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Logistic Models

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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...
Neural Networks, Computer Hospital Mortality Logistic Models Health Facilities United States Department of Veterans Affairs United States Models, Statistical Humans Patient Admission
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Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined...
ROC Curve Prognosis Area Under Curve Logistic Models Risk Factors Subarachnoid Hemorrhage Humans Treatment Outcome Cohort Studies Neural Networks, Computer Machine Learning
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Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allo...
Logistic Models Humans Support Vector Machine Machine Learning Decision Trees Algorithms Natural Language Processing Electronic Health Records Surgical Wound Infection Sensitivity and Specificity Information Storage and Retrieval
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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...
Humans Models, Statistical Machine Learning Electronic Health Records Patient Outcome Assessment Male Female Logistic Models Models, Theoretical
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Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy co...
Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Databases, Factual Logistic Models Humans Electronic Health Records Support Vector Machine Machine Learning
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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

European radiology Feb 28, 2020
PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography ...
Lung Neoplasms Logistic Models Neoplasm Invasiveness Neoplasm Recurrence, Local Adenocarcinoma of Lung Middle Aged ROC Curve Image Processing, Computer-Assisted Risk Factors Sensitivity and Specificity Male Humans Tomography, X-Ray Computed Female Machine Learning Retrospective Studies
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The application of unsupervised deep learning in predictive models using electronic health records.

BMC medical research methodology Feb 26, 2020
BACKGROUND: The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder fea...
Hospitalization Deep Learning Logistic Models Outcome Assessment, Health Care Prognosis Electronic Health Records Reproducibility of Results Humans Algorithms
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Comparing an Artificial Neural Network to Logistic Regression for Predicting ED Visit Risk Among Patients With Cancer: A Population-Based Cohort Study.

Journal of pain and symptom management Feb 21, 2020
CONTEXT: Prior work using symptom burden to predict emergency department (ED) visits among patients with cancer has used traditional statistical methods such as logistic regression (LR). Machine learning approaches for prediction, such as artificial ...
Cohort Studies Humans Ontario Logistic Models Neural Networks, Computer Emergency Service, Hospital Neoplasms
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CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists.

European radiology Feb 13, 2020
OBJECTIVES: To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model ...
Female Aged Male Middle Aged Humans Retrospective Studies Tomography, X-Ray Computed Adenocarcinoma of Lung Logistic Models Radiologists Radiography, Thoracic Deep Learning ROC Curve Lung Neoplasms
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Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection.

Scientific reports Feb 13, 2020
The aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage IV colorectal cancer patients. Four basic ML algorithms were used for prediction-logistic regression, d...
Middle Aged Logistic Models Neoplasm Staging Neoplasm Recurrence, Local Aged, 80 and over Humans Colorectal Neoplasms Machine Learning Male Female Algorithms Aged Prognosis
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