Description

Artificial intelligent system for predictors of early detection of maternal, neonatal and child health risks and their timely management.

Rationale

The idea we propose is to build an artificial intelligent (AI) system for informing on predictors of early detection of maternal, neonatal and child health risks and their timely management. Tanzania is among the countries with the highest maternal mortality rates (MMR) in the world. The estimated MMR according to the 2015-2016 Tanzania Demographics and Health Survey (DHS) was 556 per 100,000.

In fact, according to the Partnership for Maternal, Newborn and Child Health (PMNCH), the maternal mortality in Tanzania has changes only slightly over the years contrary to child mortality rates which were 99 deaths per 1000 in 1999 and had become 68 per 1000 by 2005. Therefore, much needs to be done in preventing maternal mortality.

The Ministry of Health Community Development Gender  Elderly and Children (MoHCDGEC) has the District Health Information System (DHIS-2) which digitizes health data at the district level, as well as the Integrated Diseases Surveillance and Response (IDSR) for capturing weekly data on key conditions and diseases. In addition, there is the National Bureau of Statistics (NBS), which conducts and supplies data from the Demographic and Health Survey (DHS). In this proposal we intend to make use of this data present in local, national and international databases and artificial intelligence (AI) tools to build decision-making support systems.

This will involve training AI system i.e. using machine learning algorithm to be able to identify predictors of early risk detection and early risk management. Prior research in integrating AI in health care system in detecting MMR have demonstrated that AI can bring paradigm shift in reducing MMR by predicting the pregnancy outcome.

In the Tanzanian context, the most important determinant of MMR is the timing of detection of risks and their timely management. Full understanding of this aspect will be vital to the fight to reduce MMR as well as neonatal and child deaths. Therefore, our central hypothesis is that computer-based decision procedures, under the broad umbrella of artificial intelligence (AI), can assist in reducing MMR and generally improving health care in poor resources environment through detection of predictors of early risk detection and management.

The uniqueness of our hypothesis is that it will address the crux of the maternal, neonate and child deaths problem, which is what causes untimely detection and management of risks? Understanding of the predictors will help in redesigning the health care practice, management and financing around this area. The decision support tools from this proposal will be applicable in a wider scale, from members of the households, to clinicians, researchers, policymakers and maternal, neonate and child health activists.

Outcomes

The project will involve developing two AI systems (1) An AI system to be used at National level in Tanzania and (2) AI system to be used at hospital level to predict individual cases. However, for this phase (Phase one) we will focus more in the first objective of developing AI system at National level.

Therefore, gathering of data will be divided in two phases. This phase (Phase one) will involve three National platform: District Health Information System (DHIS), Integrated Diseases Surveillance and Response (IDSR)and Demographic and Health Survey (DHS) which is under National Bureau of Statistics (NBS).

DHIS and IDSR were developed in silos and so they do not communicate, they have different sets of indicators. DHIS is under the custodian of the Ministry of Health Community Development Gender, Elderly and Children (MoHCDGEC) and is an electronic tool for digitizing data at the district level. While IDSR is used for capturing weekly data on key conditions and diseases.

Secondary data concerning maternal and neonatal and child risks filtered and cleaned from DHIS, IDSR and NBC will be generated. These data will be used to generate spectrum of factor and their weights for determining the timing of risk detection and their management at national level.

Moreover, in phase two of this project individual routine data to be collected from health facilities will be used to extract factors associated with MMR threat, in order to determine the likelihood of MMR maternal, neonatal and child health risks before and after the pregnancy. This will assist the hospital management to act and intervene at individual level.

Long-term vision

Once the models have been selected they will continue being tested with more incoming data. The second phase of this project will involve development of AI system of early detection of maternal, neonatal and child health risks at hospital level, which will also be integrated to the developed predictions models and data sources at national level.

If this pilot study will show positive results. A future project will involve testing and scale up the developed tool to be used as a control intervention schemes in other areas and even to scale up for other diseases.

Personel

Dr. Gladness G. Mwanga holds a PhD in Information and Communication Science and Engineering focusing on decision-support tools using Machine learning. She is a mentor in Data science at NOTTECH Lab. In the past four years she has been working on a data science projects that gave her experience in building AI systems in solving various problems within the society. One of them she managed to develop machine learning models to predict decisions to be made by dairy farmers, identifying factors that can influence their decisions and forecast on farmers demands regarding to their specific needs or services in four Eastern Africa countries (Ethiopia, Kenya, Tanzania and Uganda). Gladness also has four years’ experience of working as a research assistant at The Nelson Mandela African Institution of Science and Technology and a consultant in developing ICT based platforms, visualization tools and oversee all activities to ensure a successful project. She’s is going to lead this project and assist in the development of an AI system.

Mr. Timothy Wikedzi is a Senior Software Engineer and a mentor at Nottech Lab, he has intensive experience in building and managing large scale software solutions. Since 2018 he has been a part of the Core team that built and support services for ShowClix Inc an Event Management and Ticketing company based in Pittsburgh USA. Prior to that Mr. Timothy has worked as Lead Tech Consultant in projects that helped to build tools and services for various Organizations in Tanzania, UK, and the USA. Timothy’s areas of interest are in building scalable solution, secured web applications, building fast and efficient systems, forming and leading teams behind software products. He  therefore brings in skills in system development.

Mr. Scott Businge is a Senior Software Engineer and a python mentor at NOTTECH lab, but He has specialized in DevOps and Software engineering with Python and Golang. He has diverse practical
experience and abilities both in software development and Operations. He is also, committed to automation, systems optimisation, security, immediate software delivery practices and monitoring processes. He has previously worked with big tech companies in Africa such Andela which offers world class Software Engineering solutions to clients around the world. He therefore brings in skills in system development using advanced python and launching of the system (DevOps).

Disclaimer

This work has been funded by Artificial Intelligence 4 Development (AI4D) programme as part of the AI4D/IndabaX Innovation Grant programme.