Steps to personalized therapy of multiple sclerosis: predicting safety of treatment using mathematical modeling

Kasatkin, D. S.; Bogomolov, Yu. V.; Spirin, N. А.

doi:10.17116/jnevro201811808270

Cited by 1 publication

(2 citation statements)

References 10 publications

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“… Karim et al [ 78 ] INF- response CART; LASSO; SVM; LR; Hazard Ratio[4] in [1.359, 1.372]. Kasatkin et al [ 79 ] Flu-like symptoms NN; Static Model; Sensitivity in [73.4%, 81.2%]; Specificity in [71.6%, 80.6%]. Li et al [ 80 ] Cardiac data DT; Baseline hare rate (HR).…”

Section: Resultsmentioning

confidence: 99%

“…Omics and physiological data, together with data from medical records, were promising when applying ML to the treatment of MS. Nine studies (13.6%) examined responses of PwMS to treatment (Table 3 and Additional file 3 ). These studies analyzed responses to drugs, including interferon beta (IFNb) [ 75 , 78 , 79 , 81 , 82 ], fingolimod [ 76 , 80 ], natalizumab [ 77 ], and glatiramer acetate [ 83 ]. The Area Under the receiver operating characteristic Curve (AUC) reached over 90.0% only once [ 76 ]: this study classified micro RiboNucleic Acid (microRNA) data using random forests.…”

Section: Resultsmentioning

confidence: 99%

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The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

Hossain

Daskalaki

Brüstle

et al. 2022

BMC Med Inform Decis Mak

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Background Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. Methods Systematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms. Results Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. Conclusions ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01985-5.

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