Hospital-Based Medicine

Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier.

CYP3A4 is the main human enzyme responsible for phase I metabolism of dietary compounds, prescribed drugs and xenobiotics, steroid hormones, and bile acids. The inhibition of CYP3A4 activity might impair physiological mechanisms, including the endocrine system and response to drug admission. Here, we aimed to discover new CYP3A4 inhibitors from food and dietary supplements. A deep-learning model w...

Effects of Balance Exercise Assist Robot training for patients with hemiparetic stroke: a randomized controlled trial.

BACKGROUND: Robot-assisted rehabilitation for patients with stroke is promising. However, it is unclear whether additional balance training using a balance-focused robot combined with conventional rehabilitation programs supplements the balance function in patients with stroke. The purpose of this study was to compare the effects of Balance Exercise Assist Robot (BEAR) training combined with conve...

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.

The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (IC...

Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a signific...

Developing basic robotic skills using virtual reality simulation and automated assessment tools: a multidisciplinary robotic virtual reality-based curriculum using the Da Vinci Skills Simulator and tracking progress with the Intuitive Learning platform.

This study sought to develop basic robotic surgical skills among surgical trainees across multiple specialties using a VR-based curriculum and provide...

Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study.

Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can fac...

Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.

The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with como...

Robot-assisted versus video-assisted thoraco-laparoscopic McKeown's esophagectomy for esophageal cancer: a propensity score-matched analysis of minimally invasive approaches.

Minimally invasive esophagectomy for esophageal cancer decreases overall complication rate and leads to faster postoperative recovery. Robot-assisted ...

Hospital Ownership, Geographic Region, Patient Age, Comorbidities, and Insurance Status Appear to Influence Patient Selection Robot-Assisted Ureteral Reimplantation for Benign Disease: A Population-Based Analysis.

Robot-assisted ureteral reimplantation (RAUR) is a relatively new minimally invasive procedure. As such, research is lacking, and the largest adult c...

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data.

The ability to perform accurate prognosis is crucial for proactive clinical decision making, informed resource management and personalised care. Exist...

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death...

Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

BACKGROUND: Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Prev...

Machine Learning-Based Mortality Prediction of Patients at Risk During Hospital Admission.

OBJECTIVES: The ability to predict in-hospital mortality from data available at hospital admission would identify patients at risk and thereby assist ...

Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example.

BACKGROUND: There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment ...

Single-Port Retzius-Sparing Robot-Assisted Radical Prostatectomy: Feasibility and Early Outcomes.

To determine safety and feasibility of single-port Retzius-sparing robot-assisted radical prostatectomy (SP-rsRARP) using the da Vinci SP (Intuitive ...

An explainable machine learning framework for lung cancer hospital length of stay prediction.

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed t...

[Artificial intelligence for medical information departments : construction and evaluation of a decision-making tool to identify and prioritize stays of which the PMSI coding could be optimized, and to ensure the revenues generated by activity-based pricing].

BACKGROUND: Medical Information Departments help to optimize the hospital revenues generated by activity-based pricing. A review of medical files, sel...

Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020.

In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more atte...

Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.

Recent advances in whole-slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence-based d...

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network.

Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the pr...