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Natural Language Inference (NLI) is one of the critical tasks for understanding natural language. The objective of NLI is to determine if a given hypothesis can be inferred from a given premise. NLI systems have made significant progress over the years, and has gained popularity since the recent release of datasets such as the Stanford Natural Language Inference (SNLI) (Bowman et al. 2015) and Multi-NLI (Nangia et al. 2017).
We present MedNLI, a dataset for natural language inference in clinical domain that is analogous to SNLI. As the source of premise sentences, we used the MIMIC-III. More specifically, to minimize the risks to patient privacy, we worked with clinical notes corresponding to the deceased patients. The clinicians in our team suggested the Past Medical History to be the most informative section of a clinical note, from which useful inferences can be drawn about the patient.
Therefore, we segmented these notes into sections using a simple rule based program capturing the formatting of these section headers. We extracted the Past Medical History section and used a sentence splitter trained on biomedical articles from LingPipe get a pool of candidate premises. We then randomly sampled sentences from these candidates and presented them to the clinicians for annotation. The exact prompt shown to the clinicians for the annotation task is as follows.
You will be shown a sentence from Past Medical History section of a de-identified clinical note. Using only this sentence, your knowledge about the field of medicine, and common sense:
The data associated with this repository is available here: https://physionet.org/works/MIMICIIIDerivedDataRepository/files/approved/mednli/
Contributed on 2017-11-14 by Chaitanya Shivade
Source Controlled Code
Source Controlled Code Location: https://github.com/jgc128/mednli
Name Last modified Size Description
Parent Directory - mednli_code.zip 2017-11-14 16:58 51K
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Updated Friday, 28 October 2016 at 16:58 EDT