upload hub_repos/n2c2_2018_track1/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- en
+license: other
+license_bigbio_shortname: DUA
+pretty_name: n2c2 2018 Selection Criteria
+---
+
+
+# Dataset Card for n2c2 2018 Selection Criteria
+
+## Dataset Description
+
+- **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
+- **Pubmed:** False
+- **Public:** False
+- **Tasks:** Text Classification
+
+
+Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused
+on identifying which patients in a corpus of longitudinal medical records
+meet and do not meet identified selection criteria.
+
+This shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet
+a set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (
+“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),
+temporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major
+diabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical
+narratives from the 2014 i2b2/UTHealth shared task 4.
+
+The final selected 13 selection criteria are as follows:
+1. DRUG-ABUSE: Drug abuse, current or past
+2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits
+3. ENGLISH: Patient must speak English
+4. MAKES-DECISIONS: Patient must make their own medical decisions
+5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine
+resection, or small bowel obstruction.
+6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of
+this annotation, we define “major complication” (as opposed to “minor complication”)
+as any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:
+    a. Amputation
+    b. Kidney damage
+    c. Skin conditions
+    d. Retinopathy
+    e. nephropathy
+    f. neuropathy
+7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).
+For the purposes of this annotation, we define “advanced” as having 2 or more of the following:
+    a. Taking 2 or more medications to treat CAD
+    b. History of myocardial infarction (MI)
+    c. Currently experiencing angina
+    d. Ischemia, past or present
+8. MI-6MOS: MI in the past 6 months
+9. KETO-1YR: Diagnosis of ketoacidosis in the past year
+10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months
+11. ASP-FOR-MI: Use of aspirin to prevent MI
+12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%
+13. CREATININE: Serum creatinine > upper limit of normal
+
+The training consists of 202 patient records with document-level annotations, 10 records
+with textual spans indicating annotator’s evidence for their annotations while test set contains 86.
+
+Note:
+* The inter-annotator average agreement is 84.9%
+* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.
+However, author did a simple script based validation to check if any of the tags contained any text
+in any of the training set and they do not, which confirms that atleast train and test do not
+ have any evidence tagged alongside corresponding tags.
+
+
+
+## Citation Information
+
+```
+@article{DBLP:journals/jamia/StubbsFSHU19,
+  author    = {
+                Amber Stubbs and
+                Michele Filannino and
+                Ergin Soysal and
+                Samuel Henry and
+                Ozlem Uzuner
+               },
+  title     = {Cohort selection for clinical trials: n2c2 2018 shared task track 1},
+  journal   = {J. Am. Medical Informatics Assoc.},
+  volume    = {26},
+  number    = {11},
+  pages     = {1163--1171},
+  year      = {2019},
+  url       = {https://doi.org/10.1093/jamia/ocz163},
+  doi       = {10.1093/jamia/ocz163},
+  timestamp = {Mon, 15 Jun 2020 16:56:11 +0200},
+  biburl    = {https://dblp.org/rec/journals/jamia/StubbsFSHU19.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+```