AIMC Topic: Risk Factors

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[Machine learning and suicide prevention: is an algorithm the solution?].

Nederlands tijdschrift voor geneeskunde
Suicide is inherently difficult to predict. Epidemiological research identified many general risk factors such as a depression, but these predictors have limited predictive power. Machine learning offers a set of tools that can combine hundreds of pr...

Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification.

Computational and mathematical methods in medicine
For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature ...

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.

American journal of reproductive immunology (New York, N.Y. : 1989)
PROBLEM: Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various s...

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...

Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer.

Cancer science
Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E-stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TC...

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

BMC endocrine disorders
INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and v...

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

BMC infectious diseases
BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how...

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

Theranostics
The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. We investigated a unique cohort of peri-implantitis patie...

Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach.

World journal for pediatric & congenital heart surgery
OBJECTIVE: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence technique...