AIMC Topic: Risk Factors

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Chapter 8: Risk Assessment: Considerations for Coronal Caries.

Monographs in oral science
Caries risk assessment (CRA) is essential to delivering personalized/precision care in caries management. Limited formal evaluation and validation of existing CRA tools affects the ability to accurately predict new lesions. However, this should not p...

Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.

Frontiers in public health
INTRODUCTION: Age-specific risk factors may delay posttraumatic functional recovery; complex interactions exist between these factors. In this study, we investigated the prediction ability of machine learning models for posttraumatic (6 months) funct...

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.

Translational stroke research
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients wit...

Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm.

Clinical orthopaedics and related research
BACKGROUND: The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA) was developed to predict the survival of patients with spinal metastasis. The algorithm was successfully tested in five international institutions using 1101 patie...

Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach.

BMC medical informatics and decision making
BACKGROUND: Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Rec...

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies.

Journal of medical Internet research
BACKGROUND: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse...

Applying artificial intelligence and digital health technologies, Viet Nam.

Bulletin of the World Health Organization
PROBLEM: Direct application of digital health technologies from high-income settings to low- and middle-income countries may be inappropriate due to challenges around data availability, implementation and regulation. Hence different approaches are ne...

Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death.

Biotechnology & genetic engineering reviews
This study aimed to evaluate the potential of deep learning applied to the measurement of echocardiographic data in patients with sudden cardiac death (SCD). 320 SCD patients who met the inclusion and exclusion criteria underwent clinical evaluation,...

Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conv...