AIMC Topic: Risk Factors

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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.

Stroke and vascular neurology Dec 30, 2024
BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning met...
Prospective Studies Clinical Decision-Making Female Reproducibility of Results Risk Factors Humans Machine Learning Decision Support Techniques Databases, Factual Time Factors Treatment Outcome Predictive Value of Tests Ischemic Stroke Endovascular Procedures Medical Futility Aged Aged, 80 and over Male Middle Aged Risk Assessment
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Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients.

PloS one Dec 30, 2024
OBJECTIVE: To understand the prevalence and associated risk factors of lower extremity arterial disease (LEAD) in Chinese diabetic patients and to construct a risk prediction model.
Lower Extremity Peripheral Arterial Disease Adult Humans Male Algorithms Middle Aged Female China Risk Factors Logistic Models Aged
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Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.

Medicina (Kaunas, Lithuania) Dec 27, 2024
: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine lear...
Female Humans Risk Factors Machine Learning Aged Prospective Studies Adult Middle Aged Registries Male Risk Assessment Venous Thromboembolism Neoplasms Spain Case-Control Studies Neoplasms, Unknown Primary ROC Curve
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Predicting lack of clinical improvement following varicose vein ablation using machine learning.

Journal of vascular surgery. Venous and lymphatic disorders Dec 26, 2024
OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LC...
Male Middle Aged Risk Assessment Postoperative Complications Time Factors Varicose Veins Treatment Outcome Predictive Value of Tests Ablation Techniques Retrospective Studies Clinical Decision-Making Female Risk Factors Aged Humans Machine Learning Decision Support Techniques Databases, Factual
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Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

BMC cancer Dec 25, 2024
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognosis. At present, majority of the predictive models for the early postoperative recurrence of HCC rely...
Liver Neoplasms Carcinoma, Hepatocellular Aged Neoplasm Recurrence, Local Hepatectomy Adult Proportional Hazards Models Male Retrospective Studies Middle Aged Humans Female ROC Curve Machine Learning Risk Factors Algorithms Prognosis
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Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.

Nutrition (Burbank, Los Angeles County, Calif.) Dec 24, 2024
OBJECTIVES: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the ...
Poverty Humans Growth Disorders Child, Preschool Machine Learning India Health Surveys Female Socioeconomic Factors Risk Factors Family Characteristics Male Infant
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Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.

Journal of cardiovascular translational research Dec 24, 2024
This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-aortic balloon pump (IABP) therapy during cardiac surgery. 236 patients were divided into the normal...
Coronary Artery Bypass Intra-Aortic Balloon Pumping Risk Assessment Length of Stay Time Factors Treatment Outcome Predictive Value of Tests Humans Machine Learning Decision Support Techniques Retrospective Studies Intensive Care Units Aged Male Middle Aged Female Reproducibility of Results Risk Factors
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Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Dec 24, 2024
BACKGROUND: Obstructive sleep apnea (OSA) is common after stroke. Still, routine screening of OSA with polysomnography (PSG) is often unfeasible in clinical practice, primarily because of how limited resources are and the physical condition of patien...
Ischemic Stroke Humans Sleep Apnea, Obstructive Artificial Intelligence Sleep Polysomnography Machine Learning Male Decision Support Techniques Middle Aged Female Risk Assessment Risk Factors Severity of Illness Index Aged Predictive Value of Tests
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A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

BMC pregnancy and childbirth Dec 24, 2024
Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and mana...
Placenta Growth Factor Pregnancy Female Parity Risk Factors Humans Adult Machine Learning Pregnancy Trimester, First Risk Assessment Prospective Studies Pre-Eclampsia Biomarkers Severity of Illness Index Pregnancy Trimester, Second Cohort Studies
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Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

Journal of cardiovascular electrophysiology Dec 23, 2024
INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis co...
Humans Machine Learning Decision Support Techniques Male Middle Aged Risk Assessment Time Factors Treatment Outcome Cluster Analysis Phenotype Female Reproducibility of Results Risk Factors Aged Recurrence Action Potentials Predictive Value of Tests Atrial Fibrillation Heart Rate Catheter Ablation
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