AIMC Topic: Logistic Models

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Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

Circulation. Cardiovascular quality and outcomes Nov 8, 2016
BACKGROUND: Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly ...
Massachusetts Middle Aged Process Assessment, Health Care Hospitals Time Factors Treatment Outcome Hospital Mortality Cause of Death Logistic Models Registries Quality Indicators, Health Care Percutaneous Coronary Intervention Female Multivariate Analysis Data Mining Risk Factors Propensity Score Aged Likelihood Functions Aged, 80 and over Humans Male Machine Learning Algorithms Databases, Factual
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Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

Circulation. Cardiovascular quality and outcomes Nov 8, 2016
BACKGROUND: Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility.
Early Diagnosis Pennsylvania Predictive Value of Tests Area Under Curve Heart Failure Humans Case-Control Studies Electronic Health Records Data Mining Machine Learning Incidence Female Prognosis Time Factors Risk Factors ROC Curve Male Logistic Models Risk Assessment
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Analysis of Machine Learning Techniques for Heart Failure Readmissions.

Circulation. Cardiovascular quality and outcomes Nov 8, 2016
BACKGROUND: The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance predict...
Female Reproducibility of Results Risk Factors Aged Male Middle Aged Risk Assessment Time Factors Logistic Models Support Vector Machine Humans Algorithms Databases, Factual Nonlinear Dynamics Randomized Controlled Trials as Topic Heart Failure Data Mining Telemedicine Patient Readmission
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Identifying Drug-Drug Interactions by Data Mining: A Pilot Study of Warfarin-Associated Drug Interactions.

Circulation. Cardiovascular quality and outcomes Nov 8, 2016
BACKGROUND: Knowledge about drug-drug interactions commonly arises from preclinical trials, from adverse drug reports, or based on knowledge of mechanisms of action. Our aim was to investigate whether drug-drug interactions were discoverable without ...
Atrial Fibrillation Drug Prescriptions International Normalized Ratio Administrative Claims, Healthcare Polypharmacy Risk Factors Registries Humans Aged Drug Interactions Logistic Models Machine Learning Aged, 80 and over Data Mining Predictive Value of Tests Retrospective Studies Female Male Anticoagulants Pilot Projects Blood Coagulation Warfarin
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Incidence of severe renal dysfunction among individuals taking warfarin and implications for non-vitamin K oral anticoagulants.

American heart journal Nov 4, 2016
BACKGROUND: The purpose of this study is to assess incidence and risk factors for severe renal dysfunction in patients requiring oral anticoagulation to help guide initial drug choice and provide a rational basis for interval monitoring of renal func...
Creatinine Atrial Fibrillation Multivariate Analysis Odds Ratio Kaplan-Meier Estimate Anticoagulants Warfarin Coronary Artery Disease Stroke Female Risk Factors Aged Male Middle Aged Severity of Illness Index Incidence Logistic Models Renal Insufficiency, Chronic Age Factors Humans
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Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

European psychiatry : the journal of the Association of European Psychiatrists Nov 1, 2016
BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depress...
Humans Machine Learning Algorithms Computer Simulation Middle Aged Female Depression Risk Factors Logistic Models Adult Cluster Analysis Mental Health Depressive Disorder Male
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A machine learning-based framework to identify type 2 diabetes through electronic health records.

International journal of medical informatics Oct 1, 2016
OBJECTIVE: To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects wit...
Bayes Theorem Pilot Projects Genome-Wide Association Study Diabetes Mellitus, Type 2 Logistic Models Support Vector Machine Humans Machine Learning Algorithms Electronic Health Records
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How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

Journal of biomedical informatics Sep 19, 2016
OBJECT: Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared ...
Machine Learning Logistic Models Decision Trees Area Under Curve Hyperuricemia Telemedicine Humans
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Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 23, 2016
We present a novel method to segment retinal images using ensemble learning based convolutional neural network (CNN) architectures. An entropy sampling technique is used to select informative points thus reducing computational complexity while perfor...
Optic Disk Pattern Recognition, Automated Entropy Humans Neural Networks, Computer Glaucoma Logistic Models Unsupervised Machine Learning
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Network or regression-based methods for disease discrimination: a comparison study.

BMC medical research methodology Aug 18, 2016
BACKGROUND: In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based met...
Humans Neural Networks, Computer Regression Analysis Diagnosis, Differential Bayes Theorem Computer Simulation Outcome Assessment, Health Care Reproducibility of Results Sensitivity and Specificity Logistic Models
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