Approach to machine learning for extraction of real-world data variables from electronic health records.

As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI's ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability. We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.

Prevalence and Predictors of Malnutrition among Guatemalan Children at 2 Years of Age.

Objective: To identify the prevalence and predictors of malnutrition among 2-year old children in the Western Highlands of Guatemala.

Methods: Prospective cohort of 852 Guatemalan children in San Lucas Toliman, Guatemala followed from birth to age 2 from May 2008 to December 2013. Socio-demographic, anthropometric, and health data of children was collected at 2 month intervals.