Hospital-Based Medicine

LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review

Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination issues, such as generating inaccurate content or fabricating information without valid sources. In addition, electronic medical records (EMRs) typically consist of long-form data, making it challenging for LLMs to attribute the generated content to th...

Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographical disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating...

Potential role of large language models and personalized medicine to innovate cardiac rehabilitation.

Cardiac rehabilitation is a crucial multidisciplinary approach to improve patient outcomes. There is a growing body of evidence that suggests that the...

Regularizing Log-Linear Cost Models for Inpatient Stays by Merging ICD-10 Codes

Cost models in healthcare research must balance interpretability, accuracy, and parameter consistency. However, interpretable models often struggle ...

PiCME: Pipeline for Contrastive Modality Evaluation and Encoding in the MIMIC Dataset

Multimodal deep learning holds promise for improving clinical prediction by integrating diverse patient data, including text, imaging, time-series, ...

Evaluating Hierarchical Clinical Document Classification Using Reasoning-Based LLMs

This study evaluates how well large language models (LLMs) can classify ICD-10 codes from hospital discharge summaries, a critical but error-prone t...

Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept

Triage errors, including undertriage and overtriage, are persistent challenges in emergency departments (EDs). With increasing patient influx and st...

Current State of Fibrotic Interstitial Lung Disease Imaging.

Interstitial lung disease (ILD) diagnosis is complex, continuously evolving, and increasingly reliant on thin-section chest CT. Multidisciplinary disc...

Machine learning approaches for predicting prolonged hospital length of stay after lumbar fusion surgery in patients aged 75 years and older: a retrospective cohort study based on comprehensive geriatric assessment.

OBJECTIVE: Postoperative recovery following lumbar fusion surgery in patients aged 75 years and older often requires a prolonged length of stay (PLOS)...

Evaluating the Prognostic Value of Kansas City Cardiomyopathy Questionnaire (KCCQ) Scores for 6-Month Readmissions in Southeast Asian Populations With Heart Failure.

BACKGROUND: Heart failure (HF) is a prevalent cause of hospital readmissions. Our study aims to determine the correlation between the Kansas City Card...

EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.

Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effec...

Approaches for Measuring and Predicting Fouling During Thermal Processing of Dairy Solutions.

Fouling during the thermal processing of dairy products remains a significant challenge, reducing operational efficiency, increasing energy consumptio...

Insights into suspended sediment and microplastic budget of a lowland river: integrating in-situ measurements, Sentinel-2 imagery, and machine learning.

The calculations on fluvial microplastic load (MPL) provide dynamic and actionable metrics for understanding microplastic (MP) particle emissions to t...

Predictive modeling of ARDS mortality integrating biomarker/cytokine, clinical and metabolomic data.

Acute Respiratory Distress Syndrome (ARDS), characterized by the rapid onset of respiratory failure and mortality rates of ∼40%, remains a significant...

Medical physicists at the forefront of multidisciplinary AI integration in healthcare.

Machine Learning-Based Prediction to Support ICU Admission Decision Making among Very Old Patients with Respiratory Infections: A Proof of Concept on a Nationwide Population-Based Cohort Study.

BackgroundIntensive care unit (ICU) hospitalizations of very old patients with acute respiratory infection have risen. The decision-making process for...

[Rethinking scientific integrity in plastic surgery: Reflections on editorial and institutional responsibilities].

Plastic surgery, by nature an innovative discipline, has historically relied on clinical case reports to advance its techniques. Often unique, these c...

Evaluating marine environmental pollution using Fuzzy Analytic Hierarchy Process (FAHP): A comprehensive framework for sustainable coastal and oceanic management.

Marine pollution poses a significant threat to ecosystems, biodiversity, and human health, necessitating a structured evaluation framework. This study...

Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unst...

A comprehensive review of ICU readmission prediction models: From statistical methods to deep learning approaches.

The prediction of Intensive Care Unit (ICU) readmission has become a crucial area of research due to the increasing demand for ICU resources and the n...