Hospital-Based Medicine

Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models.

Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron neural network (MLPNN), support vector machine (SVM), multivariate adaptive regression splines (MARS), genetic programming (GP), M5 algorithm, and grou...

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantive...

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data.

The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time...

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.

Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients wi...

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach.

The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the dep...

A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are...

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocatio...

A Multifunctional Smart Meter Using ANN-PSO Flux Estimation and Harmonic Active Compensation with Fuzzy Voltage Regulation.

This paper aims to present the analysis and development of a complete electronic smart meter that is able to perform four-quadrant measurements, act a...

Severe intraoperative bleeding predicts the risk of perioperative blood transfusion after robot-assisted radical prostatectomy.

To evaluate potential factors associated with the risk of perioperative blood transfusion (PBT) with implications on length of hospital stay (LOHS) an...

The future of basic science in orthopaedics and traumatology: Cassandra or Prometheus?

Orthopaedic and trauma research is a gateway to better health and mobility, reflecting the ever-increasing and complex burden of musculoskeletal disea...

Commentary: When will the robots come marching in?

Minimally invasive techniques for coronary artery bypass grafting (CABG), specifically robotic-assisted CABG has increased in popularity despite confl...

Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.

The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide...

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care un...

Gaining Insights Into Patient Satisfaction Through Interpretable Machine Learning.

Patient satisfaction is a key performance indicator of patient-centered care and hospital reimbursement. To discover the major factors that affect pat...

Improving hospital readmission prediction using individualized utility analysis.

OBJECTIVE: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative a...

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

BACKGROUND: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great...

Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".

Authors' Reply to: Minimizing Selection and Classification Biases Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".

Economic and utilization outcomes of medication management at a large Medicaid plan with disease management pharmacists using a novel artificial intelligence platform from 2018 to 2019: a retrospective observational study using regression methods.

Medication therapy management (MTM) and comprehensive medication management (CMM) have been practiced by clinical pharmacists as a predominantly manu...

Prediction of Bedridden Duration of Hospitalized Patients by Machine Learning Based on EMRs at Admission.

Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedr...