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: Rubén Armañanzas, Concha Bielza, Kallol Ray Chaudhuri, Pablo Martinez-Martin, Pedro Larrañaga Tags: Research Articles Source Type: research
More News: Bioinformatics | Learning | Parkinson's Disease | Psychiatry | Sleep Disorders | Sleep Medicine | Universities & Medical Training