Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies

1. Designing A Patient Monitoring SystemUsing Cloud And Semantic Web Technologies Technical University of Crete Chryssa Thermolia -Ekaterini S. Bei​ Stelios Sotiriadis​ - Kostas Stravoskoufos​ Euripides G.M. Petrakis 17th International Conference on Brain and Health Informatics (ICBHI 2015)

2. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Motivation Patient Monitoring Systems • New era of healthcare requires new tools, devices and systems to improve health • services​ • Advanced health care increases the need for constant monitoring of patient's condition​, especially in chronic diseases • ​Solution • Multi-source patient monitoring evolves into an important service in this domain​ • Patient monitoring systems offer advantages in​ •      - early-diagnosis       • - optimal treatment strategies  • - disease prevention​ • - analysis, management and communication of medical information​ • ​ • Key factors in this attempt ​ •   integration of medical information from various sources • constant, on-time briefing of patient’s health state and behavior ​ 17th International Conference on Brain and Health Informatics (ICBHI 2015)

3. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Background Semantic Web • Semantic Web:Collection of standard technologies to realize a Web of Data • - Ontology: Heart of Semantic Web • Formal representation of knowledge as a set of concepts. • Describes the concepts (classes) of in a domain interest, their characteristics (data properties) and the relationships that hold between the concepts (object properties) • Example: • OWL (Web Ontology Language): Language that describes the concepts and their relationships. • SWRL (Semantic Web Rule Language): Implements deductive reasoning inOWL 17th International Conference on Brain and Health Informatics (ICBHI 2015)

4. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Background Internet of Things (IoT) • Internet of Things (IoT) • Relates users and their smart devices • along with sensors used in every day actions • (e.g., Smart phones and wearable devices) • Various devices could offer to sensor embedded healthcare new applications and services • IoT is expected to greatly transform the healthcare industry by improving the clinician-patient relationship • Clinicians using IoT could • - monitor patients remotely • - run a diagnosis in real-time • - be notified for sudden and • abnormal events and act • immediately 17th International Conference on Brain and Health Informatics (ICBHI 2015)

5. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Background Cloud Computing • IoT growth leads to large amounts of data • Need for big data storage, processing and • accessing • Cloud computing as a paradigm for big data storage and analytics • Cloud Computing: • Provides a platform environment where hardware and software could be delivered on a bespoke manner to users and utilized accordingly to their requests. • Allows the scaling of user resources on demand (elasticity) • Combination of IoT and Cloud Computing is the real innovation 17th International Conference on Brain and Health Informatics (ICBHI 2015)

6. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System The designed patient monitoring system is aimed to be able to support clinical professionals during the initial evaluation and diagnosis of adults with suspected BD, and during their treatment The system will be able to provide • evidence-based treatment options for a personalized therapeutic approach • notifications for early-warning signs and alerts for crucial mood swings Our design consists of three components 1) The implemented core system. 2) A proposal of the front-end system. 3) The vision of the back-end system. 17th International Conference on Brain and Health Informatics (ICBHI 2015)

7. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System Guidelines • The World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for the Biological Treatment of Bipolar Disorders (long-term treatment) • Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) collaborative update of CANMAT guidelines for the management of patients with bipolar disorder (acute episodes of mania, depression) • The CANMAT task force recommendations for the management of patients with mood disorders and comorbid medical conditions (diagnosis) • Australian and New Zealand clinical practice guidelines for the treatment of bipolar disorder (breakthrough depression) • Bipolar disorder algorithms: The Psychopharmacology Algorithm Project at the Harvard South Shore Psychiatry Program , TEXAS Medication Algorithm Project (immediate & urgent) • Rating Scales • Hamilton Depression Rating Scale (HDRS) • Young Mania Rating Scale (YMRS) • Montgomery–Åsberg • Clinical Global Impression Bipolar Version Scale, CGI-BP • Global Assessment of Functioning Scale, GAF • The Quality of Life in Bipolar Disorder Questionnaire 17th International Conference on Brain and Health Informatics (ICBHI 2015)

8. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System User Scenarios For BD • Types of Bipolar Disorder: • DSM-IV-TR Classification • Bipolar I Disorder (BDI): one or more episode of mania with or without major depressive episodes • Bipolar II Disorder (BDII): one or more episode of hypomania as well as at least one major depressive episode with no psychotic features • Cyclothymic disorder: low grade cycling of mood with the presence or history of hypomanic episodes and periods of depression that do not meet criteria for major depressive episodes • Bipolar disorder NOS: Bipolar symptoms that do not meet the criteria for previous subtypes • Diagnosis: • 1st Level (clinician studies the patient’s experience in regards of abnormal symptoms), • 2nd Level (clinician estimates according to defined criteria taking also into account family history) • Therapy: pharmacotherapy (mood stabilizers, antidepressants, antipsychotics), psychoeducation, • psychotherapy 17th International Conference on Brain and Health Informatics (ICBHI 2015)

9. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System User Scenarios For BD Possible phase transitions of Bipolar I Disorder The scenarios are developed considering possible phase transitions of BD that may occur during the progress of the disease (mania to euthymia, depression to euthymia, mania to depression, and vice versa) 17th International Conference on Brain and Health Informatics (ICBHI 2015)

10. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System User Scenarios The developed scenarios support clinician through • Diagnosis • Immediate & Urgent Management & Treatment • Acute Manic Episode Treatment • Acute Depressive Episode Treatment • Breakthrough Depressive Episode (Li) Treatment • Long-term Treatment 17th International Conference on Brain and Health Informatics (ICBHI 2015)

11. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System Ontology Dynamic Entities • PHR • PatientState • Symptom • Function Tests • Therapy • Medicine • Static entities • Patient • PatientHistory • Episode • InitialEvaluation • History • Questionnaire • (MDQ, BSDS, CIDI) • Clinical Evaluation • Medical Cause • Diagnosis • StandardTest • SideEffect

12. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Core System SWRL Rules • Clinical guidelines encoded as SWRL rules to issue alerts and recommendations • Apply rules over patient’s information. Example: If there is a positive mood questionnaire, there is a suspicion of BD and in that case, if the clinical evaluation excludes other medical causes from being responsible for the patient’s symptoms then, the rule concludes that the clinician needs to continue with assiduous clinical examination PHR ∩ InitialEvaluation ∩ (∃ Questionnaire.result = true) ∩ (∃ ClinicalEvaluation.result = normal) → Recommendation 17th International Conference on Brain and Health Informatics (ICBHI 2015)

13. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Overall Architecture High level expected functionalitiesof the monitoring system prototype: • Data collection • Interoperability • Notification services • Data analysis and integration • Secure data storage • Legacy system adaptors • Other services such as semantic analysis tools 17th International Conference on Brain and Health Informatics (ICBHI 2015)

14. "Designing A Patient Monitoring System Using Cloud And Semantic Web Technologies"​ Conclusions & Future Work Conclusions: • Analyze patient records and filtering • evidence-based guidelines to offer • individualized notifications and • recommendations for diagnosis • and treatment. • Propose a cloud deployment model • as a perspective for an advanced • environment that assists in • monitoring of complex chronic • pathologies, such as brain disorders • including BD. Future Work: • Implement and test the cloud- based • architecture on a real setting perfor- • ming data acquisition from sensors • and wearable devices 17th International Conference on Brain and Health Informatics (ICBHI 2015)

15. Thank you!(Questions ?)