Hospital-Based Medicine

Efficient Variance-reduced Estimation from Generative EHR Models: The SCOPE and REACH Estimators

Generative models trained using self-supervision of tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction. This is typically done using Monte Carlo simulation for future patient trajectories. However, existing approaches suffer from three key limitations: sparse estimate distributions that poorly differentiate patient risk levels, extreme computational cos...

An Implantable Device that Converses with Patients and Learns to Co-Manage Epilepsy

One-third of the world's 70 million people with epilepsy have seizures that are not controlled by medication; and implantable devices are an exciting option for treatment. These devices improve seizure control and can detect impending attacks, missed medication, and impaired cognition. Unfortunately, they have no way to share this information with their hosts in real-time - a limitation common to ...

UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR

Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocar...

Association of Deep Learning-Derived Temporalis Sarcopenia with Mortality in Acute Ischemic Stroke

Background: Sarcopenia is associated with mortality and morbidity following acute ischemic stroke (AIS), but the diagnosis requires specialized equipm...

Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data

Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy...

Predicting the need for medical care after toxin exposure using SHAP-interpretable gradient boosting

Objective: Experts in poison control centers must accurately and efficiently assess the severity of an exposure, neither delaying care nor pointlessly...

Determinants of Training Corpus Size for Clinical Text Classification

Introduction: Clinical text classification using natural language processing (NLP) models requires adequate training data to achieve optimal performan...

Predicting Healthcare System Visitation Flow by Integrating Hospital Attributes and Population Socioeconomics with Human Mobility Data

Healthcare visitation patterns are influenced by a complex interplay of hospital attributes, population socioeconomics, and spatial factors. However, ...

Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System

Purpose: Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes ...

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment

Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact ...

Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods

Artificial intelligence models in healthcare often fail to improve patient outcomes despite strong predictive performance because they are frequently ...

Achieving Expert-Level Clinical Infection Detection with LLMs from Clinical Documents: Validation in Complex Patient Cases with Cirrhosis

BackgroundSystemic infections are a leading cause of hospitalization and death among patients with cirrhosis. Timely and accurate infection identifica...

Advances in sulfate-reducing bacteria-driven bioelectrolysis: mechanisms and applications in microbial electrolysis cell technology.

The discharge of sulfate-rich wastewater from chemical and pharmaceutical and food processing industries results in serious environmental problems tha...

RIPTOSO: The development of a screening tool for adverse events during forensic-psychiatric inpatient treatments of offenders with schizophrenia spectrum disorders.

Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose sign...

Understanding deep learning models for Length of Stay prediction on critically ill patients through latent space visualization.

BACKGROUND AND OBJECTIVE: Continuous, real-time monitoring of Length of Stay (LoS) for critically ill patients in Intensive Care Units (ICUs) is essen...

Profiling of mangrove forest dynamics in the Fly River delta, Papua New Guinea.

Mangrove forests (MFs), as vital ecosystems in tropical and subtropical coastal regions, play a significant role in the global carbon cycle. However, ...

CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs

Large language models (LLMs), including zero-shot and few-shot paradigms, have shown promising capabilities in clinical text generation. However, re...

Illuminating the Future: Nanophotonics for Future Green Technologies, Precision Healthcare, and Optical Computing

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors inclu...

Bridging Data Gaps of Rare Conditions in ICU: A Multi-Disease Adaptation Approach for Clinical Prediction

Artificial Intelligence has revolutionised critical care for common conditions. Yet, rare conditions in the intensive care unit (ICU), including rec...

Affective-ROPTester: Capability and Bias Analysis of LLMs in Predicting Retinopathy of Prematurity

Despite the remarkable progress of large language models (LLMs) across various domains, their capacity to predict retinopathy of prematurity (ROP) r...