Calculate Hospital Frailty Risk Score from ICD codes with two-year admission history.
npx skills add https://github.com/hannesill/m4 --skill hfrs
Please help me install this Agent Skill. Skill: hfrs Source: https://github.com/hannesill/m4/tree/main/src/m4/skills/clinical/hfrs Command: npx skills add https://github.com/hannesill/m4 --skill hfrs
This Skill enables clinicians and data scientists to compute the Hospital Frailty Risk Score (HFRS) from ICD diagnosis codes, enabling frailty assessment and risk stratification using current and prior admissions.
Provide a list of ICD-coded diagnoses for a patient and run the calculation to obtain the HFRS score.
High-intent search queries and answers about installing and using this skill.
To calculate the Hospital Frailty Risk Score (HFRS), you provide a list of ICD-10 or ICD-9 diagnosis codes from a patient's current and prior two-year emergency admissions to compute a weighted numeric score and risk category.
Yes, the HFRS calculation is explicitly designed for ICU and acute care datasets like MIMIC-IV, supporting both ICD-9-CM and ICD-10-CM diagnosis code mappings to assess patient frailty.
You can compute frailty risk scores using provided Python and SQL examples that take ICD-coded diagnoses as input, map the codes to weights, deduplicate them, and return a numeric score with matched codes.
Assessing frailty using the HFRS requires ICD diagnosis codes from the index admission plus emergency admissions from the preceding two years to accurately reflect the patient's frailty status.
Calculating the Hospital Frailty Risk Score returns a deterministic numeric score, a categorical risk level, and the specific list of contributing ICD codes matched during the computation.
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