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https://www.selleckchem.com/products/rp-6685.html Proper algorithms can be used to identify patients with HF.This paper presents an application of deep neural networks (DNN) to identify patients with Alcohol Use Disorder based on historical electronic health records. Our methodology consists of four stages including data collection, preprocessing, predictive model development, and validation. Data are collected from two sources and labeled into three classes including Normal, Hazardous, and Harmful drinkers. Moreover, problems such as imbalanced classes, noise, and categorical variables were handled. A four-layer fully-connected feedforward DNN architecture was designed and developed to predict Normal, Hazardous, and Harmful drinkers. Results show that our proposed method could successfully classify about 96%, 82%, and 89% of Normal, Hazardous, and Harmful drinkers, respectively, which is better than classical machine learning approaches.Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called "Mainzelliste". This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste's API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from http

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