Application of Artificial Intelligence and Machine Learning techniques to demographic, clinical and behavioural data to generate clinical knowledge in the understanding and preserving of brain health

One of the significant challenges of the human health science in the last century is the fight against neurological disorders. Indeed, while it is true that life expectancy has undergone enormous growth over the years that does not seem destined to slow down, it is also a fact that such extension of lifespan does not match with similar increases in heathy lifespan. Therefore, the promotion of brain health makes their way widely in scientific research to avoid, or at least, delay the onset of brain diseases. Some of this kind of studies examine the influence of life habits and genetics on the maintenance of the brain health. In this line of enquiry, there is the Barcelona Brain Health Initiative (BBHI) carried on by the Institut Guttmann, a neurorehabilitation hospital. This ongoing study aims to investigate which lifestyle, behavioral, environmental, and cognitive markers are associated with cognitive and mental tasks and can influence brain health in middle to old age. The BBHI started in 2017 and it consists of three phases: in Phase I a sample of 4206 healthy volunteers between 40 and 65 years old, of both sexes, are engaged through the creation of a personal profile in a dedicated web-based platform, and periodically asked to answer to online questionnaires about their personal information, health, and lifestyles in the 7 pillars hypothesized to be linked to brain health (physical health, nutrition, physical activity, cognitive activity, sleep, socialization and purpose in life); in Phase II a subsample of 1000 participants is undergoing some clinical in-person assessments to evaluate their neuropsychological health; in Phase III, a subsample of 500 participants will undergo a non-pharmacological clinical trial of a multi-dimensional intervention to analyze its impact on improving metrics related to brain health. This Master’s thesis project will apply Artificial Intelligence techniques to analyze the data collected during the first year of the BBHI study to identify key factors in maintaining brain health. Finally, it will apply Machine Learning algorithms to predict the results of the neuropsychological assessments carried out during phase II, from the responses collected in the Phase I through self-administered online questionnaires. These models could offer a valid alternative method of clinical evaluation that is simpler, more efficient, and more affordable.