Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications

Abstract Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophysiological tests. However, although previ...

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Autores principales:	Alberto Greco, Maria Rosa Chiesa, Ilaria Da Prato, Anna Maria Romanelli, Cristina Dolciotti, Gabriella Cavallini, Silvia Maria Masciandaro, Enzo Pasquale Scilingo, Renata Del Carratore, Paolo Bongioanni
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cf1770b8409c473dac85167af072cf3c
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spelling	oai:doaj.org-article:cf1770b8409c473dac85167af072cf3c2021-12-02T12:09:25ZUsing blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications10.1038/s41598-021-82940-82045-2322https://doaj.org/article/cf1770b8409c473dac85167af072cf3c2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82940-8https://doaj.org/toc/2045-2322Abstract Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophysiological tests. However, although previous studies have demonstrated the involvement of altered metabolic pathways, biomarker-based monitoring tools are still far from being applied. In this study, we aim at characterizing and discriminating patients with involvement of both upper and lower motor neurons (i.e., amyotrophic lateral sclerosis (ALS) patients) from those with selective involvement of the lower motor neuron (LMND), by using blood data exclusively. To this end, in the last ten years, we built a database including 692 blood data and related clinical observations from 55 ALS and LMND patients. Each blood sample was described by 108 analytes. Starting from this outstanding number of features, we performed a characterization of the two groups of patients through statistical and classification analyses of blood data. Specifically, we implemented a support vector machine with recursive feature elimination (SVM-RFE) to automatically diagnose each patient into the ALS or LMND groups and to recognize whether they had a fast or slow disease progression. The classification strategy through the RFE algorithm also allowed us to reveal the most informative subset of blood analytes including novel potential biomarkers of MNDs. Our results show that we successfully devised subject-independent classifiers for the differential diagnosis and prognosis of ALS and LMND with remarkable average accuracy (up to 94%), using blood data exclusively.Alberto GrecoMaria Rosa ChiesaIlaria Da PratoAnna Maria RomanelliCristina DolciottiGabriella CavalliniSilvia Maria MasciandaroEnzo Pasquale ScilingoRenata Del CarratorePaolo BongioanniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alberto Greco Maria Rosa Chiesa Ilaria Da Prato Anna Maria Romanelli Cristina Dolciotti Gabriella Cavallini Silvia Maria Masciandaro Enzo Pasquale Scilingo Renata Del Carratore Paolo Bongioanni Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
description	Abstract Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophysiological tests. However, although previous studies have demonstrated the involvement of altered metabolic pathways, biomarker-based monitoring tools are still far from being applied. In this study, we aim at characterizing and discriminating patients with involvement of both upper and lower motor neurons (i.e., amyotrophic lateral sclerosis (ALS) patients) from those with selective involvement of the lower motor neuron (LMND), by using blood data exclusively. To this end, in the last ten years, we built a database including 692 blood data and related clinical observations from 55 ALS and LMND patients. Each blood sample was described by 108 analytes. Starting from this outstanding number of features, we performed a characterization of the two groups of patients through statistical and classification analyses of blood data. Specifically, we implemented a support vector machine with recursive feature elimination (SVM-RFE) to automatically diagnose each patient into the ALS or LMND groups and to recognize whether they had a fast or slow disease progression. The classification strategy through the RFE algorithm also allowed us to reveal the most informative subset of blood analytes including novel potential biomarkers of MNDs. Our results show that we successfully devised subject-independent classifiers for the differential diagnosis and prognosis of ALS and LMND with remarkable average accuracy (up to 94%), using blood data exclusively.
format	article
author	Alberto Greco Maria Rosa Chiesa Ilaria Da Prato Anna Maria Romanelli Cristina Dolciotti Gabriella Cavallini Silvia Maria Masciandaro Enzo Pasquale Scilingo Renata Del Carratore Paolo Bongioanni
author_facet	Alberto Greco Maria Rosa Chiesa Ilaria Da Prato Anna Maria Romanelli Cristina Dolciotti Gabriella Cavallini Silvia Maria Masciandaro Enzo Pasquale Scilingo Renata Del Carratore Paolo Bongioanni
author_sort	Alberto Greco
title	Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
title_short	Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
title_full	Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
title_fullStr	Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
title_full_unstemmed	Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
title_sort	using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cf1770b8409c473dac85167af072cf3c
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Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications

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