Neural networks to estimate multiple sclerosis disability and predict progression using routinely collected healthcare data.

Giuseppina Affinito, Marcello Moccia, Roberta Lanzillo, Ruth Ann Marrie, Jeremy Chataway, Vincenzo Brescia Morra, Raffaele Palladino
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Abstract

Background and objectives: Multiple sclerosis (MS)-related disability is conventionally measured using the Expanded Disability Status Scale (EDSS), which requires neurological examination and is generally embedded in clinical records, making it unavailable in administrative datasets. This limits its utility for population-level estimates and healthcare planning. This study aims to use routinely collected healthcare data to fill this gap.

Methods: We conducted a population-based study using administrative data from Campania Region (Italy) to develop and validate neural network algorithms to estimate MS-related disability and predict its progression (2015-2021). We employed a deep learning approach to estimate the EDSS, and a hybrid model combining survival analysis with neural network predictions to forecast the risk of EDSS progression.

Results: The model estimated EDSS with 0.68 accuracy, 0.68 precision, and 0.67 F1-score. The hybrid model had a predictive performance of 0.92. From 2016 to 2021, 9.01% of the population had EDSS ⩽ 3.0, 62.10% had EDSS between 3.5 and 5.5, and 28.89% had EDSS ⩾ 6.0. Looking at projections from 2021 to 2026, 67.68% people with EDSS ⩽ 3.0 are expected to progress to EDSS 3.5-5.5.

Conclusion: These findings highlight the potential of advanced data analytics using administrative data to improve MS monitoring, healthcare planning, and decision-making.

神经网络估计多发性硬化症残疾和预测进展使用常规收集的医疗保健数据。
背景和目的:多发性硬化症(MS)相关的残疾通常使用扩展残疾状态量表(EDSS)进行测量,该量表需要神经学检查,并且通常嵌入临床记录,使其无法在管理数据集中使用。这限制了它在人口水平估计和医疗保健计划方面的效用。本研究旨在使用常规收集的医疗保健数据来填补这一空白。方法:我们利用意大利坎帕尼亚地区的行政数据进行了一项基于人口的研究,以开发和验证神经网络算法,以估计ms相关残疾并预测其进展(2015-2021)。我们采用深度学习方法来估计EDSS,并将生存分析与神经网络预测相结合的混合模型来预测EDSS进展的风险。结果:模型估计EDSS的准确度为0.68,精密度为0.68,f1评分为0.67。混合模型的预测性能为0.92。从2016年到2021年,9.01%的人口EDSS≥3.0,62.10%的人口EDSS≥3.5至5.5,28.89%的人口EDSS≥6.0。从2021年到2026年的预测来看,67.68%的EDSS≥3.0的人预计将发展到EDSS 3.5-5.5。结论:这些发现突出了利用管理数据进行高级数据分析以改进MS监测、医疗保健计划和决策的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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