Clinical Assertion Status Detection
1 papers with code • 1 benchmarks • 1 datasets
Classifying the assertions made on given medical concepts as being present, absent, or possible in the patient, conditionally present in the patient under certain circumstances, hypothetically present in the patient at some future point, and mentioned in the patient report but associated with someoneelse. (e.g. clinical finding pertains to the patient by assigning a label such as present (”patient is diabetic”), absent (”patient denies nausea”), conditional (”dyspnea while climbing stairs”), or associated with someone else (”family history of depression”))
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Benchmarks
These leaderboards are used to track progress in Clinical Assertion Status Detection
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2010 i2b2/VA
Most implemented papers
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
JohnSnowLabs/spark-nlp-workshop
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Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient.
