Symptom progression in advanced Parkinson's disease: Dyadic perspectives

Horning, Melanie A.; Shin, Ju Young; DiFusco, Leigh Ann; Norton, Michelle; Habermann, Barbara

doi:10.1016/j.apnr.2019.151193

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…Speech deterioration is one of the motor symptoms of PD [ 14 , 24 – 26 ]. Patients have reduced pitch variability compared to controls as well as reduced intra-individual variability [ 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Gradient boosting for Parkinson’s disease diagnosis from voice recordings

Karabayir

Goldman

Pappu

et al. 2020

BMC Med Inform Decis Mak

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Background Parkinson’s Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. Method We used “Parkinson Dataset with Replicated Acoustic Features Data Set” from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson’s Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946–0.955 in 4-fold cross validation using only seven acoustic features. Conclusions Machine learning can accurately detect Parkinson’s disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson’s disease.

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Section: Methodsmentioning

confidence: 99%

Gradient boosting for Parkinson’s disease diagnosis from voice recordings

Karabayir

Goldman

Pappu

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Glottal-to-Noise Excitation Ratio (GNE), which quanti es the amount of voice excitation, was also calculated. Since PD is known to affect articulation [23] Features: Speech deterioration is one of the motor symptoms of PD [14,[24][25][26]. Patients have reduced pitch variability compared to controls as well as reduced intra-individual variability [27,28].…”

Section: Methodsmentioning

confidence: 99%

Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings

Karabayir

Goldman

Pappu

et al. 2020

Preprint

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Background: Parkinson’s Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings.Method: We used “Parkinson Dataset with Replicated Acoustic Features Data Set” from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results: The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson’s Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946-0.955 in 4-fold cross validation using only seven acoustic features.Conclusions: Machine learning can accurately detect Parkinson’s disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson’s disease.

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“…A combinação de múltiplos sintomas motores e não motores como disfunções motoras; declínio funcional, sensoriais, cognitivos; comprometimento da fala; dificuldade na deglutição; depressão; distúrbios do sono; alterações gastrintestinais e geniturinárias, bem como complicações relacionadas ao tratamento são alterações altamente complexas e comuns nos pacientes acometidos pela DP, gerando a necessidade de cuidados de uma equipe multidisciplinar de profissionais de saúde 5,6 .…”

Section: Introductionunclassified

Cuidados de enfermagem às pessoas com doença de Parkinson na Atenção primária à Saúde: protocolo de scoping review

Nunes

Álvarez

Valcarenghi

2021

Enferm. Actual Costa Rica (en línea)

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Objetivo: Identificar na literatura os principais temas investigados sobre cuidados de enfermagem à pessoa com doença de Parkinson na atenção primária à saúde. Metodologia: Trata-se de um protocolo de revisão de escopo, realizado de acordo com a metodologia do Joanna Briggs Institute e checklist do Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), seguindo as seguintes etapas: protocolo; critérios de elegibilidade; fontes de informação; estratégia de pesquisa; seleção de fontes de evidência; extração dos dados; análise de dados e apresentação dos resultados. Tem como pergunta de revisão: Quais os cuidados de enfermagem à pessoa com doença de Parkinson em nível de atenção primária à saúde, descritas na literatura? Para identificar documentos relevantes, as seguintes bases de dados bibliográficas serão pesquisadas: Public Medline, Cumulative Index to Nursing & Allied Health Literature, Web of Science, SciVerse Scopus, Base de Dados de Enfermagem, Literatura Latino-Americano e do Caribe em Ciências da Saúde, e Scientific Electronic Library Online. O resultado do fluxo de seleção, desta etapa metodológica, será apresentado em forma de figura, conforme o Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Resultados: Os resultados deste estudo possibilitarão o conhecimento das principais áreas de cuidados e intervenções de enfermagem voltadas a pessoas com doença de Parkinson, na atenção primária à saúde. Conclusão: Este estudo constitui o primeiro passo em uma agenda de pesquisa que visa aprofundar a análise e sistematização dos cuidados de enfermagem às pessoas que vivem com doença de Parkinson.

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Symptom progression in advanced Parkinson's disease: Dyadic perspectives

Cited by 12 publications

References 21 publications

Gradient boosting for Parkinson’s disease diagnosis from voice recordings

Gradient boosting for Parkinson’s disease diagnosis from voice recordings

Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings

Cuidados de enfermagem às pessoas com doença de Parkinson na Atenção primária à Saúde: protocolo de scoping review

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