AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Hospitalization

Showing 221 to 230 of 463 articles

Clear Filters

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited.

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: The clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes including asymptomatic, hospitalization, intubation, and death. Here we determined the prognostic val...

Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model.

BMC emergency medicine
BACKGROUND: Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. W...

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.

Annals of emergency medicine
STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU a...

A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes.

International journal of radiation oncology, biology, physics
PURPOSE: Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patients are lacking. We developed a machine learning model to identify high-risk patients.

A scalable approach for developing clinical risk prediction applications in different hospitals.

Journal of biomedical informatics
OBJECTIVE: Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few effort...

Harnessing artificial intelligence in cardiac rehabilitation, a systematic review.

Future cardiology
This systematic review aims to evaluate the current body of research surrounding the efficacy of artificial intelligence (AI) in cardiac rehabilitation. Presently, AI can be incorporated into personal devices such as smart watches and smartphones, i...

Identification of pediatric respiratory diseases using a fine-grained diagnosis system.

Journal of biomedical informatics
Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patie...

Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach.

The International journal of eating disorders
OBJECTIVE: Patterns of response to eating disorder (ED) treatment are heterogeneous. Advance knowledge of a patient's expected course may inform precision medicine for ED treatment. This study explored the feasibility of applying machine learning to ...