Ackermann H (2008) Cerebellar contributions to speech production and speech perception: psycholinguistic and neurobiological perspectives. Trends Neurosci 31(6):265–272
Article CAS PubMed Google Scholar
Armstrong MJ, Okun MS (2020) Diagnosis and treatment of parkinson disease: a review. JAMA 323(6):548–560
Arora P et al (2019) Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health 22(4):439–445
Bang YI et al (2013) Acoustic characteristics of vowel sounds in patients with Parkinson disease. NeuroRehabilitation 32(3):649–654
Cabreira V, Massano J (2019) Parkinson’s disease: clinical review and update. Acta Med Port 32(10):661–670
Article CAS PubMed Google Scholar
Chairta PP et al (2021) Prediction of parkinson’s disease risk based on genetic profile and established risk factors. Genes (Basel) 12(8):1278
Article CAS PubMed Google Scholar
Constantinou AC et al (2016) From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif Intell Med 67:75–93
Article PubMed PubMed Central Google Scholar
da Silva JMS et al (2021) Effect of resonance tube technique on oropharyngeal geometry and voice in individuals with parkinson’s disease. J Voice 35(5):807.e25-807.e32
Dauvergne C et al (2018) Home-based training of rhythmic skills with a serious game in Parkinson’s disease: Usability and acceptability. Ann Phys Rehabil Med 61(6):380–385
Ellis TD et al (2021) Evidence for early and regular physical therapy and exercise in parkinson’s disease. Semin Neurol 41(2):189–205
Article PubMed PubMed Central Google Scholar
Fan ZX et al (2022) Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy. Front Neurosci 16:1043922
Article PubMed PubMed Central Google Scholar
Gagnon JF, Montplaisir J, Bédard MA (2002) Rapid-eye-movement sleep disorders in Parkinson’s disease. Rev Neurol (Paris) 158(2):135–152
García AM et al (2021) Cognitive determinants of dysarthria in parkinson’s disease: an automated machine learning approach. Mov Disord 36(12):2862–2873
Hammen VL, Yorkston KM (1996) Speech and pause characteristics following speech rate reduction in hypokinetic dysarthria. J Commun Disord 29(6):429–444
Article CAS PubMed Google Scholar
Hozumi H, Shimizu H (2023) Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients. PNAS Nexus. https://doi.org/10.1093/pnasnexus/pgad133
Article PubMed PubMed Central Google Scholar
Jiménez-Jiménez FJ et al (1997) Acoustic voice analysis in untreated patients with Parkinson’s disease. Parkinsonism Relat Disord 3(2):111–116
Lam PK et al (2006) Cross-cultural adaptation and validation of the Chinese Voice Handicap Index-10. Laryngoscope 116(7):1192–1198
Lévêque N et al (2022) Acoustic change over time in spastic and/or flaccid dysarthria in motor neuron diseases. J Speech Lang Hear Res 65(5):1767–1783
Liu Z et al (2020) Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network. Int J Distributed Sensor Netw 16(3):155014772091155
Meles SK, Oertel WH, Leenders KL (2021) Circuit imaging biomarkers in preclinical and prodromal Parkinson’s disease. Mol Med 27(1):111
Article CAS PubMed PubMed Central Google Scholar
Midi I et al (2008) Voice abnormalities and their relation with motor dysfunction in Parkinson’s disease. Acta Neurol Scand 117(1):26–34
Ng JSC (2018) Palliative care for Parkinson’s disease. Ann Palliat Med 7(3):296–303
Pardoel S et al (2019) Wearable-sensor-based detection and prediction of freezing of gait in parkinson’s disease: a review. Sensors (Basel) 19(23):5141
Park E, Chang HJ, Nam HS (2018) A bayesian network model for predicting post-stroke outcomes with available risk factors. Front Neurol 9:699
Article PubMed PubMed Central Google Scholar
Parnetti L et al (2019) CSF and blood biomarkers for Parkinson’s disease. Lancet Neurol 18(6):573–586
Article CAS PubMed Google Scholar
Pinto AG, Crespo AN, Mourão LF (2014) Influence of smoking isolated and associated to multifactorial aspects in vocal acoustic parameters. Braz J Otorhinolaryngol 80(1):60–67
Article PubMed PubMed Central Google Scholar
Pu T et al (2021) Lee silverman voice treatment to improve speech in parkinson’s disease: a systemic review and meta-analysis. Parkinsons Dis 2021:3366870
PubMed PubMed Central Google Scholar
Ramig L et al (2018) Speech treatment in Parkinson’s disease: randomized controlled trial (RCT). Mov Disord 33(11):1777–1791
Article CAS PubMed PubMed Central Google Scholar
Rojas S, Kefalianos E, Vogel A (2020) How does our voice change as we age? A systematic review and meta-analysis of acoustic and perceptual voice data from healthy adults over 50 years of age. J Speech Lang Hear Res 63(2):533–551
Scimeca S et al (2023) Robust and language-independent acoustic features in Parkinson’s disease. Front Neurol 14:1198058
Article PubMed PubMed Central Google Scholar
Shim SR et al (2019) Network meta-analysis: application and practice using R software. Epidemiol Health 41:e2019013
Article PubMed PubMed Central Google Scholar
Silbergleit AK et al (2021) Self-perception of voice and swallowing handicap in parkinson’s disease. J Parkinsons Dis 11(4):2027–2034
Silva LF et al (2012) Idiopathic Parkinson’s disease: vocal and quality of life analysis. Arq Neuropsiquiatr 70(9):674–679
Skodda S, Schlegel U (2008) Speech rate and rhythm in Parkinson’s disease. Mov Disord 23(7):985–992
Skodda S, Flasskamp A, Schlegel U (2010) Instability of syllable repetition as a model for impaired motor processing: is Parkinson’s disease a “rhythm disorder”? J Neural Transm (Vienna) 117(5):605–612
Stefani A, Högl B (2020) Sleep in Parkinson’s disease. Neuropsychopharmacology 45(1):121–128
留言 (0)