Hospital-Based Medicine

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.

OBJECTIVE: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.

The trauma severity model: An ensemble machine learning approach to risk prediction.

Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant for mortality risk prediction with trauma patients, as researchers have focused exclusively on the use of generalized linear models for trauma risk prediction, and generalized linear models may be too inflexible to ca...

Frontiers of Medical Robotics: From Concept to Systems to Clinical Translation.

Medical robotics is poised to transform all aspects of medicine-from surgical intervention to targeted therapy, rehabilitation, and hospital automatio...

Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions.

IMPORTANCE: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying pati...

[Data-driven integrated diagnostics: the natural evolution of clinical chemistry?].

In the near future, making a correct medical diagnosis will be increasingly supported by artificial intelligence. The development of algorithms that i...

Robotic Cardiac Surgery Part I: Anesthetic Considerations in Totally Endoscopic Robotic Cardiac Surgery (TERCS).

The application and evolution of total endoscopic robotic cardiac surgery (TERCS) has become greater as institutions and surgeons become more comforta...

Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.

AIMS: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting event...

A motor learning therapeutic intervention for a child with cerebral palsy through a social assistive robot.

Children with cerebral palsy have difficulty to sit, stand, walk, run and jump independently. Therapy is an important factor in improving these aspec...

Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data.

Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characteri...

Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures.

Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and internal fixation (ORIF). Several patient-...

MCN: A comprehensive corpus for medical concept normalization.

Normalization of clinical text involves linking different ways of talking about the same clinical concept to the same term in the standardized vocabul...

DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning.

Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-pr...

Clinical applications of control systems models: The neural integrators for eye movements.

The first models that were proposed to account for the neural control of eye movements applied a classic control systems approach, including feedback,...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly p...

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering cl...

Predictors of in-hospital length of stay among cardiac patients: A machine learning approach.

OBJECTIVE: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This ...

Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports.

While ballast water has long been linked to the global transport of invasive species, little is known about its microbiome. Herein, we used 16S rRNA g...

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia.

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 h...

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting...

Rehabilitation of a patient with amelogenesis imperfecta and severe open bite: A multidisciplinary approach.

Amelogenesis imperfecta (AI) is typically associated with anterior open bite and a number of other dental problems, which require complex treatments s...