gitlab.com topics: python
healthdatahub/applications-du-hdh/schema-snds
Formalisation du schéma du SNDS selon la spécification table-schema. Création de produits dérivés.
Last synced at: 5 days ago - Pushed at: 11 months ago - Stars: 9 - Forks: 13
healthdatahub/boas/cnam/top-diabete
La cartographie des pathologies et des dépenses, produite par la Cnam (https://data.ameli.fr/pages/data-pathologies/), a pour objectif de répartir les dépenses de soins de l’assurance maladie entre une soixantaine de pathologies, traitements chroniques et épisodes de soins fréquents et/ou graves et/ou coûteux. L’outil se base notamment sur des algorithmes de repérage de ces pathologies dans le SNDS. Les données individuelles annuelles sont mises à disposition dans le SNDS afin de faciliter le travail des chercheurs pour repérer les pathologies et calculer les dépenses de soins remboursés de chaque individu avant leur répartition par pathologie. Le programme de l’algorithme Top Diabète permet de repérer les personnes prises en charge pour un diabète.
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healthdatahub/applications-du-hdh/cartographie-ecosysteme-snds
The main database of the French National Health Data System (SNDS) contains data from Health Insurance reimbursements, hospital treatment and medical causes of death. In order to characterise its use for health research and innovation, an interactive cartography has been produced to understand the framework of its use and to identify the stakeholders of the SNDS ecosystem. A bibliographic search via PubMed (available here), LiSSa, HAL was conducted to identify scientific articles published starting January 2007 on studies using SNDS data. The list of authors, their affiliations, keywords, the number of citations and much more were collected. A descriptive analysis was carried out in order to assess temporal and geographical trends in the use of SNDS main database. The graphs where generated with networkx, a python package used for the creation manipulation and study of complex networks. To generate the Author/Affiliations graphs we first create the adjacency matrix between the Authors/Affiliations and the article PMIDs. We then use the networkx.Graph class to create the needed undirected graphs, using the adjacency matrices as the data to intialize the graphs.
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healthdatahub/applications-du-hdh/indexation-nomenclatures
Indexation des nomenclatures (csv) dans ElasticSearch pour offrir un moteur de recherche
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