On the Validation of Multi-Level Personalised Health Condition Model

TL;DR

This paper introduces a verification-based validation methodology for personalized health condition models, ensuring safety and reliability by detecting false alarms and vulnerabilities in IoT medical data-driven models.

Contribution

It proposes a systematic, verification-based approach to validate complex multi-level health models, addressing gaps in existing testing and model checking techniques.

Findings

Validated a multi-level Diabetes health condition model

Identified unsafe and overlapping states in the model

Demonstrated effectiveness of the validation methodology

Abstract

This paper presents a verification-based methodology to validate the model of personalized health conditions. The model identifies the values that may result in unsafe, un-reachable, in-exhaustive, and overlapping states that otherwise threaten patients' life by producing false alarms by accepting suspicious behaviour of the target health condition. Contemporary approaches to validating a model employ various testing, simulation and model checking techniques to recognise such values and corresponding vulnerabilities. However, these approaches are neither systematic nor exhaustive and thus fail to identify those false values or vulnerabilities that estimate the health condition at run-time based on the sensor or input data received from various IoT medical devices. We have demonstrated the validation methodology by validating our example multi-level model that describes three different scenarios of Diabetes health conditions.