| Autor: |
Davide Caldo, Silvia Bologna, Giorgio De Nunzio, Luana Conte, Muhammad Amin, Luca Anselma, Valerio Basile, Murad Hossain, Alessandro Mazzei, Paolo Heritier, Riccardo Ferracini, Elizaveta Kon |
| Rok vydání: |
2022 |
| Popis: |
Background - Dynamic interplay between the patient collective consciousness and the subliminal affective content of digital information may play a critical role in emergence of chronic pain, within the combined perspective of somatic marker and complex adaptive system theoretical frames Goal - Testing Machine Learning (ML) algorithms accuracy to predictively discriminate back pain vs hip/knee osteoarthritis affective fingerprints of web pages Methods - Top 2000 internet pages related to the topics of interest were selected by relevance/popularity and submitted to automated sentiment analysis; Machine Learning algorithms classified the output Results - ML showed high discrimination accuracy predicting the page topic. The emotion Disgust emerged as the key discriminating factor Discussion - The new paradigm labeled “digital affective collective consciousness” (DACC) and the role of disgust in musculoskeletal disease are discussed; DACC may play a regulatory uninvestigated role of the emergence of health/illness conditions affecting subjects and communities |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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