Medical Cyberspace Subliminal Affective Collective Consciousness: Machine Learning Discriminates Back Pain vs Hip/Knee Osteoarthritis Web Pages Emotional Fingerprints

Abstract

Abstract Background - Dynamic interplay between the patients and digital information subliminal affective content may play a peculiar role in emergence of musculoskeletal degenerative chronic pain in modern society, within the combined theoretical frames of somatic marker theory and complex adaptive system theory, and cyberspace algorithm mechanism. This field of research lacks systematic investigation so far. Goal - Digital information affective content pertaining back pain was confronted with the one related to hip/knee osteoarthritis Methods - Top English internet pages related to the topics of interest were automatically selected by relevance/popularity, downloaded, then submitted to sentiment analysis; Machine Learning algorithms classified the output. Statistical association and predictivity were determined. Results - ML showed high discrimination accuracy predicting the page topic from the emotional fingerprint. The emotion Disgust emerged as a singular discriminating factor in the case study Discussion - The potential effects of disgust presence in different chronic degenerative conditions on internet texts is discussed. The potential role for a “Digital Affective Collective Consciousness” system is also discussed, and its potential contribution to psychosocial pathogenesis, maintenance and treatment outcome when biopsychosocial diseases are concerned, with implication for ethics and digital healthcare information policy