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IIT Madras to predict pregnancy outcomes with analytical approaches
The Indian Institute of Technology Madras (IIT-M) will collaborate with Translational Health Science and Technology Institute (THSTI) under the Union Ministry of Science and Technology, to apply advanced analytical approaches to predict pregnancy outcomes.
Chennai
The collaboration objectives include bringing physician-scientists, biologists, engineers, and data scientists together to solve public health problems related to maternal and child health.
Other objectives include applying advanced analytical approaches for prediction of adverse pregnancy outcomes, childhood morbidity and mortality, evaluating maternal and childhood consequences of exposure to environmental pollutants, studying the role of maternal and childhood nutrition on pregnancy outcomes, immune response to vaccines and childhood morbidity and mortality, and undertaking capacity building exercises for students and young researchers of either party in the fields of public health research and data science by enabling student exchange programmes and training courses.
“The analysis of the massive data sets is a critical need for which there has, so far, been limited capacity in India,” said Gagandeep Kang, executive director, THSTI.
THSTI will identify public health and clinical needs and research gaps in maternal, neonatal and child health, design observational studies, clinical and community trials and also acquire clinical, epidemiological and biological data using standardised protocols under quality-controlled settings. It will also participate in analysis and interpretation of clinical, epidemiological and biological data.
IIT Madras will help acquire data that is complementary (such as pollution exposure data), to clinical, epidemiological and biological data that can generate novel insights. It will also provide insights based on analyses of data for the design of observational studies, clinical and community trials to answer research questions and to generate results that are generalisable to population/subjects not part of the study cohorts.
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