Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach

Conclusion: Quantitative results are discussed from a medical point of view, reflecting a clear translation to the clinical manifestations of PD. Moreover, results include a brief panel of non-motor symptoms that could help clinical practitioners to identify patients who are at different stages of the disease from a limited set of symptoms, such as hallucinations, fainting, inability to control body sphincters or believing in unlikely facts.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Authors: Tags: Research Articles Source Type: research