Asthma is a common chronic disease that affects millions of people in the world. The most common signs and symptoms of asthma are cough, breathlessness, wheeze, chest tightness and respiratory rate. These signs and symptoms can’t be measured accurately since they consist of various types of uncertainties such as vagueness, imprecision, randomness, ignorance, incompleteness. Consequently, traditional disease suspicion, which is carried out by the physician, is unable to deliver accurate results. Hence, this paper presents the design, development and application of a decision support system to assess asthma suspicion under uncertainty. Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach (RIMER) was adopted to develop this expert system that is named as Belief Rule Based Expert System (BRBES). The system has the capability to handle various types of uncertainties both in knowledge representation and inference procedures. The knowledgebase of this system was constructed by taking account of real patient data and expert’s opinion. The practical case studies were used to validate this system. It was observed that the system generated results are more effective and reliable in terms of accuracy than the results generated by a manual system.
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A BELIEF RULE BASED (BRB) DECISION SUPPORT SYSTEM TO ASSESS CLINICAL ASTHMA SUSPICION MOHAMMAD SHAHADAT HOSSAIN A , MD. EMRAN HOSSAIN B , MD. SAIFUDDIN KHALID C , MOHAMMAD A. HAQUE D A, B DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, UNIVERSITY OF CHITTAGONG, BANGLADESH C DEPARTMENT OF LEARNING AND PHILOSOPHY & D DEPARTMENT OF ARCHITECTURE, DESIGN AND MEDIA TECHNOLOGY, AALBORG UNIVERSITY, DENMARK SCANDINAVIAN CONFERENCE ON HEALTH INFORMATICS (SHI 2014), 21-22 AUGUST
Transcript
A BELIEF RULE BASED (BRB) DECISION SUPPORT SYSTEM TO ASSESS CLINICAL
A, BDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, UNIVERSITY OF CHITTAGONG, BANGLADESHCDEPARTMENT OF LEARNING AND PHILOSOPHY & DDEPARTMENT OF ARCHITECTURE, DESIGN AND MEDIA
TECHNOLOGY, AALBORG UNIVERSITY, DENMARK
SCANDINAVIAN CONFERENCE ON HEALTH INFORMATICS (SHI 2014), 21-22 AUGUST
Aims and Objectives
Asthma & Related Works
Belief Rule Base (BRB)
BRB System to Assess Clinical Asthma Suspicion
Result & Discussion
Conclusion
Presentation Outline
Signs, Symptoms and Uncertainties
Causal relationships between signs and symptoms – representation by If-Then rule
Drawbacks of methodologies and algorithms
Expert system: Belief Rule-Based Inference Methodology Using the Evidential Reasoning (RIMER)
Work-in-progress: Optimal learning model (machine learning – to add experience of experts on real time basis.
Aims and Objectives
Asthma is a common chronic inflammatory disease of the airways characterized by variable and recurring symptoms, reversible airflow obstruction, and bronchospasm.
The most common signs and symptoms are- Cough 2) Breathlessness (Shortest of Breath) 3) Wheeze 4) Chest tightness 5) Respiratory Rate. Existing tools and methods:
Optical breath sensor, proportional logic (PL), first-order logic (FOL) or fuzzy logic (FL), forward chaining and backward chaining for inference engine
Scope: RIMER for a refined knowledge base and an inference mechanism.
Asthma and Related Works
Domain Knowledge Representation using BRB
Belief Rule
Domain Knowledge Representation using BRB (Cont.)
BRB System Prototype
Domain Knowledge Representation using BRB (Cont.)
Inference Procedure
Five input antecedents: cough (A1), breathlessness (A2), wheezing (A3), chest tightness (A4) and respiratory rate (A5).
Three referential values of these antecedent attributes: severe (S), moderate (Mo), mild (M) and normal (N).
Asthma (A6) has (2*4*3*2*2) = 96 belief rules
Domain Knowledge Representation using BRB (Cont.)
The BRBES system architecture
BRBES Interface
Asthma diagnosis by BRBES and expert
The AUC for the BRB system prototype is 0.952 (95% confidence interval = 0.960–1.012), and the AUC for the expert opinion is 0.857 (95% confidence interval = 0.939–1.014).
Results and Discussion (Cont.)
Conclusion
Reduce the medical error and various types of uncertainties Reduce medical cost BRB System employed a novel methodology known as RIMER
allows the handling of various types of uncertainty Currently, an attempt has been undertaken to enhance the
system with the capability to supporting the diagnosis of Asthma Training Module for BRB
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