Identifying signs and symptoms of AL amyloidosis in electronic health records using natural language processing, diagnosis codes, and manually abstracted registry data

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These findings demonstrate that an NLP-based approach is valuable for the comprehensive capture of signs and symptoms of AL amyloidosis from EHRs. The NLP-based method matches the quality of manual curation, but it is significantly more time-efficient and cost-effective. This analysis had several limitations. First, the lists of synonyms and ICD codes may not fully capture all terms and codes used to record the signs and symptoms. Second, this analysis only considered EHR data from a single healthcare system, and further validation studies are needed to determine if these NLP algorithms can be directly used in other healthcare systems. Going forward, an NLP method for identifying signs and symptoms from clinical notes could be integrated as part of an AL amyloidosis screening / early identification tool. These tools could reduce the time between the initial presentation of AL amyloidosis to treatment of the disease.

nference Mayo Clinic Janssen Research & Development

Correspondence to:

Angela Dispenzieri (dispenzieri.angela@mayo.edu)