AIMC Topic: Risk Factors

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Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.

Scientific reports
Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the...

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Scientific reports
Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model ...

Children on wheels: Identifying crash determinants using cluster correspondence analysis.

Accident; analysis and prevention
Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes ...

Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.

BMJ health & care informatics
BACKGROUND: Portal vein system thrombosis (PVST) is a common and potentially life-threatening complication following splenectomy plus pericardial devascularisation (SPDV) in patients with cirrhosis and portal hypertension. Early prediction of PVST is...

Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.

BMC medical informatics and decision making
BACKGROUND: The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construc...

Domestic violence and childhood trauma among married women using machine learning approach: a cross-sectional study.

BMC public health
BACKGROUND: Globally, 27% of ever-partnered women aged 15-49 have experienced physical, sexual, or intimate partner violence at least once in their lifetime. In Saudi Arabia, domestic violence (DV) remains a concern despite cultural and economic adva...

Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.

Scientific reports
This study investigated and validated all-cause in-hospital death prediction models for hospitalized pneumonia patients based on large-scale clinical data, including diagnoses, medication prescriptions, and laboratory test codes. Feature selection wa...

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Postoperative acute kidney injury (AKI) is a significant risk associated with surgeries under general anesthesia, often leading to increased mortality and morbidity. Existing predictive models for postoperative AKI are usually limited to ...

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Considering that most patients with low or no significant risk factors can safely undergo noncardiac surgery without additional cardiac evaluation, and given the excessive evaluations often performed in patients undergoing intermediate or...