AIMC Topic: Risk Factors

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Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

European heart journal. Acute cardiovascular care
AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock ...

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

World journal of gastroenterology
BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical d...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.

Comparing the accuracy of four machine learning models in predicting type 2 diabetes onset within the Chinese population: a retrospective study.

The Journal of international medical research
OBJECTIVE: To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese population by retrospectively analyzing annual health checkup records.

Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.

Cancer medicine
OBJECTIVE: We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.

Unraveling Endometrial Cancer Survival Predictors Through Advanced Machine Learning Techniques.

Studies in health technology and informatics
This study explores endometrial cancer (EC) within the broader context of oncogynecology, focusing on 3,845 EC patients at the Almazov National Research Center. The research analyzes clinical data, employing machine learning techniques like random fo...

Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies.

Studies in health technology and informatics
Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acquired factors. Current diagnostic paradigms, primarily relying on a combination of clinical assessme...

Biomarker signatures associated with ageing free of major chronic diseases: results from a population-based sample of the EPIC-Potsdam cohort.

Age and ageing
BACKGROUND: A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic dise...