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Risk Assessment

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Artificial Intelligence and Machine Learning in Preeclampsia.

Arteriosclerosis, thrombosis, and vascular biology
Preeclampsia is a multisystem hypertensive disorder that manifests itself after 20 weeks of pregnancy, along with proteinuria. The pathophysiology of preeclampsia is incompletely understood. Artificial intelligence, especially machine learning with i...

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Rheumatology international
Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascul...

Analyzing Secondary Cancer Risk: A Machine Learning Approach.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors incl...

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

International journal of surgery (London, England)
BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patien...

Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.

JMIR medical informatics
BACKGROUND: Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding ...

Machine Learning Prediction for Postdischarge Falls in Older Adults: A Multicenter Prospective Study.

Journal of the American Medical Directors Association
OBJECTIVES: The study aimed to develop a machine learning (ML) model to predict early postdischarge falls in older adults using data that are easy to collect in acute care hospitals. This may reduce the burden imposed by complex measures on patients ...

Development of risk models for early detection and prediction of chronic kidney disease in clinical settings.

Scientific reports
Chronic kidney disease (CKD) imposes a high burden with high mortality and morbidity rates. Early detection of CKD is imperative in preventing the adverse outcomes attributed to the later stages. Therefore, this study aims to utilize machine learning...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Journal of medical systems
Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention...

Development and validation of a web-based calculator for determining the risk of psychological distress based on machine learning algorithms: A cross-sectional study of 342 lung cancer patients.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Early and accurate identification of the risk of psychological distress allows for timely intervention and improved prognosis. Current methods for predicting psychological distress among lung cancer patients using readily available data are ...