AIMC Topic: Decision Support Techniques

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Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.

PloS one Mar 25, 2025
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their develo...
Environmental Monitoring Carbon Conservation of Natural Resources Neural Networks, Computer Petroleum Algorithms Decision Support Techniques Deep Learning
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Optimizing heart disease diagnosis with advanced machine learning models: a comparison of predictive performance.

BMC cardiovascular disorders Mar 22, 2025
Cardiovascular disease is the leading cause of mortality globally, necessitating precise and prompt predictive instruments to enhance patient outcomes. In recent years, machine learning methodologies have demonstrated significant potential in enhanci...
Diagnosis, Computer-Assisted Predictive Value of Tests Heart Diseases Humans Reproducibility of Results Machine Learning Male Aged Middle Aged Risk Assessment Decision Support Techniques Databases, Factual Prognosis Female
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A decision-making framework for evaluating medical equipment suppliers under uncertainty.

Scientific reports Mar 21, 2025
The procurement of medical equipment is a critical concern for healthcare organizations striving to deliver comprehensive patient care. Thus, the procurement process, including performance evaluation and selection of medical equipment suppliers, pose...
Uncertainty Decision Support Techniques Fuzzy Logic Decision Making Equipment and Supplies Humans
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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...
Humans Machine Learning Decision Support Techniques Databases, Factual Prognosis Retrospective Studies Female Nomograms Middle Aged Predictive Value of Tests Reproducibility of Results Risk Assessment Myocardial Infarction Risk Factors Time Factors Shock, Cardiogenic Hospital Mortality Aged Male
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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...
Proof of Concept Study Humans Contraindications, Drug Thrombolytic Therapy Treatment Outcome Decision Support Techniques Fibrinolytic Agents Predictive Value of Tests Stroke Electronic Health Records Natural Language Processing Reproducibility of Results Automation Ischemic Stroke
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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.
Idiopathic Pulmonary Fibrosis Algorithms Biopsy Decision Support Techniques United States Cost-Benefit Analysis Humans Quality-Adjusted Life Years Machine Learning
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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...
Predictive Value of Tests Stroke Female Reproducibility of Results Risk Factors Aged Male Middle Aged Risk Assessment Time Factors 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...
Clinical Decision-Making Female Infant, Newborn Male Humans Infant, Premature Artificial Intelligence Laparotomy Decision Support Techniques Enterocolitis, Necrotizing Retrospective Studies
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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...
Decision Support Techniques Retrospective Studies Acute Kidney Injury Female Humans Machine Learning Middle Aged Risk Assessment Treatment Outcome Glomerular Filtration Rate Contrast Media Reproducibility of Results Risk Factors Aged Male Lower Extremity Peripheral Arterial Disease Predictive Value of Tests Angioplasty
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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...
Female Risk Factors Aged Male Machine Learning Decision Support Techniques Databases, Factual Retrospective Studies Treatment Outcome Predictive Value of Tests Minimally Invasive Surgical Procedures Tumor Burden Hepatectomy Middle Aged Risk Assessment Liver Neoplasms Carcinoma, Hepatocellular Humans
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