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Anesthesia --- Decision Trees. --- Perioperative Care --- Decision Tree --- Tree, Decision --- Trees, Decision --- methods. --- Decision making. --- Methods. --- Decision Trees --- Anaesthesia --- Anesthesiology --- Analgesia --- Decision making --- methods

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Regulations that cover the legal obligations that manufacturers are bound to are essential for keeping the general public safe. Companies need to follow the regulations in order to bring their products to market. A good understanding of the regulations and the regulatory pathway defines how fast and at what cost the manufacturer can introduce innovations to the market. Regulatory technology and data science can lead to new regulatory processes and evidence in the medical field. It can equip stakeholders with unique tools that can make regulatory decisions more objective, efficient, and accurate. This book describes the latest research within the broader domain of Medical Regulatory Technology (MedRegTech). It covers concepts such as the complexity and user-friendliness of medical device regulations, novel algorithms for regulatory navigation, descriptive datasets from a health service provider, regulatory data science techniques, and considerations of the environmental impacts within a national health service. This book brings all these aspects together to offer an introduction into MedRegTech research. In the long term, these technologies and methods will help optimize the regulatory strategy for individual healthcare innovations and revolutionize the way we engage with regulatory services.

Technology: general issues --- History of engineering & technology --- medical technology --- digital healthcare --- data science --- regulations --- machine learning --- software --- medical device regulations --- healthcare provider --- software as medical device --- technology translation --- quality management system --- law --- medical devices --- regulatory data science --- natural language processing --- linguistic analysis --- optimisation --- medical device --- ecodesign --- environmental impact --- classification --- healthcare --- innovation --- regulation --- decision tree complexity --- medical technology --- digital healthcare --- data science --- regulations --- machine learning --- software --- medical device regulations --- healthcare provider --- software as medical device --- technology translation --- quality management system --- law --- medical devices --- regulatory data science --- natural language processing --- linguistic analysis --- optimisation --- medical device --- ecodesign --- environmental impact --- classification --- healthcare --- innovation --- regulation --- decision tree complexity

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The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind

Research & information: general --- Technology: general issues --- deep learning --- energy demand --- temporal convolutional network --- time series forecasting --- time series --- forecasting --- exponential smoothing --- electricity demand --- residential building --- energy efficiency --- clustering --- decision tree --- time-series forecasting --- evolutionary computation --- neuroevolution --- photovoltaic power plant --- short-term forecasting --- data processing --- data filtration --- k-nearest neighbors --- regression --- autoregression --- deep learning --- energy demand --- temporal convolutional network --- time series forecasting --- time series --- forecasting --- exponential smoothing --- electricity demand --- residential building --- energy efficiency --- clustering --- decision tree --- time-series forecasting --- evolutionary computation --- neuroevolution --- photovoltaic power plant --- short-term forecasting --- data processing --- data filtration --- k-nearest neighbors --- regression --- autoregression

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Regulations that cover the legal obligations that manufacturers are bound to are essential for keeping the general public safe. Companies need to follow the regulations in order to bring their products to market. A good understanding of the regulations and the regulatory pathway defines how fast and at what cost the manufacturer can introduce innovations to the market. Regulatory technology and data science can lead to new regulatory processes and evidence in the medical field. It can equip stakeholders with unique tools that can make regulatory decisions more objective, efficient, and accurate. This book describes the latest research within the broader domain of Medical Regulatory Technology (MedRegTech). It covers concepts such as the complexity and user-friendliness of medical device regulations, novel algorithms for regulatory navigation, descriptive datasets from a health service provider, regulatory data science techniques, and considerations of the environmental impacts within a national health service. This book brings all these aspects together to offer an introduction into MedRegTech research. In the long term, these technologies and methods will help optimize the regulatory strategy for individual healthcare innovations and revolutionize the way we engage with regulatory services.

Technology: general issues --- History of engineering & technology --- medical technology --- digital healthcare --- data science --- regulations --- machine learning --- software --- medical device regulations --- healthcare provider --- software as medical device --- technology translation --- quality management system --- law --- medical devices --- regulatory data science --- natural language processing --- linguistic analysis --- optimisation --- medical device --- ecodesign --- environmental impact --- classification --- healthcare --- innovation --- regulation --- decision tree complexity --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Regulations that cover the legal obligations that manufacturers are bound to are essential for keeping the general public safe. Companies need to follow the regulations in order to bring their products to market. A good understanding of the regulations and the regulatory pathway defines how fast and at what cost the manufacturer can introduce innovations to the market. Regulatory technology and data science can lead to new regulatory processes and evidence in the medical field. It can equip stakeholders with unique tools that can make regulatory decisions more objective, efficient, and accurate. This book describes the latest research within the broader domain of Medical Regulatory Technology (MedRegTech). It covers concepts such as the complexity and user-friendliness of medical device regulations, novel algorithms for regulatory navigation, descriptive datasets from a health service provider, regulatory data science techniques, and considerations of the environmental impacts within a national health service. This book brings all these aspects together to offer an introduction into MedRegTech research. In the long term, these technologies and methods will help optimize the regulatory strategy for individual healthcare innovations and revolutionize the way we engage with regulatory services.

medical technology --- digital healthcare --- data science --- regulations --- machine learning --- software --- medical device regulations --- healthcare provider --- software as medical device --- technology translation --- quality management system --- law --- medical devices --- regulatory data science --- natural language processing --- linguistic analysis --- optimisation --- medical device --- ecodesign --- environmental impact --- classification --- healthcare --- innovation --- regulation --- decision tree complexity --- n/a