Finding Needles in the Haystack: Identifying Patients with Rare Subtype of Multiple Myeloma Supported by a Data Warehouse and Information Extraction.
Read┬áthe original article.
- Chair for Artificial Intelligence and Applied Informatics, University of W├╝rzburg.
- Department of Medicine II, University Hospital W├╝rzburg.
- Service-center Medical Informatics, University Hospital W├╝rzburg.
- Comprehensive Heart Failure Center, University Hospital W├╝rzburg.
Finding patient cases with extremely rare pathologies is a laborious task. To decrease time spent on manually searching through thousands of discharge letters and reports, a data warehouse with a fast fulltext search index was queried. Our use case is to find “macrofocal myeloma”, i.e. Multiple Myeloma patients with few large lesions. We guessed the number of those patients in the University Hospital W├╝rzburg at about 20. Most criteria were available in the data warehouse in an unstructured form requiring information extraction. 8 patient cases were found by searching for different spellings of “macrofocal myeloma” in discharge letters directly. With an indirect search combining several criteria, we found additional 23 candidate patient cases, from which 10 were classified by a domain expert as correct. The most difficult criteria were determining the degree of bone marrow infiltration. We achieved an F1 score of 93.2 % for this task. The number of patient cases to be screened manually for this disease decreased from about 25000 to 23.
Macrofocal myeloma; NLP; data warehouse; information extraction
- PMID: 30147064
[Indexed for MEDLINE]