Distilling Clinical Scores with Symbolic Machine Learning: Frailty Index, an HIV Cohort Case Study

The Frailty Index (FI) is a well-established clinical tool for assessing elderly health. Recent efforts have aimed to refine FI for diverse settings or simplify its collection, enabling more efficient population screening. Motivated by this clinical challenge, this is the first study that aims to enhance both index efficiency and practicality by proposing a data pipeline composed of: (i) a nonlinear feature selection method to identify the most relevant variables for index prediction; (ii) Multi-Objective Symbolic Regression, a symbolic machine learning technique to generate simplified index formulas that replicate the original index's values, distribution, and risk stratification ability using fewer variables and (iii) rigorous model evaluation through calibration, temporal correlation and associations with related clinical outcomes. We tailored our approach to streamlining the 53-item FI for people living with HIV (PLWH), in use at the Modena HIV Metabolic Clinic, utilizing electronic health records from a public hospital clinic providing data on about 4,800 patients. Several reduced FI (rFI) formulas were derived, with the simplest model, rFI(16), relying on just 16 indicators. Validation confirmed that the rFIs successfully replicate FI statistical properties and screening power also maintaining consistent relations with other geriatric outcomes. By facilitating index distillation from retrospective data, our method offers broad adaptability to various clinical case studies.