ENEZA-DSI/Data_mining-to-understand-the-implementation-of-Data-Science-for-Health-in-Kenya

Data_mining to understand the implementation of Data Science for Health in Kenya # Data mining to understand the implementation of Data Science for Health in Kenya ## Background The integration of data science into health systems is rapidly transforming healthcare globally, yet its adoption in sub-Saharan Africa, including Kenya, remains underexplored. This study applies data mining and natural language processing (NLP) techniques to investigate how data science is being adopted for health in Kenya. Comparative insights with other African nations and the Global North will also be explored. Through temporal, geographical, and thematic visualizations, this project offers a comprehensive and multidimensional perspective on the progress, challenges, and opportunities in the field of health data science in Kenya. ## Aim We aim to uncover key trends, dominant themes, public sentiment, and institutional collaborations in health data science field. The findings will inform stakeholders and policymakers about research gaps, collaboration patterns, and societal perception which are critical components in shaping future digital health strategies. ## Objectives We’ll be exploring published research articles, policy documents, news media, and social media discourse to understand the implementation of Data Science for Health in Kenya. 1. To identify and analyze temporal trends and thematic focus in health data science publications related to Kenya. 2. To assess public and media sentiment on data science adoption in healthcare through analysis of social media and news articles. 3. To map institutional collaborations and funding sources driving health data science research in Kenya. 4. To compare Kenya’s health data science landscape with other African countries and the Global North. ### consolidated notebook 1. Data Mining Notebook 2. Original Data Set in Excel