1. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering A Causal Probabilitic Network for Optimal Treatment of Bachterial Infections Alicia Ruvinsky Scott Langevin

2. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Problem: Bacterial Infections 30 percent mortality rate from severe bacterial infection 1/3 given inappropriate treatment 20% prescribed superfluous drugs Anti-biotic drugs account for 20-50 percent of hospitals drug expenses Bacterial resistance to anti-biotic treatment aggravated by miss-diagnoses

3. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Research Objectives Build a Decision Support System to provide: Likelihood of a bacterial infection Measure of its severity Most likely site of infection Most likely pathogen Susceptibility of pathogen to drugs Gain in life expectancy through treatment Cost of drug treatment (price, side-effect, ecological impact, future resistance) Ranking of anti-biotic drugs (Cost-Benefit)

4. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Problems with initial BN Approach Model is not portable (specific to region/hospital) –Dichotomous data (local vs universal) Human input error (20% of cases) Obviating Enhancements Fix by normalizing system (localizing model) Fix by objective input requirements (symptoms, test results, etc)

5. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering General Modularized Design Nodes in BN: Pathogen - Represent the potential pathogens of infections at the given site M_Distrib – Major patient-groups exhibiting a particular pathogen within the given site Minor – minor distribution factors; factors that change the likelihood of one or more pathogens without affecting the overall risk for infection. Infection – the different patterns in which infections manifest Local-respo – local responses caused by an infection Local-sign – manifestation of local responses. Sys-respo – systemic response caused by infection and common to all sites of infections Sign – manifestations of sys-respo Spec-cultu – ability of pathogen to grow in local specimen Blood-cultu – ability of pathogen to grow in the blood Lab-site – ability of pathogen to grow at local site Antibiotic_tr – antibiotic treatment prescribed for an infection Coverage – the percentage of pathogens of a given infection susceptible to an antibiotic drug Resistance – in-vitro susceptibility of pathogen to treatment Cost – accounts for cost of using antibiotic: cost of purchase, side effects, and ecological impact Gain – net gain in life expectancy gotten by prescribing an antibiotic drug Underlying – disorders of the patient

6. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering General Scheme for Site of Infection Network

7. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Urinary Tract Infections Network

8. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Calibrating the System

9. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Results Addresses all important decision-points in first days of patient care Expect the system to perform better than clinician No test data showing it improves clinical practice and patient outcome - need a clinical trial Convenience of calibrating system for new locations?