Word wide web has become the first choice of patients to inform themselves about their disease, side effects and possible treatments. While patient's knowledge from internet is widely regarded as having a positive influence on the treatment, a lot of criticism exists for the quality and the diversity of the available information. In this paper we demonstrate the Personal Medical Information Recommender (PMIR), a semantically-enabled, intelligent platform that empowers patients to search in a high quality set of web documents for relevant medical knowledge. In addition, the platform automatically provides intelligent and personalized recommendations, according to the individual preferences and medical conditions. To demonstrate the platform example patients will be used to show the functionality of the system. Then we will allow conference participants to directly interact with the system to test its capabilities.
During the last decade, the number of users who look for health and medical
information has dramatically increased. However despite the increase in those numbers and
the vast amount of information currently available online, one important challenge is
the problem of the quality and the amount of information that can be found online [
Although similar approaches exist already, on the e-Health (such as WebMD
(www.webmd.com) and HONSearch (www.hon.ch/HONsearch/Patients/)) and other
domains [
frequency of the annotations in the document and the collection. On the other hand user requests are represented as vectors r = (w1,Q,…, wt,Q, w1,P,…, wm,P, w1,I, …, wn,I,) where wj,Q is a weight for the jth annotation of the input query Q, wiP is a weight for the ith annotation of patient profile P and wkI is the weight of the inferred annotations I. Weights wk,Q and wmP are calculated based on the term frequency inverse document frequency of the annotations in the collection and patient profiles respectively. Concerning the inferred annotations, for each annotation on the user query and the profile we call Semantic Reasoning service to get its three super-classes and sub-classes that are similarly used for matching annotations in documents.
The documents are first ranked by the cosine of the angle between the document vectors and the request vector, and these weights are multiplied by preference weights that change the order of the presented results. The preference weights are calculated based on personalized preferences (user clicks and ratings). Finally, documents with low similarity (below a threshold) are discarded, so as to exclude irrelevant document. Null queries can be submitted as well, that correspond to retrieval of documents pertaining to the medical record and the patient’s preferences. This is used when calling the Automatic Recommendation App. Finally when no preferences are available (the cold start problem) they are not considered for recommendations. 3
To demonstrate the functionalities of the PMIR system, dummy patients will be used. A first prototype of the Demo is online (http://139.91.210.42/ - login: peter, password: peter123 and select the Recommender App). A richer and more stable version of the platform is about to be released. The demonstration will proceed in five phases, the main components of which are shown in Fig. 2: (1) Semantic Annotator: The demonstration will start by presenting the app that the medical experts are using to register, delete and modify external documents that contain useful information to a targeted set of patients. The annotations performed automatically will be explained and some examples will be presented. (2) Patient Medical Record: Then, the demonstration will proceed by presenting the information a dummy patient has already stored in his account and how this information is also annotated using the semantic annotator service. (3) Semantic Search Engine: In this phase, we will search for useful information using the semantic search engine. Modifications will be performed to the patient profile and then the adaptation of these results according to the modified patient profile will be demonstrated. In addition some results will be rated and clicked and the change in the order of the results will also be shown. (4) Intelligent Recommendations: Besides allowing the patient to search information by himself we will also present some recommendations proposed to him automatically. Again, we will demonstrate how these recommendations are adapted dynamically by changing the profile of the user. (5) Handson” phase: In this phase conference participants will be invited to directly interact with the system, either guided or by accessing the online PHR system using their laptops. To conclude, in this demonstration, we present a new platform that focuses on making the available information timelier and more relevant with respect to dynamic influences in the individual patient's treatment. The idea is that even if two patients suffer from the same disease and they are in the same phase of the treatment, their interests on available information may differ based on various factors such as additional comorbidities, patient preferences etc. We demonstrate our platform and allow the direct participation of the conference participants to test its capabilities. The extensive evaluation performed shows excellent result that will be published in a follow-up paper. To the best of our knowledge no other system, providing medical information, is able to be dynamically adapted in such a diverse environment.
Acknowledgments.
This work was partially supported by the iManageCancer (H2020-643529), the pMedicine (FP7-270089) and the EURECA (FP7-288048) EU projects 4