AIMC Topic: Time Factors

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Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease.

Respiratory research
BACKGROUND: The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine ...

Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models.

Environmental science and pollution research international
Cardiovascular diseases belong to the leading causes of disability and premature death worldwide, including in Iran. It is predicted that the burden of the disease in Iran in 2025 will be more than doubled compared to 2005. Therefore, many forecastin...

Advancing pharmacy and healthcare with virtual digital technologies.

Advanced drug delivery reviews
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are provid...

Finite-time synchronization of quaternion-valued neural networks with delays: A switching control method without decomposition.

Neural networks : the official journal of the International Neural Network Society
For a class of quaternion-valued neural networks (QVNNs) with discrete and distributed time delays, its finite-time synchronization (FTSYN) is addressed in this paper. Instead of decomposition, a direct analytical method named two-step analysis is pr...

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series.

Anaesthesia, critical care & pain medicine
BACKGROUND: There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiological time series (PTS) data recorded on the first day of intensive care would contribute significan...

Time Series Classification with InceptionFCN.

Sensors (Basel, Switzerland)
Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the la...

Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks.

Methods (San Diego, Calif.)
Coronary heart disease is the first killer of human health. At present, the most widely used approach of coronary heart disease diagnosis is coronary angiography, a surgery that could potentially cause some physical damage to the patients, together w...

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Sensors (Basel, Switzerland)
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlyin...

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network.

Sensors (Basel, Switzerland)
Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material...

Finite-Time Output Synchronization and H Output Synchronization of Coupled Neural Networks With Multiple Output Couplings.

IEEE transactions on cybernetics
This article investigates the finite-time output synchronization and H output synchronization problems for coupled neural networks with multiple output couplings (CNNMOC), respectively. By choosing appropriate state feedback controllers, several fini...