Developing and validating approaches to foster maternal and child health is difficult due to the interaction of biological, environmental, and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by analyzing existing data arising from multiple sources in India and formulating public health recommendations that are data-driven and cost-effective.

Data Science RFP

Developing and validating approaches to foster maternal and child health is difficult due to the interaction of biological, environmental, and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by analyzing existing data arising from multiple sources in India and formulating public health recommendations that are data-driven and cost-effective.

Data Science Approaches

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Data Science Approaches to Improve Maternal and Child Health in India

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.

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.

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

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.

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

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.

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.

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

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.

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.

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

The study aims to explore regional trends and variations in vaccine uptake, uncover relationships to other socioeconomic, demographic, and public health indicators, and develop a predictive model of the state of vaccine confidence in different parts of India. This will infer local-level confidence in vaccines by identifying areas with good access to healthcare infrastructure. The main goal of the proposal to develop a prototype coverage monitoring and forecasting system across districts by using Gaussian process methods.

The study aims to explore regional trends and variations in vaccine uptake, uncover relationships to other socioeconomic, demographic, and public health indicators, and develop a predictive model of the state of vaccine confidence in different parts of India. This will infer local-level confidence in vaccines by identifying areas with good access to healthcare infrastructure. The main goal of the proposal to develop a prototype coverage monitoring and forecasting system across districts by using Gaussian process methods.