AIMC Topic: Time Factors

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Understanding EMS response times: a machine learning-based analysis.

BMC medical informatics and decision making Mar 24, 2025
BACKGROUND: Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning...
Sweden Time Factors Emergency Medical Services Humans Machine Learning Retrospective Studies
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Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Computational biology and chemistry Mar 21, 2025
Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DT...
MCF-7 Cells Humans Artificial Intelligence Machine Learning Gene Expression Profiling Transcriptome Time Factors
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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...
Prognosis Retrospective Studies Female Reproducibility of Results Risk Factors Aged Male Middle Aged Predictive Value of Tests Humans Myocardial Infarction Shock, Cardiogenic Risk Assessment Machine Learning Time Factors Decision Support Techniques Hospital Mortality Databases, Factual Nomograms
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Interpretable machine learning models for prolonged Emergency Department wait time prediction.

BMC health services research Mar 18, 2025
OBJECTIVE: Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of M...
Triage Emergency Service, Hospital Waiting Lists Machine Learning Retrospective Studies Female Aged Adult Male Middle Aged Time Factors Humans
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Reduction of Acquisition Time in Fourier Transform Infrared Spectral Imaging by Deep Learning for Clinical Applications.

Analytical chemistry Mar 17, 2025
In infrared Fourier transform spectral imaging applied to biomedical challenges, data quality is of primary importance to achieving clinical objectives. However, different noise sources affect the infrared signal coming from the sample. Generally, th...
Spectroscopy, Fourier Transform Infrared Signal-To-Noise Ratio Humans Deep Learning Time Factors Image Processing, Computer-Assisted
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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...
Risk Assessment Time Factors Predictive Value of Tests Stroke Risk Factors Aged Male Middle Aged Humans Machine Learning Decision Support Techniques Prognosis Female Reproducibility of Results
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Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.

Accident; analysis and prevention Mar 4, 2025
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...
Time Factors Models, Theoretical Environment Design Pedestrians Humans Algorithms Computer Simulation Deep Learning Avoidance Learning Unmanned Aerial Devices Accidents, Traffic Safety Reinforcement Machine Learning
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Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty - a development and validation study.

BMC medical informatics and decision making Mar 3, 2025
BACKGROUND: Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patient...
Retrospective Studies Female Aged Male Middle Aged Time Factors Humans Arthroplasty, Replacement, Knee Machine Learning Arthroplasty, Replacement, Hip
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Does the alternating timing of rTMS combined with soft-hand rehabilitation robot affect the recovery of hand function in patients after stroke? A study protocol for a multicentre randomised controlled trial.

BMJ open Mar 3, 2025
INTRODUCTION: Combining repetitive transcranial magnetic stimulation (rTMS) with robotic training could result in more significant improvements in motor function than either treatment alone. The efficacy of this combination may depend on the sequenci...
Male Middle Aged Time Factors Multicenter Studies as Topic Humans Hand Female Stroke Aged Stroke Rehabilitation Adult Transcranial Magnetic Stimulation Recovery of Function Randomized Controlled Trials as Topic Robotics Single-Blind Method
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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...
Cholesterol, HDL Triglycerides Length of Stay Time Factors Predictive Value of Tests Databases, Factual Critical Illness Atherosclerosis Biomarkers Intensive Care Units Humans Prognosis Female Machine Learning Retrospective Studies Aged Risk Factors Decision Support Techniques Male Middle Aged Risk Assessment
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