Key result
CardioEmote framework introduces 5 testable hypotheses integrating affective computing into AI cardiac risk forecasting.
Why the study?
Most AI-driven cardiac risk models rely exclusively on physiological biomarkers like ECG and HRV, omitting emotional and psychological states that regulate cardiovascular dynamics through the ANS. No unified theoretical framework currently exists that formally integrates affective computing with AI-based cardiac risk prediction.
Proposes CardioEmote, a theoretical framework and processing architecture designed to formally integrate emotional and psychological states into AI-driven cardiovascular risk prediction models.
Answers are grounded in this paper and cite their sources.
Integrating emotional states into AI cardiac risk models may enhance prediction; leaves open whether prospective validation confirms clinical utility.
Cardiovascular disease (CVD) remains a leading cause of global mortality, yet most artificial intelligence (AI)-driven cardiac risk models rely exclusively on physiological biomarkers such as electrocardiograms (ECG) and heart rate variability (HRV), systematically omitting the influence of emotional and psychological states. Psychophysiological research has firmly established that emotional stress, anxiety, and affective arousal regulate cardiovascular dynamics through the autonomic nervous system (ANS), altering HRV, blood pressure, and cardiac rhythm in clinically meaningful ways. Despite this, no unified theoretical framework currently exists that formally integrates affective computing with AIbased cardiac risk prediction. This paper proposes CardioEmote, a theoretical framework that treats emotional states as first-order modulating constructs in cardiac risk forecasting rather than as auxiliary inputs. Grounded in the Neurovisceral Integration Model (NIM), Polyvagal Theory (PVT), and the Allostatic Load Model (ALM), CardioEmote introduces four novel theoretical constructs (Affective Cardiac Modulation (ACM), Temporal Affective Burden (TAB), Emotion-Physiology Coupling Strength (EPCS), and Multimodal Fusion Fidelity (MFF)) and derives five provisional, testable research hypotheses. The framework specifies a five-stage processing architecture, a formal research model, and a structured empirical validation roadmap.
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Ashraf et al. (2026) studied Cardiovascular disease. CardioEmote theoretical framework was evaluated. CardioEmote integrates 4 novel theoretical constructs and a 5-stage processing architecture to derive 5 testable hypotheses for AI-based cardiac risk forecasting.
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