AIMC Topic: Logistic Models

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Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.

European radiology Mar 12, 2020
OBJECTIVE: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radiomics features.
Artificial Intelligence Machine Learning Algorithms Tomography, X-Ray Computed Humans Neural Networks, Computer Lung Neoplasms Logistic Models Early Detection of Cancer Radiology Semantics Reproducibility of Results Aged Middle Aged Image Processing, Computer-Assisted
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Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

Diabetes & metabolic syndrome Mar 10, 2020
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based c...
Support Vector Machine Factor Analysis, Statistical Area Under Curve Discriminant Analysis Diabetes Mellitus, Type 2 Logistic Models Treatment Outcome Diabetes Mellitus Automation Bangladesh Demography Humans Machine Learning Female Reproducibility of Results Adult Male Middle Aged Health Surveys
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Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Acta oncologica (Stockholm, Sweden) Mar 9, 2020
The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-mak...
Machine Learning Retrospective Studies Female Aged Aged, 80 and over Humans Biopsy, Fine-Needle Sentinel Lymph Node Biopsy Neoplasm Invasiveness Adult Lymph Node Excision Breast Neoplasms Logistic Models Axilla Middle Aged Regression Analysis Lymph Nodes ROC Curve Breast Nomograms Tumor Burden Lymphatic Metastasis Carcinoma, Ductal, Breast Sentinel Lymph Node
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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...
Models, Statistical Health Facilities United States Department of Veterans Affairs Patient Admission Neural Networks, Computer Humans Hospital Mortality Logistic Models United States
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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...
Treatment Outcome Humans Risk Factors Neural Networks, Computer Cohort Studies Area Under Curve Machine Learning ROC Curve Subarachnoid Hemorrhage Prognosis Logistic Models
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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...
Surgical Wound Infection Information Storage and Retrieval Logistic Models Support Vector Machine Decision Trees Humans Natural Language Processing Machine Learning Algorithms Electronic Health Records Sensitivity and Specificity
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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 Machine Learning Models, Theoretical Models, Statistical Electronic Health Records Patient Outcome Assessment Female Male Logistic Models
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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...
Humans Machine Learning Databases, Factual Electronic Health Records Logistic Models Support Vector Machine Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems
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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 ...
Middle Aged Female ROC Curve Risk Factors Neoplasm Recurrence, Local Image Processing, Computer-Assisted Logistic Models Humans Sensitivity and Specificity Adenocarcinoma of Lung Lung Neoplasms Neoplasm Invasiveness Male Machine Learning Retrospective Studies Tomography, X-Ray Computed
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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 Algorithms Humans Prognosis Electronic Health Records Reproducibility of Results
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