Development of Artificial Intelligence-Based Predictive Models to Analyze the Impact of Psychosocial Profile, Fatigue, and Sleep Quality on Disease Severity in Patients With Fibromyalgia

Fibromyalgia (FM) is a condition characterized by chronic musculoskeletal pain, the pathophysiology of which remains unclear. In addition, this disorder is frequently associated with sleep disturbances, pronounced fatigue, morning stiffness, poor quality of life, cognitive alterations (primarily memory-related problems), and psychological disturbances, including depression, anxiety, and stress.

FM is significantly more prevalent in women, with an estimated female-to-male ratio of approximately 3:1. Its prevalence in the adult population is estimated at 2-3%, increasing with age and peaking between 45 and 65 years. The socioeconomic and healthcare burden of this condition is substantial, as FM is associated with high rates of medical consultations, unemployment, work disability, and the need for disability-related financial support.

FM has been linked to higher levels of negative affect, defined as a general state of distress encompassing aversive emotions such as sadness, fear, anger, and guilt. Patients with FM commonly exhibit elevated levels of anxiety, depression, pain catastrophizing, and stress, which are associated with worsening of symptoms, including cognitive impairments.

In recent years, machine learning, data mining, and artificial intelligence (AI) techniques have been successfully applied to the development of computer-aided diagnosis (CAD) systems for the identification of complex health conditions, achieving good levels of accuracy and efficiency by recognizing potentially meaningful patterns in health-related data. Accordingly, these technologies provide powerful tools for multivariable data analysis, enabling model-based predictions and offering a clear advantage in risk assessment across a wide range of diseases. In addition, these approaches not only support the development of clinical predictive models but also enhance clinicians' ability to interpret and apply their results in practice.

Explainable machine learning models allow clinical experts to make data-driven decisions and to deliver personalized treatments while maintaining a high standard of care. These models fall within the field of explainable artificial intelligence (XAI), which aims to develop interpretable models that preserve high predictive accuracy while improving the transparency, understanding, and trustworthiness of model outputs.