RESEARCH POSTER PRESENTATION DESIGN © 2012

www.PosterPresentations.com

Infectious diseases outbreaks demand a timely and proportional

response to mitigate effects on public health. Management of

these outbreaks is becoming a growing concern in public health,

as it requires extreme actions and coordination between

governing authorities at both state and national levels. Dealing

with large numbers of incoming reports and alerts requires an

automated system which performs real time analysis on a

centralized repository of collected clinical information.

Health authorities use these collected information to identify,

manage, and investigate infectious diseases outbreaks. Such

data, when visually and adequately represented, support the

education of healthcare providers within participating facilities

and improves the outcome of disease outbreaks management.

The challenges includes

INTRODUCTION

OBJECTIVES

Infectious Disease – Eco System

Architecture

CONCLUSION

This research work is driven by the immense needs and

posed challenges for integrating different healthcare

systems to monitor and manage public health indicators

such as infectious diseases. As such, this work can be

extended in many aspects such as putting forward a policy-

based framework to allow injecting regulators rules. This

direction should follow the approach specified by El-

Hassan et al. [13] which allows specifying rules for

accessing resources (e.g.. patients data) in both normal

and emergency situations

REFERENCES

1. Pandiyan, M., El Hassan, O.,Maamar Z.,Rajasekaran, P.

"Semantic Interoperability for Infectious Diseases Reporting

System", Sep 2011, Computer Science and Information Systems

(FedCSIS).

2. M. K. Smith, C.Welty, and D. McGuinness. OWLWeb Ontology

Language Guide. W3C Recommendation,

http://www.w3.org/TR/owl-guide/ ,May 14, 2011.

3. W3C. Owl web ontology language-reference. LSDIS Lab,

University of Georgia,2004. http://www.w3.org/TR/owl-ref/.

4. Iqbal, A.M.; Shepherd, M.; Abidi, S.S.R., “An Ontology-Based

Electronic Medical Record for Chronic Disease Management” in

Jan. 2011 44th Hawaii International Conference pp. 4–6.

5. Sampalli, T. Shepherd, M. Duffy, J.., “A Patient Profile

Ontology in the Heterogeneous Domain of Complex and Chronic

Health Conditions” in Jan. 2011 System Sciences (HICSS), 2011

44th Hawaii International Conference.pp 4-6.

6. Arch-int, N.; Arch-int, S “SEMANTIC INFORMATION

INTEGRATION FOR ELECTRONIC PATIENT RECORDS

USING ONTOLOGY AND WEB SERVICES MODEL” in April.

2011 Information Science and Applications (ICISA),

International Conference.pp 3-5

7. International Classification of Diseases.

http://www.cdc.gov/nchs/icd/icd10.htm, May 14, 2011.

8. International Classification of Diseases, Ninth Revision (ICD-9)

http://www.cdc.gov/nchs/icd/icd9.htm, May 24, 2011.

9. Intersystems Cache. http://www.intersystems.com/cache/, May

24, 2011.

10.Business Objects

http://www.sap.com/solutions/sapbusinessobjects/index.epx, May

24, 2011

11.Jena A Semantic Web Framework for Java.

http://jena.sourceforge.net/ , May 14, 2011.

12.Hong Jiang, Hua-qiong Wang, Hong-lei Zhang, Peng-fei Li,

Jing-song Li. “Modeling for the Semantic Integration of Clinical

Pathways with Related Medical Systems”, 2012 International

Symposium on Information Technology in Medicine and

Education.

13.El-Hassan, Osama and Fiadeiro, Jos'e Luiz and Heckel, Reiko

“Managing socio-technical interactions in healthcare systems” in

2008 Proceedings of the 2007 international conference on

Business process management.

ACKNOWLEDGEMENTS

Thanks go to Dubai Health Authority team and particularly the

Director of Health Data and Information Analysis department whose

guidance and support are vital for completing this research

1. To gather information from various sources (i.e., healthcare

facilities) in real-time because of the diverse and

heterogeneous nature of healthcare applications.

2. To come up with a nation-wide policy-based infectious disease

monitoring system which can be implemented across several

healthcare regulators, has the ability to process generated

infectious diseases reports at different diagnosis stages (pre &

post confirmation) and statistically compute accurate infectious

diseases rates.

3. From a regulatory body point of view, since the infectious

disease is a growing concern in public health, it is necessary to

collaborate with other health authorities to exchange and

manage the related information and alerts.

