Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis

PROCEDURE

1. Under the permission granted by the study centers, the team will obtain the medical records of all eligible cases within the past 5 years

2. The investigators then collect the information of interest variable/parameter which obtained by history taking and further examinations and also medical Billing and Hospital pay per service. For participants with Health Insurance, the direct spending for treatment will be based on INA-CBGs (case-based group) payment. This data then will be recorded in an electronic database.

Parameter for model development :

Host-based :

1. Presence of Diabetes Mellitus (Including years of being diagnosed, HbA1c Before DST examination and treatment, medication either insulin or oral anti-diabetic)

2. Presence of HIV ((Including years of being diagnosed, CD4 level Before DST examination and treatment, and anti-retroviral medication)

3. Tobacco cessation (Brinkman Index)

4. Alcohol consumption

5. History of Immunosuppressant use (steroid)

6. Presence of other diseases (cancer, stroke, cardiovascular disease)

7. History of drug abuse

8. History of adverse drug reaction during tuberculosis treatment

9. Adherence of previous tuberculosis therapy

10. Presence of COPD

11. Body Mass Index

Environment

1. History of Contact with Tuberculosis Patients

2. Healthy Index of Living Environment (Household crowds)

Agent

1. Level of Bacterial Smear Before DST

2. Extension of Lesion in Chest X-Ray

3. Presence of Cavitation

Sociodemographic Factors

1. Age

2. Gender

3. Education

4. Income Level

5. Health Insurance

6. Marital Status

7. Employment Status

3. For incomplete information, a confirmation to the health center that was referring the cases will be done using the Tuberculosis Registration or questionnaire.

4. The model building will be done using an Artificial Intelligent Model in R. A selected model is an Artificial Neural Network either using Radial Base Function or multi-layer perceptron. Several important procedures including :

1. Determine Significant Parameter

2. Dealing with Insufficient and Imbalanced data class (over or under-sampling)

3. Normalization (Batch, Min-Max)

4. Layer and design

5. Training and test distribution (70:30)

6. Model Selection

5. External Validation will be done to the appointed study center. Precision: (true positive + True Negative)/All cases

6. The Incremental Cost-Effectiveness Ratio Simulation will be done, comparing the best model versus the gold standard and GeneXpert yielding a saving per unit of effectiveness