1 / 10

CS 566 Web Semantics Health Clinic Model

Constructed using RDF and RDF Schema, this model facilitates comprehensive information retrieval about health clinics, staff, patients, illnesses, treatments, and medicines for research and commercial purposes.

psorensen
Download Presentation

CS 566 Web Semantics Health Clinic Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CS 566Web SemanticsHealth Clinic Model Professor Antoniou Grigoris Antonis Misargopoulos misarg@csd.uoc.gr Athina Tziaki tziaki@csd.uoc.gr

  2. Introduction • Health Clinic model is constructed using RDF and RDF Schema • HC model is tested using VRP 2.5 • HC Schema is used to construct a PostgresQL DB of RSSDB • HC models a real-world health Clinic activities

  3. Basic Classes: HC Model Description age weight name Health Clinic Person integer height fname string string lname specialty Staff Patient ID card_ID Doctor Assistant : subClassOf RDF Schema

  4. Other Classes HC Model Description editionDate lastModifiedDate File Treatment type ID description s t r i n g name name Medicine Illness type Advices Medication Course description description

  5. HC RDF Schema RDF Schema

  6. HC RDF Data : subClassOf : type : association

  7. HC Potential Queries (1/2) • Information retrieval about health clinics, i.e. what clinic is visited mostly or what specialties supports • Information retrieval about clinic staff personal data, i.e. specialty, etc. • Information retrieval about patients personal data, i.e. average age, etc. • Information retrieval about patients files, i.e. what period of year visit clinic mostly • Information retrieval about illnesses, suggested treatments and medicines for research and commercial reasons

  8. HC Potential Queries (2/2) • Find the clinic with the larger number of cardiologists. • Find all doctor specialties, Benizeleio supports. • Find all doctors, who work at PEPAGNI. • Find all patients, monitored by Misargopoulos. • How many assistants cooperate with Misargopoulos. • Find average age of PEPAGNI patients. • Find George Jackson’s illness name and type. • Find all medicines suggested to Mary Jackson. • Find all medicines suggested by Benizeleio doctors • Find all assistant take care of George Jackson.

  9. HC RDF Schema Limitation (1/2) • Cardinality • Each patient visits at least one clinic (1...n) • If there is a patient, then there is a file and just one (1...1) • Each patient has at least one illness (1…n) • Each clinic has at least one staff member (1…n) • Each medication consists at least of one medicine (1…n) • If there is a medicine, then there is a course as well

  10. HC RDF Schema Limitation (2/2) • Reverse association • If a doctor cooperates with an assistant, then assistant cooperates with this doctor at once • If a file is updated by an assistant, then assistant update this file at once • etc. • Union, Intersection • A doctor or an assistant can also be a patient • etc.

More Related