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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EVaccination app to ensure each newborn’s vaccination through trained semi-literate local people as incentivized Community Health Workers (CHWs) to find, counsel and accompany new mothers to public hospitals for vaccination
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.
Environmental exposure to antibiotics is correlated well with AMR. The present study proposes the development of high throughput screening (HTS) technique of 125 antibiotics from environmental samples like water (from river, aquifers and food sources like egg and raw meat). A novel in-vitro method will be adopted to correlate AMR with the environmental levels of antibiotics found in Delhi-NCR region and derive safe levels of antibiotics that should be permissible in the environment.
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 Inali Arm solves a problem that derails the lives of millions of people across the world. India alone is home to more than 22 million people with disabilities, with children making up nearly 8 million of that number. Due to cost and lack of accessibility, most of these people never get the help they need. The proposal intends to develop indigenous low-cost, affordable arm to for thousands of people who are living without any prosthetic care.
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.
Studies promoting egg consumption among women and children in lower middle-income countries (LMICs) show that growth indicators are significantly improved in children who consume eggs consistently. However, many states in India are chronically egg deficit, unable to fulfil their daily egg demand. The study is exploring the use of powdered-eggs in place of fresh eggs as they are cheaper and have a longer shelf life. Furthermore, the product is convenient to use, easy to transport, store and can be incorporated as an ingredient into Indian recipes and daily diets.
Wireless Capsule Endoscopy (WCE) has the potential to become the leading screening technique for the entire gastrointestinal (GI) tract. Till date, no Artificial Intelligence (AI) based diagnostic aid has been developed to assist endoscopists in identification and classification of abnormalities post the WCE procedure. The core objective of this proposal is to deliver a first-of-its-kind, multi-platform, AI-powered, fully automated diagnostic web application for real-time detection and classification of multiple abnormalities (angioectasias, aphthae, bleeding, chylous cysts, villous oedema, lymphangiectasia, polypoids, stenosis and ulcers).
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.
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.