Hospitalists

Semi-supervised learning for marine anomaly detection on board satellites

Aquatic bodies face numerous environmental threats caused by several marine anomalies. Marine debris can devastate habitats and endanger marine life through entanglement, while harmful algal blooms can produce toxins that negatively affect marine ecosystems. Additionally, ships may discharge oil or engage in illegal and overfishing activities, causing further harm. These marine anomalies can be ...

Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA

To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context grounded in real-world patient experiences. ...

MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways

Inpatient pathways demand complex clinical decision-making based on comprehensive patient information, posing critical challenges for clinicians. De...

Application of Interpretable Machine Learning Models to Predict the Risk Factors of HBV-Related Liver Cirrhosis in CHB Patients Based on Routine Clinical Data: A Retrospective Cohort Study.

Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrho...

PyEvalAI: AI-assisted evaluation of Jupyter Notebooks for immediate personalized feedback

Grading student assignments in STEM courses is a laborious and repetitive task for tutors, often requiring a week to assess an entire class. For stu...

Towards Conditioning Clinical Text Generation for User Control

Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which conti...

Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts

Sharing sensitive texts for scientific purposes requires appropriate techniques to protect the privacy of patients and healthcare personnel. Anonymi...

CliniQ: A Multi-faceted Benchmark for Electronic Health Record Retrieval with Semantic Match Assessment

Electronic Health Record (EHR) retrieval plays a pivotal role in various clinical tasks, but its development has been severely impeded by the lack o...

MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters

While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex ...

Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.

PURPOSE: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing bi...

URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT

With the rapid advancement of Artificial Intelligence, particularly in Natural Language Processing, Large Language Models (LLMs) have become pivotal...

IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients

Effective fall risk assessment is critical for post-stroke patients. The present study proposes a novel, data-informed fall risk assessment method b...

Deep Learning–Based Early Detection of Major Adverse Cerebral Injuries in Cardiothoracic and Vascular Surgery

Despite advances in central nervous system (CNS)-protective anesthetic and surgical strategies, perioperative stroke remains a significant concern in ...

Potential of ChatGPT in Youth Mental Health Emergency Triage: Comparative Analysis with Clinicians

Large language models (LLMs), such as GPT-4, are increasingly integrated into healthcare to support clinicians in making informed decisions. Given Cha...

Summarizing Clinical Notes using LLMs for ICU Bounceback and Length-of-Stay Prediction

Recent advances in the Large Language Models (LLMs) provide a promising avenue for retrieving relevant information from clinical notes for accurate ri...

A pragmatic randomized controlled trial of artificial intelligence (AI)-based predictive analytics monitoring for early detection of clinical deterioration

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

Systemic Metabolic Alterations after Aneurysmal Subarachnoid Hemorrhage: A Plasma Metabolomics Approach

Aneurysmal subarachnoid hemorrhage (aSAH) causes systemic changes that contribute to delayed cerebral ischemia (DCI) and morbidity. Circulating metabo...

A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outco...

A Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke

We developed a classifier to infer acute ischemic stroke (AIS) severity from Medicare claims using the Modified Rankin Scale (mRS) at discharge. The c...

Using Artificial Intelligence to Personalize Caring Contact Messages for Recently Discharged Patients: Protocol for a Mixed-Methods Feasibility Study

Suicide risk is substantially elevated following discharge from a psychiatric hospitalization. Caring Contact (CC) messages are brief messages of hope...