With Melax Tech NLP pipelines, users can amass information on any number of conditions, co-morbidities, or cohorts within an unstructured data set.
This pipeline extracts COVID-19 related signs and symptoms defined by WHO, as well as eight associated attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership Common Data Model. (Reference)
This pipeline extracts disease mentions, together with associated modifiers including “negation,” “severity,” “uncertainty,” “condition,” “subject,” and “body location” from clinical reports. The recognized diseases will be mapped to ICD-10 CM codes.
This pipeline identifies mentions of medications as well as their signature information including “form,” “dosage,” “strength,” “route,” “duration,” and “frequency” from clinical reports. It then maps recognized medication and signature information to RxNorm codes.
This pipeline extracts comprehensive types of cancer-related information in pathology reports such as tumor size, tumor stage, and biomarkers. (Reference)
This pipeline extracts metastases-related information from pathology reports of lung cancer patients, including histological type, grade, specimen site, metastatic status indicators and the procedure.
This pipeline extracts diverse types of stressors, such as lost job, family violence, and financial difficulty, from psychiatry notes. (Reference)
This pipeline extracts genes, chemicals, and diseases from MEDLINE titles and abstracts.
This pipeline will recognize HPO terms in clinical text and map them into HPO codes.