Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an...

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Autores principales:	André Wirries, Florian Geiger, Ahmed Hammad, Andreas Redder, Ludwig Oberkircher, Steffen Ruchholtz, Ingmar Bluemcke, Samir Jabari
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence supervised unsupervised machine learning methodology back pain Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/01cc7a365c33491e8bf35bd05d1831db
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spelling	oai:doaj.org-article:01cc7a365c33491e8bf35bd05d1831db2021-11-25T17:20:04ZCombined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups10.3390/diagnostics111119342075-4418https://doaj.org/article/01cc7a365c33491e8bf35bd05d1831db2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/1934https://doaj.org/toc/2075-4418Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.André WirriesFlorian GeigerAhmed HammadAndreas RedderLudwig OberkircherSteffen RuchholtzIngmar BluemckeSamir JabariMDPI AGarticleartificial intelligencesupervisedunsupervisedmachine learningmethodologyback painMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 1934, p 1934 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence supervised unsupervised machine learning methodology back pain Medicine (General) R5-920
spellingShingle	artificial intelligence supervised unsupervised machine learning methodology back pain Medicine (General) R5-920 André Wirries Florian Geiger Ahmed Hammad Andreas Redder Ludwig Oberkircher Steffen Ruchholtz Ingmar Bluemcke Samir Jabari Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups
description	Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.
format	article
author	André Wirries Florian Geiger Ahmed Hammad Andreas Redder Ludwig Oberkircher Steffen Ruchholtz Ingmar Bluemcke Samir Jabari
author_facet	André Wirries Florian Geiger Ahmed Hammad Andreas Redder Ludwig Oberkircher Steffen Ruchholtz Ingmar Bluemcke Samir Jabari
author_sort	André Wirries
title	Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups
title_short	Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups
title_full	Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups
title_fullStr	Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups
title_full_unstemmed	Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups
title_sort	combined artificial intelligence approaches analyzing 1000 conservative patients with back pain—a methodological pathway to predicting treatment efficacy and diagnostic groups
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/01cc7a365c33491e8bf35bd05d1831db
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Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

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