To detect negations of medical entities in free-text pathology reports with different approaches, and evaluate their performances.
We address the task of extracting information from free-text pathology reports, focusing on staging information encoded by the TNM (tumour-node-metastases) and ACPS (Australian clinico-pathological stage) systems.
Flatiron's oncologist-facing platform, as explained by one Forbes contributor, has differentiated itself with a dataset brimming with unstructured oncology data and features like free-text pathology reports and clinical notes.
We have demonstrated that it is possible to create an open-source deidentification program which performs well on free-text pathology reports.
The system takes free-text pathology reports, breaks them into sections, maps free-text data within these sections to NCI Metathesaurus concepts, and stores these concepts and conceptual relationships.
This work demonstrates that an open-source tool can be built which performs well in the domain of free-text pathology reports and emphasizes the challenges inherent in such reports.
However, the free text in pathology reports is primarily used as a tool for communication between clinicians to enable them to make informed management choices about their patients, not as a tool for cancer registration [ 6].
While HIPAA allows ages less than 90 years to be included in de-identified reports, we feel that it is safer to exclude all ages from the text of pathology reports if possible.
The second thing is that this book has a different approach from standard texts in pathology.
