###### Abstract  
The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as'mild','moderate', or'severe' based on neurological impairment. Yet, stroke survivors argue that the classification of'mild' does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment profiles among stroke survivors classified as'mild'. We used survivors of mild stroke' data from the START longitudinal stroke cohort (n = 73), with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work, and social adjustment over 12 months. Given the multisource, multigranular, and unlabeled nature of the data, we utilized a structure-adapting, unsupervised machine learning approach, the growing self-organizing map (GSOM) algorithm, to generate distinct clinical profiles. These diverse impairment profiles revealed that survivors of mild stroke experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified'mild' stroke. This emphasizes the necessity of creating a holistic and more comprehensive representation of survivors of mild stroke' needs over the first year after stroke to improve rehabilitation and poststroke care. mild stroke; artificial intelligence; patient profiling; unsupervised learning; personalized healthcare +
Footnote †: journal:movement, and language using 11 items and was designed for use primarily in the acute phase after stroke [3]. The overall score is interpreted as a score of neurological stroke severity according to the following groupings: 0-4 as mild stroke, 5-15 as moderate stroke, 16-20 as moderate to severe stroke, and 21-42 as severe stroke [4]. This classification often remains with the stroke survivor in the later stages of recovery. Yet,'mild' stroke survivors often argue the classification of'mild' does not correspond with their daily experiences, as they report depression and difficulties in advanced physical and social activities, leading to a diminished quality of life [5,6]. Moreover, studies show that persons with mild stroke struggle to cope with the consequences of stroke, experience difficulties in everyday life [7], and may undergo persistent disability and difficulty with complex activities [8]. While it is recognized that the NIHSS is a valid and reliable screening measure, it has been reported that many acute stroke survivors with mild classification by NIHSS could have been overlooked for intensive rehabilitation therapy [9]. Given the complexity of the physical, psychological, and social burdens associated with stroke, it is important to measure the holistic impact of impairment and recovery following stroke to achieve targeted and personalized care [10,11]. This is particularly important for stroke survivors classified as'mild', as survivors of mild stroke may be investigated less due to the assumption that they are expected to regain their premorbid functionality with minimal or no intervention. This is despite evidence that even mild symptoms can impact the ability to perform daily activities and household chores [12]. Given the physical, social, emotional, and functional burden experienced by persons with mild stroke and considering the common practice of using only the NIHSS to classify stroke severity, further investigation of the latent impairments associated with'mild' stroke survivorship is required, thus leading to the question, 'Is mild really mild?' In this study, our objective was to identify various patterns in mild stroke recovery to facilitate tailored and personalized poststroke care. We aim to detect groupings of survivors of mild stroke based on their cognitive, mood, social, and physical abilities, as well as quality of life, analyzing variations in their poststroke experiences beyond the NIHSS-scale-based categorization.