AIMC Topic: Time-to-Treatment

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Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients.

Cerebrovascular diseases (Basel, Switzerland) Apr 13, 2021
BACKGROUND AND PURPOSE: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent whe...
Aged Aged, 80 and over Male Middle Aged Diagnosis, Computer-Assisted Decision Support Techniques Databases, Factual Retrospective Studies Clinical Decision-Making Female Time-to-Treatment Endovascular Procedures Computed Tomography Angiography Cerebral Angiography Delivery of Health Care, Integrated Predictive Value of Tests Triage Ischemic Stroke Radiographic Image Interpretation, Computer-Assisted Workflow Humans Artificial Intelligence
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Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BMC medical informatics and decision making Oct 27, 2020
BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse...
Severity of Illness Index Sepsis Time Factors Hospital Mortality Decision Support Systems, Clinical Predictive Value of Tests Datasets as Topic Time-to-Treatment Retrospective Studies Intensive Care Units Female Reproducibility of Results Male Humans Machine Learning Algorithms Forecasting
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Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model.

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences Aug 26, 2020
BACKGROUND: Recently approved artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams....
Humans Artificial Intelligence Software Workflow Stroke Time-to-Treatment Computed Tomography Angiography Cerebral Angiography Retrospective Studies Female Aged Male Length of Stay
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Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study.

Scandinavian journal of trauma, resuscitation and emergency medicine Jun 25, 2020
INTRODUCTION: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and wheth...
Cardiopulmonary Resuscitation Out-of-Hospital Cardiac Arrest Age Factors Emergency Medical Services Heart Rate Survival Analysis Time-to-Treatment Female Sweden Aged Male Humans Machine Learning Algorithms
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Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples.

Acta oncologica (Stockholm, Sweden) Jun 22, 2020
Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decrease the treatment wait time. We developed and evaluated the use of a fused model Dense V-Network for its abil...
Femur Urinary Bladder Rectum Workflow Organs at Risk Spinal Cord Time-to-Treatment Humans Neural Networks, Computer Algorithms Female Uterine Cervical Neoplasms Deep Learning Intestines Pelvis
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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

PloS one Jun 5, 2020
Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In thi...
Time Factors ROC Curve Renal Insufficiency, Chronic Taiwan Models, Biological Datasets as Topic Time-to-Treatment Renal Replacement Therapy Clinical Decision-Making Disease Progression Comorbidity Severity of Illness Index Humans Machine Learning Retrospective Studies
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Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times could improve patients' satisfaction by reducing uncertainty. However, these are rarely known about effi...
Patient Satisfaction Linear Models Time-to-Treatment Models, Statistical Ophthalmology Supervised Machine Learning Ambulatory Care Facilities Pediatrics Humans Machine Learning Time Factors ROC Curve
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Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway.

BMJ open Sep 17, 2019
OBJECTIVE: To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.
Machine Learning Computer Simulation Hospitals England Time Factors Thrombolytic Therapy Fibrinolytic Agents Stroke Brain Ischemia Tissue Plasminogen Activator Humans Time-to-Treatment Clinical Audit
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Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Jan 29, 2019
OBJECTIVE: To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation.
Predictive Value of Tests Neoplasm Staging Time-to-Treatment Radiotherapy, Adjuvant Cohort Studies United States Socioeconomic Factors Head and Neck Neoplasms Female Carcinoma, Squamous Cell Aged Male Middle Aged Humans Machine Learning Algorithms Databases, Factual
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Association of time to colonoscopy after a positive fecal test result and fecal hemoglobin concentration with risk of advanced colorectal neoplasia.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver Dec 23, 2018
BACKGROUND: We evaluated the risk of advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) according to time to colonoscopy after positive fecal immunochemical test (FIT), fecal hemoglobin concentration, and combination of both.
Early Detection of Cancer Neoplasm Staging Hemoglobins Time-to-Treatment Multivariate Analysis Risk Assessment Colon Colonoscopy Feces Logistic Models Occult Blood Humans Republic of Korea Female Aged Colorectal Neoplasms Male Middle Aged
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