Research in Health Informatics: Bangladesh Perspective Dr. Abu Sayed Md. Latiful Hoque Professor, Dept. of CSE, BUET Workshop on Health Data Analytics

Outline 2  Motivation  Introduction  Health Informatics, Data, and Problems  Research on the Development of Data Marts and Warehouses and Applications  Research on Data Mining for better Health Services  Conclusions

3 Motivation for Developing National Health Data Warehouse (NHDW) of Bangladesh 3  Bangladesh needs to develop NHDW for  Better Healthcare Delivery  Better Health related Research  Better Health Monitoring and Administration  Developed Countries like US, UK or Australia already developed DWs

Define National Reference level In Bangladesh, rule of thumbs is for Woman > 15 years, non pregnant Hb> 11 Good; Hb >=10.0 ok, Less than 10 medication needed. 4 WHO's Hemoglobin thresholds used to define anemia [29] 4 Motivation (2)

5 Motivation (3) National health trend analysis 5

Motivation (4) Location Dependency analysis Finding Impact of different region of Bangladesh on the test result Arsenic effect on Chandpur; Forecasting of disease, virus or epidemic Juice of Palm tree of which location is more suspicious for Nipah virus 6

Motivation (5) Redundant/ Fraud Testing Awareness If For a Costly Test T 3 : Age(X, Negative (X, T 3 ) [Support =70%, Confidence=95%] National awareness can be developed not to perform the test at initial level for Young Males. 7

Introduction  What Is Data Mining?  Data mining (knowledge discovery from data)  Extraction of interesting ( non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data  Alternative names  Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

Knowledge Discovery (KDD) Process  This is a view from typical database systems and data warehousing communities  Data mining plays an essential role in the knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

Research on Health Data 10  Using effective data mining tools & algorithms possible to produce useful information from Health dataset Applications of Health Data Mining

Patient Visit Cycle in Bangladesh 11

Patient Visit Cycle (Cont’) 12

Patient Visit Cycle (Cont’) 13

Patient Visit Cycle (Cont’) 14

Patient Visit Cycle (Cont’) 15

Existing NHDW Overview Now DGHS has: 33 aggregated data sets Most health programs are included Data entered up-to 4, 501 Union level facility 3 individual data sets Data entered from 13,000 community clinic 16

Research on Health Data: Challenges 17  Architecture of National Health DW of Bangladesh  Preprocessing  Missing Value Problem  Noisy Data Problem  Data Transformation  Record Linkage  No Standard Patient Identification in data  Missing Value Problem  Noisy Data Problem  Privacy Preservation

18 Proposed Architecture of NHDW, Bangladesh

19 Sample Fact and Dimension Tables of NHDW for Pathological Data

Designing NHDW Data Cube 20

21 Snapshot of Pathological Data

Our Approach: Patient Identification Technique based on Secured Record Linkage (PITSRL) 22

Patient De-Identification with Linkage Preservation (PDLP) Technique 23

Dataset Quality 24

Research on Specialized Group: Diabetic Food Intake Insulin Type & Dose Exercise Stress Blood Glucose Level time  (t+PH) t+PH Historical Dataset Knowledge Acquirement Current Time t Conceptual View of BGL Prediction Model

Scopes of Future Research Improvement of data quality:  A dataset with sufficient amount of variations is indispensible.  The quality of dataset depends on accuracy of estimation and accumulation of life style data (diet, insulin, exercise, stress etc.)  An intelligent entry module can be developed for estimating effects of emotional events on blood glucose level.

Scopes of Future Research Development of Prediction Technique: A model can be developed with neural network based classification techniques for predicting glucose concentration for improved performance. Development of Therapy Optimizer: Development of an inexpensive, hand-held ANN diabetes therapy system for patients, which will incorporate a BGL monitoring device.

Conclusions 28  Health Data Warehouse is very essential for a Nation for better healthcare delivery  Main challenges are proper framework development, data preprocessing (e.g., cleaning, missing value imputation), record linkage establishment and privacy preservation  We have proposed a national framework for integration of enormous, diverse health data to facilitate knowledge discovery.  We have performed some preprocessing on health data such as cleaning, normalization etc.

Conclusions (2) 29  Proper Record Linkage and Privacy Preservation are two big issues for Integrated health systems  We have developed Patient Identification Technique based on Secured Record Linkage (PITSRL) for Privacy Preserved Record Linkage  For a noisy health dataset of patients, we achieved 87% sufficient record linkage key.  For a training dataset of 100 patient records, PITSRL achieved 100% accuracy of identifying unique and duplicate patients.

Conclusions (3) 30  Data mining technique can be used for the prediction of severity of diseases e.g., diabetic, blood pressure etc.  Specialized patient management system need to be developed for special care for critical patients

Thank You 31