Titlebook: Data Science for Healthcare; Methodologies and Ap Sergio Consoli,Diego Reforgiato Recupero,Milan Pet Book 2019 Springer Nature Switzerland

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Overview of Data Linkage Methods for Integrating Separate Health Data SourcesIn many practices, such identifiers are simply lacking meaning that one has to resort to variables that are not necessarily unique to a person which makes the task of linking data far more challenging. To make things worse, these linking variables come with errors due to misspellings, coding differe

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Data Visualization in Clinical Practicet undergone thorough external validation or because circumstances have changed since the model was developed..We can allow for the doctor to stay in charge of any inferences made from data through visualization instead of mere presentation of, e.g., risk scores or survival probabilities from a train

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Book 2019y intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book..  .

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Application Programming Interfacesce technologies in the healthcare sector. This contribution is based on a recent whitepaper (.) provided by the Big Data Value Association (BDVA) (.), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data

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Case Study: Natural Language ProcessingChallenges, in broad adoption of DL, are commonly related to some of its main drawbacks, particularly lack of interpretability and transparency. We discuss the drawbacks and limitations of DL technology that specifically come to light in the domain of healthcare. We also address the need for a consi

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