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Interrupted Time Series Analysis

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Using machine learning to identify structural breaks in single-group interrupted time series designs.

Journal of evaluation in clinical practice
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...

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
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...

Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.

Journal of evaluation in clinical practice
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...

Multivariate LSTM-FCNs for time series classification.

Neural networks : the official journal of the International Neural Network Society
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...

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
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...

Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks.

Journal of bioinformatics and computational biology
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...

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

Current rheumatology reports
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...

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

International journal of medical informatics
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.

A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns.

American journal of epidemiology
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...

Reducing readmissions in the safety net through AI and automation.

The American journal of managed care
OBJECTIVES: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.