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Designing%20A%20Patient%20Monitoring%20System%20Using%20Cloud%20And%20Semantic%20Web%20Technologies

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Title: Designing%20A%20Patient%20Monitoring%20System%20Using%20Cloud%20And%20Semantic%20Web%20Technologies


1
Designing A Patient Monitoring System Using
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 patients 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"?
Motivation Bipolar Disorder Domain
  • Bipolar Disorder (BD) is a mental disorder,
    characterized by dramatic shifts in mood,
    thinking, behavior and energy that is seemed in
    up to 5 of primary health care patients
  • Symptoms of bipolar disorder may have great
    effects in the daily activities and general
    behavior of a person preventing him from a normal
    life
  • BD Management Must address different aspects
    of patients physical, emotional and social daily
    status
  • Need for a patient monitoring system to
    support longitudinal follow-up of BD

17th International Conference on Brain and Health
Informatics (ICBHI 2015)
4
"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 in OWL

17th International Conference on Brain and Health
Informatics (ICBHI 2015)
5
"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)
6
"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)
7
"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)
8
"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)
  • MontgomeryÅsberg
  • 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)
9
"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 patients
    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)
10
"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)
11
"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)
12
"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

13
"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 patients 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 patients symptoms then, the rule concludes
that the clinician needs to continue with
assiduous clinical examination PHR n
InitialEvaluation n (? Questionnaire.result
true) n (? ClinicalEvaluation.result normal)
? Recommendation
17th International Conference on Brain and Health
Informatics (ICBHI 2015)
14
"Designing A Patient Monitoring System Using
Cloud And Semantic Web Technologies"?
Overall Architecture
  • High level expected functionalities of 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)
15
"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)
16
Thank you! (Questions ?)
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