AIMC Topic: Risk Factors

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Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms.

Advances in skin & wound care
OBJECTIVE: Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate descriptio...

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

Anesthesia and analgesia
BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determin...

The Cost of Robot-assisted Total Hip Arthroplasty: Comparing Safety and Hospital Charges to Conventional Total Hip Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Utilization of robotic assistance is increasing for total hip arthroplasty (THA). However, few studies have directly examined the efficacy of this technique at reducing complications. This research aims to compare the rates of periopera...

Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients.

The journal of trauma and acute care surgery
BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linea...

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.

Cardiovascular research
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances ...

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.

BMJ health & care informatics
Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 20...

Predictors of bleeding event among elderly patients with mechanical valve replacement using random forest model: A retrospective study.

Medicine
Available classification tools and risk factors predicting bleeding events in elderly patients after mechanical valve replacement may not be suitable in Asian populations. Thus, we aimed to identify an accurate model for predicting bleeding in elderl...