Table 1 Features extracted for each patient
From: The impact of payer status on hospital admissions: evidence from an academic medical center
Ontology | Number of factors | Examples |
|---|---|---|
Demongraphics | 6 | Sex; Age; Race; Marital Status; Language; Zip-Code |
Payer | 5 | Government-Sponsored (Medicare, Medicaid, Health safety net, commonwealth care); Private-sponsored (Commercial, Accident); Uninsured(Self-pay, Unknown); Other; Multiple-sponsor(Insured by both government and private sponsors) |
Primary Diagnose | 22 | e.g., Infections (ICD9: 001-139); Neoplasms (ICD9: 140-239); Endocrine (ICD9: 240-259); Nutrition (ICD9: 260-269); Blood (ICD9: 280-289); Circulatory (ICD9: 390-459); Injury&Position (ICD9: 800-999); Illdefined (ICD9: 780-799); Supplementary 1 (ICD9: V01-V91) |
Chronic Disease | 1 | e.g., Hypertensive disease (ICD9: 401.0-401.99); Diabetes mellitus (ICD9: 250.00-250.13, 250.22, 250.40-250.93); HIV (ICD9: 042.0-043.9, 079.53, v65.44); etc. |
Prescription | 1 | Whether there are prescriptions or not |
Sevice by department | 3 | Inpatient, Outpatient, Emergency Room |
Admissions | 22 | The cause of hospitalization. |