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Time-to-Treatment

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Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium 32308909
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...
Ambulatory Care Facilities Humans Linear Models Machine Learning Models, Statistical Ophthalmology Patient Satisfaction Pediatrics ROC Curve Supervised Machine Learning Time Factors Time-to-Treatment
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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 30733186
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.
Aged Colon Colonoscopy Colorectal Neoplasms Early Detection of Cancer Feces Female Hemoglobins Humans Logistic Models Male Middle Aged Multivariate Analysis Neoplasm Staging Occult Blood Republic of Korea Risk Assessment Time-to-Treatment
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Automated video surveillance and machine learning: Leveraging existing infrastructure for cardiac arrest detection and emergency response activation.

Resuscitation 29474880
Emergency Medical Services Humans Machine Learning Out-of-Hospital Cardiac Arrest Time-to-Treatment Video Recording
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Critical care medicine 29286945
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-ti...
Academic Medical Centers Age Factors Aged Blood Pressure Comorbidity Critical Illness Decision Support Systems, Clinical Electrocardiography Electronic Health Records Female Heart Rate Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Organ Dysfunction Scores ROC Curve Sepsis Severity of Illness Index Sex Factors Socioeconomic Factors Time Factors Time-to-Treatment Vital Signs
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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 30691352
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.
Aged Algorithms Carcinoma, Squamous Cell Cohort Studies Databases, Factual Female Head and Neck Neoplasms Humans Machine Learning Male Middle Aged Neoplasm Staging Predictive Value of Tests Radiotherapy, Adjuvant Socioeconomic Factors Time-to-Treatment United States
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Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

JCO clinical cancer informatics 31112417
PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can ...
Adult Aged Cluster Analysis Female Follow-Up Studies Humans Kaplan-Meier Estimate Leukemia, Lymphocytic, Chronic, B-Cell Machine Learning Male Middle Aged Models, Theoretical Prognosis Reproducibility of Results Risk Factors Time-to-Treatment Treatment Outcome
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Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway.

BMJ open 31530590
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.
Brain Ischemia Clinical Audit Computer Simulation England Fibrinolytic Agents Hospitals Humans Machine Learning Stroke Thrombolytic Therapy Time Factors Time-to-Treatment Tissue Plasminogen Activator
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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

PloS one 32502209
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...
Clinical Decision-Making Comorbidity Datasets as Topic Disease Progression Humans Machine Learning Models, Biological Renal Insufficiency, Chronic Renal Replacement Therapy Retrospective Studies ROC Curve Severity of Illness Index Taiwan Time Factors Time-to-Treatment
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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 32586339
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...
Age Factors Aged Algorithms Cardiopulmonary Resuscitation Emergency Medical Services Female Heart Rate Humans Machine Learning Male Out-of-Hospital Cardiac Arrest Survival Analysis Sweden Time-to-Treatment
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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) 32568616
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...
Algorithms Deep Learning Female Femur Humans Intestines Neural Networks, Computer Organs at Risk Pelvis Rectum Spinal Cord Time-to-Treatment Urinary Bladder Uterine Cervical Neoplasms Workflow
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