Seccia Ruggiero, Romano Silvia, Salvetti Marco, Crisanti Andrea, Palagi Laura, Grassi Francesca
Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, 00185 Rome, Italy.
Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, Italy.
Life (Basel). 2021 Feb 5;11(2):122. doi: 10.3390/life11020122.
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.
多发性硬化症的病程始于复发缓解期，在一段极不固定的时期内逐渐发展为继发进展型，这取决于许多因素，每个因素都有细微的影响。迄今为止，尚未有经过验证的预后因素或风险评分可用于预测个体的疾病进程。这愈发令人沮丧，因为尽管可能存在相关的不良反应，尤其是对于更有效的药物，但有几种治疗方法可以预防复发并减缓疾病进展，甚至能持续很长时间。对疾病进程的早期预测将有助于根据疾病预期的侵袭性来区分治疗方法，为风险更高的患者保留高影响力的疗法。鉴于其他方法均告失败，目前正在尝试基于机器学习（ML）算法的方法来提高预后能力。在此，我们回顾了最近使用临床数据单独或与其他类型数据相结合来推导预后模型的研究。文中描述了几种已被使用和比较的算法。尽管尚无研究提出临床上可用的模型，但相关知识正在积累，未来可能会出现强大的工具。
