Hospitalists

Effects of psychological torture and cybertorture with emerging digital technologies under anti-torture legal obligations in China: A mixed methods research in risks and remedies.

International norms and domestic law-making and decision-making often underestimate the effects of psychological torture and the challenges posed by digital technologies like artificial intelligence, neurotechnology, and cybertechnology, such as cybertorture, used in interrogation techniques. This article focuses on how anti-torture legal measures, both in the international context and in their do...

A deep learning strategy to identify cell types across species from high-density extracellular recordings.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics and pharmacology u...

Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa.

Unplanned readmission within 30 days is a major challenge both globally and in South Africa. The aim of this study was to develop a machine learning m...

Enhancing readmission prediction model in older stroke patients by integrating insight from readiness for hospital discharge: Prospective cohort study.

BACKGROUND: The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-l...

Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.

INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancem...

Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.

BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpo...

Prediction of 90 day mortality in elderly patients with acute HF from e-health records using artificial intelligence.

AIMS: Mortality risk after hospitalization for heart failure (HF) is high, especially in the first 90 days. This study aimed to construct a model auto...

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate no...

Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission.

OBJECTIVE: To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in p...

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The tradi...

Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

BACKGROUND: Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the ...

ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.

OBJECTIVES: This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to...

Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.

OBJECTIVES: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening an...

Machine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.

Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been d...

Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.

Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessin...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators a...

A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.

OBJECTIVE: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neur...

Predicting Discharge Destination From Inpatient Rehabilitation Using Machine Learning.

Predicting discharge destination for patients at inpatient rehabilitation facilities is important as it facilitates transitions of care and can improv...

Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.

Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presen...

A machine learning-based clinical predictive tool to identify patients at high risk of medication errors.

A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality...