Hospital-Based Medicine

Machine learning in diagnostic support in medical emergency departments.

Diagnosing patients in the medical emergency department is complex and this is expected to increase in many countries due to an ageing population. In this study we investigate the feasibility of training machine learning algorithms to assist physicians handling the complex situation in the medical emergency departments. This is expected to reduce diagnostic errors and improve patient logistics and...

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients.

Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization.

INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models d...

A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Exist...

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI)...

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transport...

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly po...

Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to ...

Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study.

PURPOSE/OBJECTIVES: Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict th...

Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.

BACKGROUND: Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' l...

Predicting hospital length of stay using machine learning on a large open health dataset.

BACKGROUND: Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making proces...

Role of artificial intelligence in critical care nutrition support and research.

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of...

Additional Rehabilitative Robot-Assisted Gait Training for Ambulation in Geriatric Individuals with Guillain-Barré Syndrome: A Case Report.

We present a case of a 75-year-old Asian woman with Guillain-Barré syndrome (GBS) who underwent a 1-month comprehensive rehabilitation training progra...

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).

BACKGROUND: Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgica...

Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments.

Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to th...

Ethics and responsibility in biohybrid robotics research.

The industrial revolution of the 19th century marked the onset of an era of machines and robots that transformed societies. Since the beginning of the...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalation...

A systematic literature review of predicting patient discharges using statistical methods and machine learning.

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital...

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.

INTRODUCTION: Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routi...

Machine learning analysis of lab tests to predict bariatric readmissions.

The purpose of this study was to develop a machine learning model for predicting 30-day readmission after bariatric surgery based on laboratory tests....