Hospital-Based Medicine

Hospitalists

Latest AI and machine learning research in hospitalists for healthcare professionals.

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Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study.

BACKGROUND: Unplanned readmissions increase unnecessary health care costs and reduce the quality of ...

An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard i...

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...

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...

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 9...

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

Accurately labeling large datasets is important for biomedical machine learning yet challenging whil...

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 se...

Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.

AIM: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients...

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 physiologi...

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...

Predicting Discharge Destination From Inpatient Rehabilitation Using Machine Learning.

Predicting discharge destination for patients at inpatient rehabilitation facilities is important as...

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 consid...

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...

Expectations and Requirements of Surgical Staff for an AI-Supported Clinical Decision Support System for Older Patients: Qualitative Study.

BACKGROUND: Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. ...

Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is cruc...

Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol.

BACKGROUND/OBJECTIVES: Child acute malnutrition is a global public health problem, affecting 45 mill...

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