AIMC Topic: Interrupted Time Series Analysis

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Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights.

International journal of medical informatics Jul 11, 2024
OBJECTIVE: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm.
Female SARS-CoV-2 Aged Humans COVID-19 Electronic Health Records Deep Learning Incidence Australia Adult Male Middle Aged Emergency Service, Hospital Pain Interrupted Time Series Analysis
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Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

Current rheumatology reports May 20, 2020
PURPOSE OF REVIEW: The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowin...
Artificial Intelligence Machine Learning Linear Models Arthritis Stochastic Processes Interrupted Time Series Analysis Humans
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A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method.

Computer methods and programs in biomedicine Jan 9, 2020
BACKGROUND AND OBJECTIVE: The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelli...
Humans Interrupted Time Series Analysis Research Design Artificial Intelligence Arthroplasty, Replacement, Hip Outcome Assessment, Health Care Incidence Hip Arthroplasty, Replacement, Knee Arthritis, Rheumatoid Knee Linear Models
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Multivariate LSTM-FCNs for time series classification.

Neural networks : the official journal of the International Neural Network Society May 4, 2019
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Atten...
Interrupted Time Series Analysis Neural Networks, Computer Multivariate Analysis
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Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.

Journal of evaluation in clinical practice Jun 11, 2018
RATIONALE, AIMS, AND OBJECTIVES: Interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time, and the intervention is expected to "interrupt" the level and/or trend of th...
Smoking Prevention Program Evaluation Commerce Humans Interrupted Time Series Analysis Machine Learning Discriminant Analysis California Propensity Score Research Design Treatment Outcome Evaluation Studies as Topic
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A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network.

IEEE transactions on neural networks and learning systems Oct 12, 2017
This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced perfor...
Interrupted Time Series Analysis Machine Learning Heart Rate Respiratory Mechanics Humans Neural Networks, Computer
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Using machine learning to identify structural breaks in single-group interrupted time series designs.

Journal of evaluation in clinical practice Apr 19, 2016
RATIONALE, AIMS AND OBJECTIVES: Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the interve...
Middle Aged Discriminant Analysis Female Selection Bias Adult Interrupted Time Series Analysis Humans Machine Learning Research Design Male
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Reducing readmissions in the safety net through AI and automation.

The American journal of managed care Mar 1, 2025
OBJECTIVES: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.
Retrospective Studies Electronic Health Records Female Humans Artificial Intelligence Patient Readmission Safety-net Providers Interrupted Time Series Analysis Aged Male Middle Aged
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A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns.

American journal of epidemiology Jan 8, 2025
When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from o...
Interrupted Time Series Analysis SARS-CoV-2 Cities United States Causality Humans Air Pollution Machine Learning Nitrogen Dioxide COVID-19
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Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks.

Journal of bioinformatics and computational biology Feb 1, 2020
Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be upd...
Influenza A Virus, H3N2 Subtype Influenza Vaccines Interrupted Time Series Analysis Mutation Rate Humans Viral Proteins Neural Networks, Computer Computational Biology Epitopes Influenza, Human
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