4. For this automatic assessment of degree of accuracy of diagnosis

process and management are prime importance.

Murugavell Pandiyan(Student), Osama ElHassan (Head of eHealth – Dubai Health Authority), Pallikonda Rajasekaran (Professor, KLU, Tamil Nadu, India)

Semantic Interoperability with Decision Support for Infectious Disease

CHALLENGES

1. Each healthcare facility has its own software to manage patients

data e.g., Physician Practice Management System (PPMS) and

Electronic Medical Records (EMR). Moreover, even standardized

EMR systems might be interfaced differently and sub-systems

such as Lab systems are isolated in terms of their used standards

(i.e., proprietary standards) and thus raise extra integration

challenges.

2. Regulatory authority has to make sure the data retrieved from

clinics /laboratories have undergone through proper

policies/procedures.

3. Treating different policies/workflows of reporting a certain

infectious disease.

4. The Challenge of computing the accurate infectious diseases

rates is exacerbated by the inclusion of incomplete

information(e.g. incomplete diagnosis) or misdiagnose. Diagnosis

workflows of certain infectious diseases e.g. tuberculosis

5. It is necessary to collaborate with other health authorities to

exchange and manage the related information and alerts.

Disease centric model

Ontology model on SNOMED

Ontology model on ICD 9/ ICD 10

Relationship / Other codes

Ontology model on Health

Authority rules / regulations and

Diseases.

Ontology model on healthcare provider and

their licensure

Ontology model on Facility

Licensing Rules and the Quality

Certification Results

Ontology model on licensure

and their treatment procedure

relationship

Ontology modelDecision support - flow

neuron

X1

X2

Xn

W1

W2

Wk

bias

X2

Sigmoidal Function

Net Weightage.

Net𝑋𝑘 = 𝑘=1𝑘=𝑛𝑋𝑘𝑊𝑘 + 𝜃𝑘

Sigmoidal FunctionF(Sig) = 1/(1+𝑒−𝑥)

Transforming the net weightage.F(x)= Net𝑋𝑘 * F(Sig)

The current weightage change

∆𝑊𝑖=-ηΔ𝐸

𝑊𝑖

Net Xk

TO DETERMINE DIAGNOSIS USING ARTIFICIAL NEURAL NETWORK

LOINC Code Result Weightage Satisfied

X1 N1 W1 True

X2 N2 W2 True

X3 N3 W3 True

X4 N4 Wn False

From the statistical database calculate the incident rate

(𝜏).

Quantity of vaccination ∝ proportional to incident rate.

Incident rate likelihood over the period can be calculated

from Poisson Distribution.

𝑒−𝜏 ∗ 𝜏𝐾

F(K; 𝜏) = ---------------------

k!

CALCULATE THE VACCINATION QUANTITY REQUIREMENT

<ha:regulation>

<rule:licensurerule licensetype=”X”>

<rule:allowedProcedures>

<rule:treatmentcode codingsystem=”SNOMED-CT”>

xxx-yyyy

</rule:treatmentcode>

<rule:treatmentcode codingsystem=”SNOMED-CT”>

xxx-zzzzz

</rule:treatmentcode>

</rule:licensurerule>

<rule: diagnosispolicies seq=“1”>

<disease:tuberculosis code=”ddd-eeee” codingsystem=”SNOMED-CT”>

<disease:coretests>

<labtest:procedure code=”17296-5” codingsystem=”LOINC”>

<labtest:description>

Mycobacterium tuberculosis complex rRNA [Presence] in Unspecified specimen by

DNA probe

</labtest:description>

<labtest:weightage>W1</labtest:weightage>

</labtest: procedure >

</disease:coretests>

</disease:tuberculosis>

</rule:diagnosispolicies>

<rule:authorizationpolcies>

<rule:signingauthorities>

<regulator:provider code=“physician1”>

<regulator:providername>

</regulator:providername>

</regulator:provider>

</rule:signingauthorities>

</rule:authorizationpolcies>

……………………….

<ha:IsQuaCertificationRule>

<disease:tuberculosis code=”ddd-eeee” codingsystem=”SNOMED-CT”>

<ha:rule code=“x1” refers=“isquacode1” >

<rule:action call=“certificationCheckList()”>

<action:param>isquacode</actionparam>

<action:paramValue>$isquacode</action:paramValue>

</rule:action>

</ha:rule>

</disease:tuberculosis>

</ha:IsQuaCertificationRule>

</ha:regulation>