Hospital-Based Medicine

Latest AI and machine learning research in hospital-based medicine for healthcare professionals.

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Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the oper...

Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes.

This study introduces a novel approach for generating machine-generated instruction datasets for fin...

Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies.

Developing novel predictive models with complex biomedical information is challenging due to various...

Predicting Patient No-Shows: Situated Machine Learning with Imperfect Data.

Patients who do not show up for scheduled appointments are a considerable cost and concern in health...

Development of a Nursing Diagnosis/Record Generative AI System Based on Virtual Patient Data.

This study investigates how to reduce nurses' repetitive electronic nursing record tasks. We applied...

The Application Artificial Intelligence-Assisted Robot System in Nursing Follow-up of Discharged Patients.

To construct a robot intelligent discharge follow-up platform and explore its application effects in...

Development of an Intelligent Health Education System Based on Large Language Model for Elderly Pulmonary Aspiration Prevention.

As the aging process accelerates, the incidence of chronic diseases in the elderly is rising. As a r...

Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning.

Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur ...

Evaluating LLMs for Diagnosis Summarization.

During a patient's hospitalization, extensive information is documented in clinical notes. The effic...

LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection.

Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but...

Detection of hospital super bacteria MRSA from the hands of healthcare professionals using machine learning and hyperspectral imaging.

Healthcare-associated infections resulting from cross-contamination, particularly from the hands of ...

Assessment of Knowledge, Practice, Perception, and Expectations of Artificial Intelligence in Medical Care among Staff of a Tertiary Hospital.

BACKGROUND: Artificial intelligence (AI) is the simulation of human intelligence processes by machin...

[Translated article] Introducing artificial intelligence to hospital pharmacy departments.

Artificial intelligence is a broad concept that includes the study of the ability of computers to pe...

Approaching artificial intelligence to Hospital Pharmacy.

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers ...

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Ass...

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-int...

The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review.

The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) com...

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