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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/11/1934 |
| _version_ | 1797510627428663296 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:34:01Z |
| format | Article |
| id | doaj.art-01cc7a365c33491e8bf35bd05d1831db |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T05:34:01Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-01cc7a365c33491e8bf35bd05d1831db2023-11-22T23:00:00ZengMDPI AGDiagnostics2075-44182021-10-011111193410.3390/diagnostics11111934Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic GroupsAndré Wirries0Florian Geiger1Ahmed Hammad2Andreas Redder3Ludwig Oberkircher4Steffen Ruchholtz5Ingmar Bluemcke6Samir Jabari7Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, GermanySpine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, GermanySpine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, GermanySpine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, GermanyCenter for Orthopaedics and Trauma Surgery, Philipps University of Marburg, Baldingerstrasse, 35043 Marburg, GermanyCenter for Orthopaedics and Trauma Surgery, Philipps University of Marburg, Baldingerstrasse, 35043 Marburg, GermanyNeuropathological Institute, University Hospitals Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyNeuropathological Institute, University Hospitals Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyPatients 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.https://www.mdpi.com/2075-4418/11/11/1934artificial intelligencesupervisedunsupervisedmachine learningmethodologyback pain |
| spellingShingle | 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 Diagnostics artificial intelligence supervised unsupervised machine learning methodology back pain |
| title | 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_short | 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 |
| topic | artificial intelligence supervised unsupervised machine learning methodology back pain |
| url | https://www.mdpi.com/2075-4418/11/11/1934 |
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