Hospitalists

A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.

BACKGROUND: The rapid advancement of Artificial Intelligence (AI) has led to its widespread application across various domains, showing encouraging outcomes. Many studies have utilized AI to forecast emergency department (ED) disposition, aiming to forecast patient outcomes earlier and to allocate resources better; however, a dearth of comprehensive review literature exists to assess the objective...

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor ...

Integrating Dynamic Red Blood Cell Distribution Width Monitoring and β-Blocker Therapy for Mortality Prediction in Intensive Care Unit Cardiomyopathy Patients: A Bayesian Multivariate Joint Model and Machine Learning Study.

Cardiomyopathy is a key cause of cardiovascular mortality in critically ill patients. Although red blood cell distribution width (RDW) is recognized ...

Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition.

Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can result in muscle atrophy, polyneuropathy, increase...

Assessing and validating machine learning-enhanced imputation of admission American Spinal Injury Association Impairment Scale grades for spinal cord injury.

OBJECTIVE: The American Spinal Injury Association Impairment Scale (AIS) assigned at patient admission is an important predictor of outcomes following...

Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling.

Regardless of the materials' intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The pr...

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.

An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This...

MIMIC-\RNum{4}-Ext-22MCTS: A 22 Millions-Event Temporal Clinical Time-Series Dataset with Relative Timestamp for Risk Prediction

Clinical risk prediction based on machine learning algorithms plays a vital role in modern healthcare. A crucial component in developing a reliable ...

AI-ready Snow Radar Echogram Dataset (SRED) for climate change monitoring

Tracking internal layers in radar echograms with high accuracy is essential for understanding ice sheet dynamics and quantifying the impact of accel...

Exploring the Potential of ChatGPT for the Summarization of Patient Medical Histories: A Pilot Study.

Improvements in operations through the use of artificial intelligence (AI) are expected in various fields. Chat Generative Pre-trained Transformer (Ch...

Temporal Entailment Pretraining for Clinical Language Models over EHR Data

Clinical language models have achieved strong performance on downstream tasks by pretraining on domain specific corpora such as discharge summaries ...

Prognosis Of Lithium-Ion Battery Health with Hybrid EKF-CNN+LSTM Model Using Differential Capacity

Battery degradation is a major challenge in electric vehicles (EV) and energy storage systems (ESS). However, most degradation investigations focus ...

Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification

Federated Learning (FL) faces major challenges regarding communication overhead and model privacy when training large language models (LLMs), especi...

Paging Dr. GPT: Extracting Information from Clinical Notes to Enhance Patient Predictions

There is a long history of building predictive models in healthcare using tabular data from electronic medical records. However, these models fail t...

Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis

Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e...

Can Reasoning LLMs Enhance Clinical Document Classification?

Clinical document classification is essential for converting unstructured medical texts into standardised ICD-10 diagnoses, yet it faces challenges ...

On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions

Understanding and mitigating biases is critical for the adoption of large language models (LLMs) in high-stakes decision-making. We introduce Admiss...

Going beyond explainability in multi-modal stroke outcome prediction models

Aim: This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data. ...

Leveraging LLMs for Predicting Unknown Diagnoses from Clinical Notes

Electronic Health Records (EHRs) often lack explicit links between medications and diagnoses, making clinical decision-making and research more diff...

Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records

We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity reco...