Hospitalists

A hierarchical clinical fusion transformer model for personalized opioid treatment: Development and validation in diabetic surgical patients

Background Machine learning (ML) models are increasingly used to predict adverse outcomes after surgery. However, most rely on static patient characteristics (e.g., age, comorbidities) and overlook clinician-controlled treatment decisions that can be actively modified at the point of care. Discharge opioid prescribing is a key modifiable, clinician-controlled decision, yet optimizing prescribing c...

MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models

Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in translational research, but candidate rankings produced by any single feature-selection or classification method depend on algorithmic choices and rarely reproduce across pipelines. We present a disease-agnostic machine-learning framework that addresses this dependence by systematically benchmarking 25 (f...

Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries

Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream c...

Stigmatizing Language Detection in Opioid Use Disorder Patient-Directed Discharge Clinical Documentation: A Privacy-Preserving Analysis Using a Locally Deployed Large Language Model

Objective: Stigmatizing language in the electronic health record (EHR) has been associated with adverse patient experience in substance use disorder c...

Development and validation of a dynamic risk stratification tool for predicting multidrug-resistant bacterial infections in ICU patients: A clinical prediction model and web-based calculator

Background: Multi-drug resistant Bacterial (MDRB) Infections in the intensive care units (ICUs) substantially elevate patient mortality, prolong hospi...

Post-ED Trajectory Prediction in Abdominal Pain with a Generative Medical Event Model

Importance: Abdominal pain causes roughly 10 million US emergency department (ED) visits annually, most resulting in discharge. Post-discharge courses...

Clinical Note Comparison and Data Retrieval Via Embedding Vectors: Model Selection, Metrics, and Convergence

Background: Embedding models are an integral part of generative AI architectures, transforming text into embedding vectors that represent semantic con...

A biologically-grounded cerebellar spiking network model with realistic synaptic transmission captures complex circuit dynamics.

Cerebellar neural circuit dynamics rely on a rich repertoire of synaptic and excitability mechanisms, which are thought to determine network computati...

RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation

Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves....

Causal Fairness for Survival Analysis

In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to info...

Enhance the after-discharge mortality rate prediction via learning from the medical notes

With the increase of the Electronic Health Records (EHR) data, more and more researchers are developing machine learning models to learn from the medi...

From Data Lifting to Continuous Risk Estimation: A Process-Aware Pipeline for Predictive Monitoring of Clinical Pathways

This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical pathways. The approach integrates data lifting, te...

Development and Validation of a Two-Stage NLP-LLM System for Automated Extraction of Deprescribing Recommendations from Discharge Summaries

Introduction: Polypharmacy in older adults is associated with increased risks of adverse drug events and functional decline. Discharge summaries often...

Multicohort development and validation of a machine learning model to predict six-month functional traumatic brain injury outcomes in a large national registry

Background: Prognostication after moderate-to-severe traumatic brain injury (TBI) rarely captures long-term functional recovery, despite its importanc...

A Systematic Exploration of LLM Behavior for EHR phenotyping

Background Electronic health record (EHR) phenotyping underpins observational research, cohort discovery, and clinical trial screening. Large language...

MIMIC-IV-Phenotype-Atlas (MIPA) : A Publicly Available Dataset for EHR Phenotyping

Introduction Secondary use of electronic health records (EHRs) often requires transforming raw clinical information into research-grade data. A centra...

MEDICALBENCH: EVALUATING LARGE LANGUAGE MODELS TOWARDS IMPROVED MEDICAL CONCEPT EXTRACTION

Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful...

Learning Preference-Based Objectives from Clinical Narratives for Sequential Treatment Decision-Making

Designing reward functions remains a central challenge in reinforcement learning (RL) for healthcare, where outcomes are sparse, delayed, and difficul...

Automating Early Disease Prediction Via Structured and Unstructured Clinical Data

This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructure...

ECG spectrogram-based deep learning model to predict deterioration of patients with early sepsis at the emergency department: a study from the Acutelines data- and biobank

Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scori...