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Welcome to Nutrition Informatics

WHAT IS NUTRITION INFORMATICS?

Effective retrieval, storage, organisation, and optimum use of information, data and knowledge for food and nutrition related problem solving and decision making.

WHY NUTRITION INFORMATICS?

Nutrition transition seen with rapid urbanization, economic development and technological advancement has led to the growing need of nutrition informatics.

SMAART Hub for Informatics enabled Nutrition Education (SHINE)

Become a global platform for the field of nutrition informatics
Research, Innovate, Policy, Practice and Entrepreneurship. – Ashish Joshi

About NI Membership

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What are the benefits of Membership?
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Click below to view details.

Next Batch Starting September 26, 2026

Certificate in Health and Nutrition Informatics

  • As per UGC prescribed norms
  • Synchronous and asynchronous learning
  • Weekly interactive lectures
  • Research seminar
  • Mentorship
  • Experiential learning and much more

Our Highlights

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AI for Climate Change and Public Health Risk Prediction

Climate change is increasingly becoming a public health challenge. Heatwaves, extreme weather, air pollution, and changing rainfall patterns can affect infectious diseases, respiratory health, cardiovascular conditions, and mental wellbeing.

AI can help connect climate, environmental, population, and health data to identify patterns and predict health risks. These insights could give health systems more time to prepare, strengthen early warning systems, and protect communities most at risk.

But prediction alone is not enough. Data quality, bias, privacy, infrastructure, and the ability to translate predictions into local action remain critical challenges. Responsible AI must complement not replace public health expertise and human decision-making.

This discussion is open on COPHI (Community of Population Health Informatics), a discussion forum anchored in the SMAART framework and envisioned by Dr. @ashish_joshi_phd,MBBS,MPH 

Share your views on:
1. How can AI strengthen climate-related health risk prediction?
2. How can AI-generated warnings be translated into timely public health action?
3. What challenges must be addressed to make these systems reliable and equitable, especially in resource-limited settings?

Share your experiences, perspectives, and evidence-informed ideas.

Join the discussion: https: https://smaartlab.org/discussion-board/public-health-interventions-ai-in-public-health/ai-for-climate-change-and-public-health-risk-prediction/

#AIinPublicHealth #ClimateHealth #ClimateChange #PublicHealth #COPHI #PopulationHealth #SMAART #HealthTech #ClimateResilience #EarlyWarningSystems
This Week on SHINE: Global Nutrition & Health Updates 🌍

This week, the @who announced a major step toward improving access to quality-assured, child-friendly cancer medicines, with a new call for manufacturers to submit priority childhood cancer medicines for evaluation.

With an estimated 400,000 children and adolescents developing cancer every year, and nearly 90% living in low- and middle-income countries, improving access to appropriate medicines remains a critical global health priority.

This is the kind of global health development SHINE brings together.
Launched in 2021, SHINE is a global platform for Nutrition Informatics, connecting nutrition, information, and technology to support evidence-based practice, research, programs, and policies.

📌 Every week, SHINE brings you:
- Global health & nutrition happenings
- Emerging evidence and research
- Policy & practice updates
- Innovations and success stories

Stay informed. Stay connected. Stay ahead with SHINE.

Visit our website to know more: //nutritioninformatics.info/

#SHINE #NutritionInformatics #GlobalNutrition #PublicHealth
AI-Driven Disease Surveillance & Early Outbreak Detection

Disease outbreaks often begin with scattered warning signs across hospitals, laboratories, news, social media, environmental data, and more. AI can help connect these signals, using machine learning and natural language processing to identify unusual patterns and strengthen early warning systems.

Initiatives such as WHO’s Epidemic Intelligence from Open Sources (EIOS) demonstrate how technology can support near-real-time monitoring of public health threats.

AI can combine epidemiological data, web reports, climate patterns, wastewater surveillance, and mobility data to strengthen outbreak detection. But AI is not a replacement for epidemiologists- it is a partner. Data quality, reporting gaps, algorithmic bias, infrastructure limitations, and privacy concerns make human verification essential before AI-generated signals inform action.

This discussion is open on COPHI (Community of Population Health Informatics), a discussion forum anchored in the SMAART framework and envisioned by @ashish_joshi_phd.

Share your views:

1. How can AI be integrated into disease surveillance without compromising accuracy or trust?
2. What role should human oversight play in verifying AI-generated outbreak signals?
3. What challenges- data quality, bias, infrastructure, or others- must be addressed, especially in resource-limited settings?

Share your experiences, perspectives, and evidence-informed ideas on strengthening outbreak preparedness through responsible AI use.

Join the discussion now: https://smaartlab.org/discussion-board/public-health-interventions-ai-in-public-health/ai-driven-disease-surveillance-and-early-outbreak-detection/

#AIinPublicHealth #DiseaseSurveillance #OutbreakDetection #PublicHealth #COPHI #PopulationHealth #SMAART #HealthTech #EpidemicIntelligence #healthawareness 

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call us on +91-8527897771 or write to us contact@fhts.ac.in