{"title":"A Self Developing System for Medical Data Analysis","authors":"Adriana Dinis, Todor Ivascu, V. Negru","doi":"10.1109/SYNASC.2018.00058","DOIUrl":null,"url":null,"abstract":"In this paper we present a concept project for a self developing system based on agents built for a hospital. The system monitors patients during and after being released from hospitalization, with the aim of understanding patterns and predicting future problems. Due to its complexity and dynamism the agents must be automatically generated. They need to cooperate and \"compete\" with each other in order to get good results. By combining meta-heuristic algorithms with reinforcement and clustering techniques we target a large degree of autonomy in decision making.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2018.00058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper we present a concept project for a self developing system based on agents built for a hospital. The system monitors patients during and after being released from hospitalization, with the aim of understanding patterns and predicting future problems. Due to its complexity and dynamism the agents must be automatically generated. They need to cooperate and "compete" with each other in order to get good results. By combining meta-heuristic algorithms with reinforcement and clustering techniques we target a large degree of autonomy in decision making.
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