{"title":"UbMed:一个无处不在的监测药物依从性的系统","authors":"V. Silva, M. Rodrigues, R. Barreto, V. Lucena","doi":"10.1109/HealthCom.2016.7749419","DOIUrl":null,"url":null,"abstract":"One of the biggest problems with chronical patients in treatment is medication adherence. Studies indicate that taking drugs out of time influence the patient's treatment decreasing the drugs effect. To minimize this problem, we developed a ubiquitous and intelligent system able to monitor the taking of medicines and to identify whether the patient is meeting the requirements prescribed by the doctor. An architecture provided with a decision system based on rules and trees to evaluate data collected from an intelligent medicine cabinet, sensors and electronic devices available in the patient's home was designed. The system classified the drugs taken pattern and released messages on social networks, SMS, and consumer electronic devices such as TV, smartphone and tablets, without human interference. Its goal is to help keeping the medication on time and helping to decide what to do in case of missing the right time. The algorithms J48, Rep and Random tree, were tested to classify the taking medicine patterns and to chose the right services available. The obtained results are very promising and reached an acceptable accuracy rate.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"UbMed: A ubiquitous system for monitoring medication adherence\",\"authors\":\"V. Silva, M. Rodrigues, R. Barreto, V. Lucena\",\"doi\":\"10.1109/HealthCom.2016.7749419\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the biggest problems with chronical patients in treatment is medication adherence. Studies indicate that taking drugs out of time influence the patient's treatment decreasing the drugs effect. To minimize this problem, we developed a ubiquitous and intelligent system able to monitor the taking of medicines and to identify whether the patient is meeting the requirements prescribed by the doctor. An architecture provided with a decision system based on rules and trees to evaluate data collected from an intelligent medicine cabinet, sensors and electronic devices available in the patient's home was designed. The system classified the drugs taken pattern and released messages on social networks, SMS, and consumer electronic devices such as TV, smartphone and tablets, without human interference. Its goal is to help keeping the medication on time and helping to decide what to do in case of missing the right time. The algorithms J48, Rep and Random tree, were tested to classify the taking medicine patterns and to chose the right services available. The obtained results are very promising and reached an acceptable accuracy rate.\",\"PeriodicalId\":167022,\"journal\":{\"name\":\"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HealthCom.2016.7749419\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HealthCom.2016.7749419","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UbMed: A ubiquitous system for monitoring medication adherence
One of the biggest problems with chronical patients in treatment is medication adherence. Studies indicate that taking drugs out of time influence the patient's treatment decreasing the drugs effect. To minimize this problem, we developed a ubiquitous and intelligent system able to monitor the taking of medicines and to identify whether the patient is meeting the requirements prescribed by the doctor. An architecture provided with a decision system based on rules and trees to evaluate data collected from an intelligent medicine cabinet, sensors and electronic devices available in the patient's home was designed. The system classified the drugs taken pattern and released messages on social networks, SMS, and consumer electronic devices such as TV, smartphone and tablets, without human interference. Its goal is to help keeping the medication on time and helping to decide what to do in case of missing the right time. The algorithms J48, Rep and Random tree, were tested to classify the taking medicine patterns and to chose the right services available. The obtained results are very promising and reached an acceptable accuracy rate.