Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning
| Autor: | Aleksandra Wierzbicka, Fabio Pizza, Francesca Canellas, António Martins da Silva, Rosa Peraita-Adrados, Ramin Khatami, Teresa Paiva, Laura Lillo-Triguero, Peter Young, Mauro Manconi, Joan Santamaria, Carles Gaig, Birgit Högl, Yves Dauvilliers, Elena Antelmi, Lucie Barateau, Alex Iranzo, Gianina Luca, Markku Partinen, Michel Lecendreux, Johannes Mathis, Geert Mayer, Carole Pesenti, Raphael Heinzer, Zhongxing Zhang, Pablo Medrano-Martínez, Claudio L. Bassetti, Giuseppe Plazzi, Christian R. Baumann, Gert Jan Lammers, Eva Feketeova, Karel Sonka, José Haba-Rubio, Rafael del Rio-Villegas, Corina Gorban, Sebastiaan Overeem, Rolf Fronczek |
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| Přispěvatelé: | Signal Processing Systems, Biomedical Diagnostics Lab, Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), University of Bologna, Centre de Recherche Saint-Antoine (UMRS893), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Zhang, Zhongxing, Mayer, Geert, Dauvilliers, Yve, Plazzi, Giuseppe, Pizza, Fabio, Fronczek, Rolf, Santamaria, Joan, Partinen, Markku, Overeem, Sebastiaan, Peraita-Adrados, Rosa, Da Silva, Antonio Martin, Sonka, Karel, Rio-Villegas, Rafael Del, Heinzer, Raphael, Wierzbicka, Aleksandra, Young, Peter, Högl, Birgit, Bassetti, Claudio L., Manconi, Mauro, Feketeova, Eva, Mathis, Johanne, Paiva, Teresa, Canellas, Francesca, Lecendreux, Michel, Baumann, Christian R., Barateau, Lucie, Pesenti, Carole, Antelmi, Elena, Gaig, Carle, Iranzo, Alex, Lillo-Triguero, Laura, Medrano-Martínez, Pablo, Haba-Rubio, José, Gorban, Corina, Luca, Gianina, Lammers, Gert Jan, Khatami, Ramin |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Multiple Sleep Latency Test
Male sueño Data Interpretation Cataplexy Databases Factual Computer science [SDV]Life Sciences [q-bio] humanos lcsh:Medicine Excessive daytime sleepiness Datasets as Topic narcolepsia Polysomnography computer.software_genre polisomnografía 0302 clinical medicine Feature (machine learning) lcsh:Science narcolepsy machine learning Multidisciplinary medicine.diagnostic_test Sleep disorders adulto Statistical Sleep Latency 3. Good health adulto joven Data Interpretation Statistical Female Supervised Machine Learning medicine.symptom enfermedades raras Adult Sleep REM 610 Medicine & health Machine learning Models Biological Article 03 medical and health sciences Databases Young Adult Rare Diseases curva ROC procesos estocásticos medicine Humans Factual Narcolepsy Stochastic Processes business.industry lcsh:R Supervised learning medicine.disease narcolepsy machine learning 030228 respiratory system ROC Curve lcsh:Q Artificial intelligence Sleep business computer 030217 neurology & neurosurgery |
| Zdroj: | Scientific reports, vol. 8, no. 1, pp. 10628 Scientific Reports Scientific Reports, 8(1):10628. Nature Publishing Group Scientific Reports, 8 Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) Agência para a Sociedade do Conhecimento (UMIC)-FCT-Sociedade da Informação instacron:RCAAP Scientific Reports, Nature Publishing Group, 2018, 8 (1), pp.10628. ⟨10.1038/s41598-018-28840-w⟩ Zhang, Zhongxing; Mayer, Geert; Dauvilliers, Yves; Plazzi, Giuseppe; Pizza, Fabio; Fronczek, Rolf; Santamaria, Joan; Partinen, Markku; Overeem, Sebastiaan; Peraita-Adrados, Rosa; da Silva, Antonio Martins; Sonka, Karel; Rio-Villegas, Rafael del; Heinzer, Raphael; Wierzbicka, Aleksandra; Young, Peter; Högl, Birgit; Bassetti, Claudio; Manconi, Mauro; Feketeova, Eva; ... (2018). Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning. Scientific Reports, 8(1), p. 10628. Nature Publishing Group 10.1038/s41598-018-28840-w Scientific Reports, Vol 8, Iss 1, Pp 1-11 (2018) Repositorio Institucional de la Consejería de Sanidad de la Comunidad de Madrid Consejería de Sanidad de la Comunidad de Madrid |
| ISSN: | 2045-2322 |
| Popis: | Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapideye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications. The EU-NN database is financed by the EU-NN. The EU-NN has received financial support from UCB Pharma Brussels for developing the EU-NN database. |
| Databáze: | OpenAIRE |
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