Artificial Intelligence-Driven Data Science for Enhancing TB Treatment Outcomes and Reducing Mortality Rates

Abstract

Tuberculosis (TB) remains a leading global health concern, ranking among the top causes of death worldwide. It surpasses HIV in mortality, with approximately 1.3 million deaths annually among HIV-negative individuals and 214,000 among those co-infected with HIV [1]. Despite progress in reducing TB mortality rates, as evidenced by Zambia’s decrease from 759 to 361 deaths per 100,000 population between 2000 and 2017, TB continues to be a significant health challenge in high-burden regions like southern Africa [1]. In Zambia's Itezhi-Tezhi district, factors contributing to TB mortality include advanced age, poor treatment adherence, extra-pulmonary TB, and complications related to co-infection with HIV. While systems such as SmartCare and YATHU DR TB have been developed to manage records for drug-susceptible and drug-resistant TB, there is a critical need for more advanced tools to further enhance treatment outcomes. This paper proposes an AI-driven automated system that utilizes data science techniques to improve TB treatment outcomes and reduce mortality rates. The system employs data mining to track and analyze comprehensive patient records throughout the treatment phases, including intensive and continuation phases. It gathers and evaluates data on patient demographics, drug adherence, treatment progress, and outcomes in comparison with similar cases that have achieved successful treatment. By leveraging AI algorithms to predict treatment outcomes based on historical data, healthcare providers can gain valuable insights into patient progress, enabling more timely and effective interventions. This proactive approach aims to address challenges in TB management, enhance patient monitoring, and ultimately improve overall treatment efficacy. The integration of AI and data science into TB care represents promising advancement in combating one of the world’s most persistent infectious diseases.