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Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.

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Challenges: Data Science Approaches

Exploring Risk Factors of Adverse Maternal and Child Health Outcomes Using Machine Learning and Other Advanced Data Analytical Approaches

Sourabh Paul, Department of Humanities and Social Sciences (New Delhi, Delhi, India)
Nov 27, 2020
Grand Challenges India> Data Science Approaches

Combining multiple data sets from HBGDKi using ML tools for prediction, classification and topic discovery may yield new insights for adverse birth outcomes and intermediate outcomes of interest. The study is based on a set of epidemiological, clinical and biochemical variables risk stratification algorithms for various adverse outcomes with practical applicability in health programme, and clinical settings may be feasible to develop using ML tools.ML can be used to suitably impute/bin missing values within datasets and merge variables from multiple datasets using robust data triangulation algorithms.

Modelling the Mortality Impact of Treatment Regimens for Antimicrobial-Resistant Neonatal Bloodstream Infections in 7 African Countries

Angela Dramowski, The University of Stellenbosch (Stellenbosch, South Africa)
Nov 20, 2020
Grand Challenges Africa> Data Science Approaches

Determining the optimal timing of antibiotic switching from "early-onset" to "late-onset" pathogen coverage and provide evidence to challenge the prevailing paradigm of providing empiric antibiotic cover based on a 72-hour cut-off.

Preterm Birth Risk in Pregnant Women – Prediction Using Machine Learning Models

Shinjini Bhatnagar, Translational Health Science and Technology Institute (Faridabad, Haryana, India)
Nov 16, 2020
Grand Challenges India> Data Science Approaches

The study proposes on pregnant women (Garbh-ini) cohort, a multidimensional longitudinal dataset purposely designed to study preterm birth. The study will apply data-driven machine learning approaches to develop an accurate and clinically useful model to predict the risk of preterm births. It will use multiple models for classification, with better objective functions and misclassification penalties that will aid in a higher rate of accurate predictions, and resampling of the data to avert biases arising from class imbalance. The primary deliverable will be dynamic prediction models that can predict, at different periods of gestation, the PTB risk using the clinical, epidemiological and imaging data.

A Data Science Approach to Develop Growth Cutoffs for Graded Care of Malnutrition

Uma Chandra Mouli, St. John's Research Institute (Bangalore, Karnataka, India)
Nov 2, 2020
Grand Challenges India> Data Science Approaches

The study aims to calculate cut-offs using data provided by HBGDki and datasets with SAS, SJRI where weight, height, and age are available for children below five years in combination with other outcomes such as death, morbidity or hospitalization. Using WHO standards, weight for height, height for age and weight for age will be calculated, and these metrics will be used as determinants for risk of death, morbidity/hospitalization. A finer categorization of malnutrition based on the risk of mortality or significant morbidity can be used to develop and then deliver tailored optimized therapeutic options for what is essentially a far more eclectic group than what is captured by a three-category classification MAM, SAM and others.

Improving Stillbirth and Neonatal Death Data Capture and Quality in Household Surveys: Big-Data Interdisciplinary Advances Using the Every Newborn-Indepth Multi-Country Survey Database

Joseph Akuze Waiswa, Makerere University College of Health Sciences, School of Public Health (Kampala, Uganda)
Sep 20, 2020
Grand Challenges Africa> Data Science Approaches

Analyzing data from the EN-INDEPTH study to develop methods for enhancing quality of household survey content and processes for measurement of stillbirths and neonatal deaths. They will do this through developing statistical process control and quality control indicators and using deep-learning approaches for pattern recognition and sequence analysis.

Child Undernutrition in India: Exploration of Nutritional Gap Based on Distal and Proximal Factors

Tinku Thomas, St. John's Research Institute (Bangalore, Karnataka, India)
Sep 16, 2020
Grand Challenges India> Data Science Approaches

Intend to adopt unconventional data analytical techniques to explore the multiple dimensions of child undernutrition in India, utilizing existing national surveys and the HBGDKi database. The novel and sophisticated analytical methods that will be used include geospatial analysis, data triangulation through statistical matching, and multilevel modeling. The idea is unconventional because India does not have a single granular and multidimensional Health and Nutrition survey that can be analyzed at the district and sub-district level to provide precision insights to public health policy.

Dietary and Environmental Mediators of Socio-Economic Inequalities in Child Undernutrition in West Africa

Adeladza Kofi Amegah, University of Cape Coast (Cape Coast, Ghana)
Aug 20, 2020
Grand Challenges Africa> Data Science Approaches

Establishing the dietary and environmental factors mediating the association of socio-economic status (education, wealth status) with childhood undernutrition (stunting, wasting, underweight and anemia) in the West African sub-region and also the mediating role of biological effects of low birth weight (LBW) in the dietary/environmental factors – childhood undernutrition pathway.

Evaluating Gestation Age Cutoff for Defining Premature Births in Africa: Mortality Prediction-Based Empiric Approach Using Data Science Computational Models Approach

Said Mohammed Ali, Ministry of Health Tanzania (Dar es Salam, Tanzania)
Aug 20, 2020
Grand Challenges Africa> Data Science Approaches

Using data from a longitudinal cohort of 5000 pregnancies with ultrasound-confirmed gestational age to develop an artificial neural network (ANN)-based model that will be able to improve prediction of gestational age using a combination of last menstrual period (LMP) and other metadata collected during the antenatal period.

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View the Grand Challenges partnership network

The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.