AIMC Topic: Risk Assessment

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Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.

The Lancet. Digital health
BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk ...

Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

The Journal of surgical research
BACKGROUND: Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predi...

Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.

JAMA network open
IMPORTANCE: Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. However, the atypical presentation of sepsis within this population makes detection challenging, and exi...

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.

Respiratory research
BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pn...

Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

The Journal of surgical research
BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who...

Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: While the prevalence of active cancer patients experiencing acute stroke is increasing, the effects of active cancer on reperfusion therapy outcomes are inconclusive. Thus, we aimed to compare the safety and outcomes of reperfusion therap...

Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival.

Scientific reports
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical met...

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.

Scientific reports
To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely dur...