Harvard Alumni Association’s Post

Want to learn how to robustly analyse data to improve population health? More interested in helping make the world better than making the hype stronger with machine learning? Consider our masters in Population Health Data Science. Send me a message or email if you want more details. https://lnkd.in/eFMqHT3H #MachineLearning #AI #Health #Medicine #SQL

Essentials of Biostatistics in Public Health Master the basics of biostatistics to analyze research and guide impactful public health decisions. 📊🩺 Enroll at https://lnkd.in/dmkNW9KM #Biostatistics #PublicHealth #DataDriven #HealthResearch

Master the basics of biostatistics to enhance your ability to interpret research and make informed decisions in public health. 📊🔬 #Biostatistics #PublicHealth #Research #HealthData #InformedDecisions Enroll at https://lnkd.in/dmkNW9KM

Master the basics of biostatistics to enhance your ability to interpret research and make informed decisions in public health. 📊🔬 #Biostatistics #PublicHealth #Research #HealthData #InformedDecisions Enroll at https://lnkd.in/dmkNW9KM

Essentials of Biostatistics in Public Health Master the basics of biostatistics to analyze research and guide impactful public health decisions. 📊🩺 Enroll at https://lnkd.in/dmkNW9KM #Biostatistics #PublicHealth #DataDriven #HealthResearch

It is my pleasure to introduce to you the book entitled "Biostatistics Modeling and Public Health Applications", edited together with Professor Din-Chen, and part of the Springer book series on "Emerging Topics in Statistics and Biostatistics". The book was designed to empower readers with both foundational knowledge and practical skills in a wide range of models with applications in Public Health. A second volume entitled "Statistical Modeling and Applications - Multivariate, Heavy-Tailed, Skewed Distributions and Mixture Modeling" is expected to be published by the beginning of 2025. https://lnkd.in/dHtpXJxH

Key areas of focus in biostatistics include: Study Design: Developing effective methodologies for clinical trials and observational studies, ensuring that data collected is reliable and valid. Data Analysis: Using statistical techniques to analyze data sets, such as regression analysis, survival analysis, and hypothesis testing, to identify trends and associations. Interpretation: Helping researchers understand the implications of their findings, including how they relate to health outcomes and disease prevention. Public Health: Contributing to epidemiological studies that track disease outbreaks, health behaviors, and treatment effectiveness in populations.

👏 Exciting news from DATAMIND! DATAMIND, the hub for mental health informatics research development based in Population Data Science at Swansea University, continues to drive major advancements in mental health research across the UK. Find out more and read the full news piece from DATAMIND 👇 #MentalHealthResearch #FAIRData #PopulationDataScience #DATAMIND Health Data Research UK (HDR UK) HDR UK Wales

Excited to share the results of our comprehensive obesity data analysis and prediction models! 🔍💪 As part of a recent client engagement, our team dove deep into 4 crucial datasets related to adolescent and adult obesity in the US. Using advanced machine learning techniques, we were able to uncover valuable insights and make accurate predictions to help address this pressing public health issue. Some key highlights: ✅ Developed linear regression and random forest models that achieved 99% and 100% accuracy respectively in predicting obesity rates. ✅ Identified critical factors and trends influencing obesity across different demographics and geographic regions. ✅ Recommended targeted interventions and policies to combat the obesity epidemic, from nutrition education to physical activity promotion. ✅ Demonstrated the power of data-driven, ML-powered analytics in driving impactful public health decisions. The success of this project showcases how innovative data analysis can transform the way we approach complex societal challenges. By leveraging the right tools and expertise, we're able to uncover actionable insights that can lead to real, positive change. Excited to continue pushing the boundaries of what's possible with #MachineLearning and data science in the public health domain. Reach out if you'd like to learn more about our capabilities! #DataAnalytics #ObesityPrevention #HealthcareInnovation #ObesityPredictions #MachineLearning #PublicHealthAnalysis Contact me Now for Your ML and DL Related Projects

🌟 Excited to Share Another Publication for 2024! 📚 I'm thrilled to have been involved in a fascinating study that explores adolescent obesity in New Mexico using artificial intelligence techniques! 🚀 Our research, titled: "Elucidating Community-Level Determinants of Adolescent Obesity in New Mexico: An Artificial Intelligence Approach Using Bayesian Belief Networks", leverages Bayesian Belief Networks (BBNs) to uncover the complex relationships between land use and adolescent obesity across counties in the state. 🔑 Key Insights: The average prevalence of adolescent obesity was 16.6%, with certain counties like Cibola (26%), Guadalupe (21.5%), and De Baca (23.1%) experiencing significantly higher rates. Land use, modeled at a mean of 17,000 acres, plays a crucial role, with distinct patterns influencing obesity rates. We identified clusters where obesity risk was 20–32% higher than national averages, highlighting an urgent need for targeted interventions. This study underscores the importance of understanding local land use contexts when addressing public health challenges. By leveraging AI, we can guide informed decision-making for public health policies and urban planning, creating a positive impact for adolescents across New Mexico. 💡 Why It Matters: The findings pave the way for developing focused strategies to reduce obesity prevalence by addressing regional land use practices. Let's connect to discuss how AI-driven approaches can transform public health! 🌍🤝 A big shoutout to Onyedikachi Joshua Okeke and all the incredible contributors for their invaluable efforts in making this possible! 🙌 #PublicHealth #ArtificialIntelligence #UrbanPlanning #AdolescentHealth #AIInHealth #SpatialStatistics #GIS #Geospatial