Question on the relationship between admissions from "EMERGENCY ROOM" and the MIMIC-ED

Prerequisites

• Put an X between the brackets on this line if you have done all of the following:
  • Checked the online documentation: https://mimic.mit.edu/
  • Checked that your issue isn't already addressed: https://github.com/MIT-LCP/mimic-code/issues?utf8=%E2%9C%93&q=

Description

Hi, in the admissions table of MIMIC-IV_v2.0, we noticed that there are 123,879 patients with an admission_location of "EMERGENCY ROOM", and 36,808 (29.71%) of these patients do not have any records in the MIMIC-IV-ED_v2.0 dataset. We are wondering if we should expect patients admitted through the emergency room to be present in the MIMIC-IV-ED dataset? If not, what are the differences between the patients that are included in the ED dataset and the patients that are not included? Thanks in advance!

Here are some code that we used to discover this question:

ed_diagnoses = pd.read_csv(ed_diagnoses_path)
admissions = pd.read_csv(admissions_path)
ed_admits = admissions[admissions['admission_location'] == 'EMERGENCY ROOM']

fig = plt.figure(figsize=(8, 6), dpi=100)
plt.title('Breakdown of patients with admissions that came from the "EMERGENCY ROOM"')
pts_in_ed_diagnoses = set(ed_admits['subject_id']).intersection(set(ed_diagnoses['subject_id']))
pts_not_in_ed_diagnoses = set(ed_admits['subject_id']).difference(set(ed_diagnoses['subject_id']))

assert(len(pts_in_ed_diagnoses)+len(pts_not_in_ed_diagnoses) == len(set(ed_admits['subject_id'])))
sizes = [len(pts_in_ed_diagnoses), len(pts_not_in_ed_diagnoses)]
labels = ['# of patients appeared in ed_diagnoses' + '\n(' + str(len(pts_in_ed_diagnoses)) + ')', '# of patients did not appear in ed_diagnoses' + '\n(' + str(len(pts_not_in_ed_diagnoses)) + ')']
plt.pie(sizes,labels = labels, explode = [0, 0.1], shadow = True, autopct='%1.2f%%')
plt.show()

Start with the MIMIC documentation linked in the issue, then inspect the MIMIC-IV admissions and MIMIC-IV-ED tables, especially ed_diagnoses and the admission_location field. Reproduce the provided pandas comparison and determine whether the datasets are expected to contain the same patients; done means documenting the relationship and explaining the observed difference.