestrates a dynamically assigned team of specialist agents who engage in a hierarchical, iterative debate to reach a consensus.
  arXiv  Detail & Related papers  (2025-08-29T17:31:24Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Investigating Alternative Feature Extraction Pipelines For Clinical Note Phenotyping [0.0]
  Using computational systems for the extraction of medical attributes offers many applications. BERT-based models can be used to transform clinical notes into a series of representations. We propose an alternative pipeline utilizing ScispaCyNeumann for extraction of common diseases.
  arXiv  Detail & Related papers  (2023-10-05T02:51:51Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Patient Aware Active Learning for Fine-Grained OCT Classification [12.89552245538411]
  We propose a framework that incorporates clinical insights into the sample selection process of active learning. Our medically interpretable active learning framework captures diverse disease manifestations from patients to improve performance of OCT classification.
  arXiv  Detail & Related papers  (2022-06-23T05:47:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.