AIMC Topic: Models, Statistical

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LEAP: Using machine learning to support variant classification in a clinical setting.

Human mutation Apr 1, 2020
Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogeni...
Humans Machine Learning Cardiovascular Diseases Neoplasms Models, Genetic Models, Statistical Area Under Curve Mutation, Missense ROC Curve Genomics Logistic Models
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Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury.

Journal of clinical epidemiology Mar 20, 2020
OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.
Models, Statistical Decision Making, Computer-Assisted Glasgow Coma Scale Prognosis Machine Learning Male Humans Female Logistic Models Algorithms Middle Aged Adult Brain Injuries, Traumatic
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Human respiration monitoring using infrared thermography and artificial intelligence.

Biomedical physics & engineering express Mar 13, 2020
The respiration rate (RR) is the most vital parameter used for the determination of human health. The most widely adopted techniques, used to monitor the RR are contact in nature and face many drawbacks. This paper reports the use of Infrared Thermog...
Male Adolescent Support Vector Machine Monitoring, Physiologic Young Adult Adult Sensitivity and Specificity Body Temperature Respiration Signal-To-Noise Ratio Infrared Rays Models, Statistical Equipment Design Signal Processing, Computer-Assisted Humans Artificial Intelligence Temperature Reproducibility of Results Algorithms Female Respiratory Rate Thermography Stochastic Processes Normal Distribution
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A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI.

Magnetic resonance imaging Mar 12, 2020
BACKGROUND: The classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are heterogeneous and complex. Recently, deep learning methods have been used for b...
Humans Breast Neoplasms Diagnosis, Computer-Assisted Algorithms Female Magnetic Resonance Imaging Deep Learning ROC Curve Image Interpretation, Computer-Assisted Models, Statistical Image Processing, Computer-Assisted
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Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation.

Neural networks : the official journal of the International Neural Network Society Mar 9, 2020
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilis...
Models, Statistical Neurons Humans Brain Neural Networks, Computer Models, Neurological
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Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Scientific reports Mar 9, 2020
Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route...
Female Dairying Animals Animal Husbandry Mastitis, Bovine Infections Cattle Machine Learning Models, Statistical
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One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

Psychological methods Mar 5, 2020
Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed-comb...
Psychology Models, Statistical Factor Analysis, Statistical Data Interpretation, Statistical Humans Machine Learning
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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...
Supervised Machine Learning Ambulatory Care Facilities Pediatrics Time-to-Treatment ROC Curve Patient Satisfaction Models, Statistical Linear Models Humans Ophthalmology Machine Learning Time Factors
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Comparison of Prediction Model Performance Updating Protocols: Using a Data-Driven Testing Procedure to Guide Updating.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
In evolving clinical environments, the accuracy of prediction models deteriorates over time. Guidance on the design of model updating policies is limited, and there is limited exploration of the impact of different policies on future model performanc...
Health Facilities United States Department of Veterans Affairs Patient Admission Hospital Mortality Humans Logistic Models Neural Networks, Computer United States Models, Statistical
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Interpretation of machine learning predictions for patient outcomes in electronic health records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcom...
Male Logistic Models Humans Models, Theoretical Patient Outcome Assessment Machine Learning Models, Statistical Electronic Health Records Female
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