METAPHOR  A Multiagent Architecture using IoT and Classification Algorithms for Referral Postoperative Patients Diagnosing patients in a postoperative situation, more accurate and timely, is one of the health care challenges where computer science should help. According to studies conducted in some countries, patients are concerned about to stay longer time in the hospital after surgery, mainly for financial reasons. Reduction on hospital stay also reduces the risks of hospitalacquired infections. In this context, we tried to verify the impact of Internet of Things (IoT) devices and inference techniques in order to improve the diagnosis procedure. This paper presents METAPHOR (MultiagEnT Architecture POstoperative Referral) as an evolution of a previous proposed model applying machine learning classifier algorithms in order to indicate the best referral for postoperative patients based on online medical information. The best prediction classification algorithm reached an accuracy of almost 90% in the evaluated cases, evidencing the possibility of IoT medical devices can be used to improve postoperative patients management. To confirm the results, the Precision, Recall and F1 Score of each estimator where analyzed as well.

Internet of things in medicine: A systematic mapping study Abstract   Context  The current studies on IoT in healthcare have reviewed the uses of this technology in a combination of healthcare domains, including nursing, rehabilitation sciences, ambient assisted living (AAL), medicine, etc. However, no review study has scrutinized IoT advances exclusively in medicine irrespective of other healthcare domains.    Objectives  The purpose of the current study was to identify and map the current IoT developments in medicine through providing graphicaltabular classifications on the current experimental and practical IoT information in medicine, the involved medical subfields, the locations of IoT use in medicine, and the bibliometric information about IoT research articles.    Methods  In this systematic mapping study, the studies published between 2000 and 2018 in major online scientific databases, including IEEE Xplore, Web of Science, Scopus, and PubMed were screened. A total of 3679 papers were found from which 89 papers were finally selected based on specific inclusionexclusion criteria.    Results  While the majority of medical IoT studies were experimental and prototyping in nature, they generally reported that home was the most popular place for medical IoT applications. It was also found that neurology, cardiology, and psychiatrypsychology were the medical subfields receiving the most IoT attention. Bibliometric analysis showed that IEEE Internet of Things Journal has published the most influential IoT articles. India, China and the United States were found to be the most involved countries in medical IoT research.    Conclusions  Although IoT has not yet been employed in some medical subfields, recent substantial surge in the number of medical IoT studies will most likely lead to the engagement of more medical subfields in the years to come. IoT literature also shows that the ambiguity of assigning a variety of terms to IoT, namely system, platform, device, tool, etc., and the interchangeable uses of these terms require a taxonomy study to investigate the precise definition of these terms. Other areas of research have also been mentioned at the end of this article.