## 4 Discussion
The evidence presented in this paper relating to various groupings of survivors of mild stroke confirms that the stroke severity classification should not rely only on neurological functions but would benefit from incorporating cognition, mood, functional disability, physical, and social activity measures and self-perceived impact. Based on our results, we propose that an optimal approach for assessing stroke recovery would integrate multiple existing scales to provide a more comprehensive view of a patient's recovery process. While each scale mentioned in our manuscript--including the Montreal Cognitive Assessment (MoCA), Montgomery-Asberg Depression Rating Scale (MADRS), modified Rankin scale (mRS), Rapid Assessment of Physical Activity (RAPA), Work and Social Adjustment Scale (WSAS), and Stroke Impact Scale (SIS)--effectively measures specific aspects of poststroke impairment, none captures the entire recovery spectrum alone. Our findings suggest that combining these scales allows for a more nuanced profiling of stroke survivors. Furthermore, our findings indicate that at different time points, different scales were distinctive in the profiling, e.g., MoCA and MADRS on day 3-7; MADRS, mRS/SIS, and WSAS at 3 months; and MoCA/RAPA, MADRS, mRS, and SIS at 12 months. By clustering and analyzing the combined outputs of these diverse scales, we can better identify patient profiles and tailor interventions accordingly. New insights from and use of the AI clustering approach now makes profiling across multiple scales feasible for clinicians. Alternatively, this integrated approach could lead to the development of a new, comprehensive stroke assessment tool that more holistically evaluates cognitive, emotional, physical, and social recovery aspects. Such a tool could significantly enhance personalized care plans and improve overall patient outcomes. Our approach enables widening the scope of the monitoring of stroke survivors and demonstrates the value of incorporating multiple domains in the characterization of survivors of stroke. This is significant in the medium to longer term, when survivors of stroke continue to experience impairments and impact despite being classified as'mild'. Second, the identification of distinct profiles of impairment and impact at different times enables the provision of personalized and targeted care and rehabilitation to survivors of stroke focusing on the domain and profile of impairment. For example, the identification of cognitive impairment and depression in survivors of'mild' stroke may initiate treatments related to emotional health, cognition, and quality of life. This could promote intervention therapies, as the early detection of survivors of stroke with similar levels of depression could facilitate counselling and evidence-based care at early stages. Such need-based care and precautions would in turn enrich the poststroke quality of life of survivors of'mild' stroke, thereby improving the current rehabilitation and person-centered care. Third, we suggest the plausibility of integrating AI-enabled insights for decision making and designing strategies for rehabilitation that are associated with improving function and the quality of life in survivors of stroke. Using the framework presented in this paper, clinicians can input data related to different stroke assessments to visualize distinct subgroupings of stroke survivors, as shown in Appendix A. The framework is scalable to accommodate data from many patients and operate on a larger scale. While this serves as a cost-effective decision-making platform, it also categorizes stroke survivors based on the similarity of their impairments, permitting clinicians and therapists to strategically design treatment and rehabilitation programs for survivors of stroke who have similar disabilities. Using this approach in healthcare institutions to analyze poststroke patient data has practical implications: The interactive visualization tool puts the approach and resources into the hands of clinicians.