A Systematic Approach to Prioritise Diagnostically Useful Findings for Inclusion in Electronic Health Records as Discrete Data to Improve Clinical Artificial Intelligence Tools and Genomic Research.

Aims: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy. Unfortunately, much of the data contained in EHRs are not optimally structured for even the most sophisticated AI approaches. There are very few published efforts investigating methods for recording discrete data in EHRs that would not slow current clinical workflows or ways to prioritise patient characteristics worth recording.

Factors determining whether diffuse large B-cell lymphoma samples are detected by flow cytometry.

Introduction: Flow cytometry (FCM) is widely used in the diagnosis of mature B-cell neoplasms (MBN), and FCM data are usually consistent with morphological findings. However, diffuse large B-cell lymphoma (DLBCL), a common MBN, is sometimes not detected by FCM. This study aimed to explore factors that increase the likelihood of failure to detect DLBCL by FCM.