Hospital-Based Medicine

Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices.

PURPOSE: The cancer research community is constantly evolving to better understand tumor biology, di...

Real-time finger force prediction via parallel convolutional neural networks: a preliminary study.

Continuous and accurate decoding of intended motions is critical for human-machine interactions. Her...

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.

With the development of advanced robotic hands, a reliable neural-machine interface is essential to ...

Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach.

Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to...

Comparison of Machine Learning Algorithms for Classifying Adverse-Event Related 30-Day Hospital Readmissions: Potential Implications for Patient Safety.

Studies in the last decade have focused on identifying patients at risk of readmission using predict...

Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients.

Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clini...

Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compa...

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it ha...

Association rule mining for the ordered placement of traditional Chinese medicine containers: An experimental study.

In traditional Chinese medicine (TCM) clinics, the pharmacists responsible for dispensing the herbal...

Discussion on: The paradox of the robotic approach to inguinal hernia repair in the inpatient setting.

Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with ...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack co...

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of majo...

Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations.

BACKGROUND: All patients admitted to an acute inpatient mental health unit must have nursing observa...

Evaluation of clinical impact of pharmacogenomics knowledge involved in CPIC guidelines on Chinese pediatric patients.

To evaluate the clinical benefits of implementing pharmacogenomics testing for Chinese pediatric pa...

Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness.

The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild...

Postimplementation Evaluation of a Machine Learning-Based Deterioration Risk Alert to Enhance Sepsis Outcome Improvements.

Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accuratel...

Laparoscopic versus Robotic Peripheral Pancreatectomy: A Systematic Review and Meta-analysis.

PURPOSE: The current systematic review and meta-analysis aimed to compare Laparoscopic Distal Pancre...

Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning.

OBJECTIVE: Detecting adverse drug events (ADEs) and medications related information in clinical note...

A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.

Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presen...

Response to Comment on "Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission".