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.

Sebaceous glands in the buccal mucosa in patients with rheumatic disorders.

When seeking oral lesions in patients with rheumatic disorders (RD), the most frequent discernible finding in the buccal mucosa seems to be sebaceous glands (SG). The purpose of the present study was to examine whether or not SG are associated with RD, especially Reiter's syndrome (RS). Observation of SG in the buccal mucosa took place by thorough inspection of 50 males and 80 females suffering from RD.