AIMC Topic: Predictive Value of Tests

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What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Clinical orthopaedics and related research Oct 1, 2020
BACKGROUND: Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder ...
Minimal Clinically Important Difference Arthroplasty, Replacement, Shoulder Humans Machine Learning Female Aged Male Middle Aged Treatment Outcome Predictive Value of Tests Pain Measurement Range of Motion, Articular
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Development and validation of a machine-learning model for prediction of shoulder dystocia.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology Oct 1, 2020
OBJECTIVES: To develop a machine-learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model's predictive accuracy and potential clinical efficacy in optimizing the use of Cesarean delivery in the context of sus...
Female Cesarean Section Reproducibility of Results Risk Factors Adult Diabetes, Gestational Humans Ultrasonography, Prenatal Gestational Age ROC Curve Machine Learning Biometry Predictive Value of Tests Pregnancy Patient Selection Israel Fetal Weight Fetal Macrosomia Shoulder Dystocia
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Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI).

Pakistan journal of pharmaceutical sciences Sep 1, 2020
This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intelligence based multilayer perceptron (MLP). 300 cases of diabetes mellitus type-2, of age between 18-80 ...
Pakistan Restless Legs Syndrome Adult Prevalence Humans Female Cross-Sectional Studies Adolescent Neural Networks, Computer Risk Factors Male Incidence Artificial Intelligence Middle Aged Diabetes Mellitus, Type 2 Algorithms Risk Assessment Predictive Value of Tests Decision Support Techniques Aged Aged, 80 and over Young Adult
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Machine Learning To Predict Serious Bacterial Infections in Young Febrile Infants.

Pediatrics Sep 1, 2020
BACKGROUND: Recent decision rules for the management of febrile infants support the identification of infants at higher risk of serious bacterial infections (SBIs) without the performance of routine lumbar puncture. We derive and validate a model to ...
Bacterial Infections Predictive Value of Tests Fever Prospective Studies Humans Female Machine Learning Infant, Newborn Male Infant
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Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?

Clinical orthopaedics and related research Sep 1, 2020
BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As the demand for TKA rises, reducing the risk of revision TKA is becoming increasingly important. Predictive tools based on machine-learning algorithm...
Preoperative Period Osteoarthritis, Knee Knee Injuries Predictive Value of Tests Registries Body Weight Arthroplasty, Replacement, Knee Reoperation Middle Aged Risk Assessment Postoperative Complications Age Factors Denmark Humans Algorithms Aged Machine Learning Female Adult Risk Factors Male
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Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

The journal of ECT Sep 1, 2020
OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach.
Depressive Disorder, Major Magnetic Resonance Imaging Predictive Value of Tests Remission Induction Electroconvulsive Therapy Humans Machine Learning Brain Female Aged Male Middle Aged
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New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF.

European heart journal. Cardiovascular pharmacotherapy Sep 1, 2020
AIMS: Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from ...
Humans Neural Networks, Computer Databases, Factual Prospective Studies Female Risk Assessment Time Factors Treatment Outcome Predictive Value of Tests Anticoagulants Blood Coagulation Reproducibility of Results Risk Factors Aged Aged, 80 and over Male Atrial Fibrillation Vitamin K Hemorrhage Prothrombin Time International Normalized Ratio Drug Therapy, Computer-Assisted Drug Monitoring Registries Stroke Administration, Oral
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Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

Journal of the American College of Cardiology Aug 25, 2020
BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.
Myocardial Contraction Predictive Value of Tests Signal Processing, Computer-Assisted Ventricular Dysfunction, Left Female Early Diagnosis Humans Echocardiography Male Heart Failure, Diastolic Machine Learning Middle Aged Stroke Volume ROC Curve
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Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients With Liver Disease.

The American journal of gastroenterology Aug 1, 2020
INTRODUCTION: There is increasing recognition of the central role of muscle mass in predicting clinical outcomes in patients with liver disease. Muscle size can be extracted from computed tomography (CT) scans, but clinical implementation will requir...
Female Aged Young Adult Adult Male Middle Aged Deep Learning Adolescent Cohort Studies Muscle, Skeletal Humans Algorithms Prospective Studies Predictive Value of Tests Tomography, X-Ray Computed Fibrosis Michigan
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Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model.

The journal of trauma and acute care surgery Jul 1, 2020
BACKGROUND: Computed tomography is the criterion standard for diagnosing intra-abdominal injury (IAI) but is expensive and risks radiation exposure. The Pediatric Emergency Care Applied Research Network (PECARN) model identifies children at low risk ...
Female Risk Factors Male Risk Assessment Adolescent Humans Machine Learning Wounds, Nonpenetrating Child Predictive Value of Tests Abdominal Injuries
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