AIMC Topic: Decision Support Techniques

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Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

BMC cardiovascular disorders Mar 19, 2025
BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to...
Myocardial Infarction Shock, Cardiogenic Time Factors Hospital Mortality Nomograms Predictive Value of Tests Databases, Factual Prognosis Retrospective Studies Female Reproducibility of Results Risk Factors Aged Male Middle Aged Risk Assessment Humans Machine Learning Decision Support Techniques
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Automated Identification of Stroke Thrombolysis Contraindications from Synthetic Clinical Notes: A Proof-of-Concept Study.

Cerebrovascular diseases extra Mar 17, 2025
INTRODUCTION: Timely thrombolytic therapy improves outcomes in acute ischemic stroke. Manual chart review to screen for thrombolysis contraindications may be time-consuming and prone to errors. We developed and tested a large language model (LLM)-bas...
Fibrinolytic Agents Treatment Outcome Stroke Humans Automation Predictive Value of Tests Decision Support Techniques Proof of Concept Study Electronic Health Records Natural Language Processing Contraindications, Drug Reproducibility of Results Ischemic Stroke Thrombolytic Therapy
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Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.

BMC health services research Mar 15, 2025
OBJECTIVES: Novel non-invasive machine learning algorithms may improve accuracy and reduce the need for biopsy when diagnosing idiopathic pulmonary fibrosis (IPF). We conducted a cost-effectiveness analysis of diagnostic strategies for IPF.
Decision Support Techniques United States Idiopathic Pulmonary Fibrosis Biopsy Cost-Benefit Analysis Quality-Adjusted Life Years Humans Machine Learning Algorithms
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Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Mar 13, 2025
BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guiding treatment decisions and rehabilitation planning. The ability of Machine learning models to proce...
Male Middle Aged Risk Assessment Time Factors Predictive Value of Tests Female Reproducibility of Results Risk Factors Aged Stroke Humans Machine Learning Decision Support Techniques Prognosis
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Do Treatment Choices by Artificial Intelligence Correspond to Reality? Retrospective Comparative Research with Necrotizing Enterocolitis as a Use Case.

Medical decision making : an international journal of the Society for Medical Decision Making Mar 12, 2025
BackgroundIn cases of surgical necrotizing enterocolitis (NEC), the choice between laparotomy (LAP) or comfort care (CC) presents a complex, ethical dilemma. A behavioral artificial intelligence technology (BAIT) decision aid was trained on expert kn...
Laparotomy Enterocolitis, Necrotizing Humans Artificial Intelligence Decision Support Techniques Clinical Decision-Making Retrospective Studies Female Infant, Newborn Male Infant, Premature
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Contrast-Induced Acute Kidney Injury in Lower Limb Percutaneous Transluminal Angioplasty: A Machine Learning Approach for Preoperative Risk Prediction.

Annals of vascular surgery Mar 11, 2025
BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a common complication of lower limb percutaneous transluminal angioplasty (PTA). Common risk models are based on cardiology cohorts for percutaneous coronary intervention. They include a mi...
Acute Kidney Injury Female Reproducibility of Results Risk Factors Aged Humans Machine Learning Decision Support Techniques Retrospective Studies Angioplasty Glomerular Filtration Rate Contrast Media Lower Extremity Peripheral Arterial Disease Male Predictive Value of Tests Middle Aged Risk Assessment Treatment Outcome
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Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning.

HPB : the official journal of the International Hepato Pancreato Biliary Association Mar 4, 2025
BACKGROUND: Despite technical advancements, minimally invasive liver surgery (MILS) for hepatocellular carcinoma (HCC) remains challenging. Nonetheless, effective tools to assess MILS complexity are still lacking. Machine learning (ML) models could i...
Databases, Factual Retrospective Studies Female Risk Factors Aged Treatment Outcome Humans Predictive Value of Tests Machine Learning Minimally Invasive Surgical Procedures Decision Support Techniques Male Tumor Burden Middle Aged Hepatectomy Risk Assessment Liver Neoplasms Carcinoma, Hepatocellular
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Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning.

Cardiovascular diabetology Feb 28, 2025
BACKGROUND: The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular...
Female Cholesterol, HDL Risk Factors Humans Risk Assessment Triglycerides Prognosis Machine Learning Aged Length of Stay Retrospective Studies Male Decision Support Techniques Time Factors Critical Illness Middle Aged Databases, Factual Predictive Value of Tests Intensive Care Units Atherosclerosis Biomarkers
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Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective.

BMC cardiovascular disorders Feb 20, 2025
Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to ach...
Predictive Value of Tests Reproducibility of Results Cardiovascular Diseases Humans Diagnosis, Computer-Assisted Decision Trees Machine Learning Decision Support Techniques
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Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

Cardiovascular diabetology Feb 15, 2025
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...
Biomarkers Retrospective Studies Female Risk Factors Aged Male Middle Aged Risk Assessment Blood Glucose Time Factors Humans Machine Learning Decision Support Techniques Databases, Factual Cardiac Surgical Procedures Hospital Mortality Stress, Physiological Hyperglycemia Treatment Outcome Predictive Value of Tests Cause of Death
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