Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size.
Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size.
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DOI：
10.1177/23312165211066174
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发表时间：
2021-01
影响因子：
2.7
通讯作者：
Anjomshoa H
中科院分区：
文献类型：
--
作者：
Shafieibavani E;Goudey B;Kiral I;Zhong P;Jimeno-Yepes A;Swan A;Gambhir M;Buechner A;Kludt E;Eikelboom RH;Sucher C;Gifford RH;Rottier R;Plant K;Anjomshoa H
虽然人工耳蜗已经帮助了成千上万的人，但仍然很难预测个人的听力将在多大程度上受益于植入。一些出版物表明，与经典统计方法相比，机器学习可以提高人工耳蜗植入结果的预测准确性。然而，现有的研究是有限的，在模型验证和评估因素，如样本量的预测性能。我们对机器学习方法进行了彻底的检查，以预测在植入后约12个月测量的语言后听力损失成年人的单词识别分数（WRS）。这是迄今为止最大的人工耳蜗植入结局回顾性研究，评估了来自三家诊所的2，489名人工耳蜗植入者。我们证明，虽然机器学习模型在预测WRS方面明显优于线性模型，但它们的总体准确性仍然有限（平均绝对误差：17.9-21.8）。这些模型在临床队列中是稳健的，当在从训练集中排除的诊所上进行评估时，预测误差最多增加16%。我们发现，预测的改善不太可能通过单独增加样本量来改善，在组合数据集上，样本量增加一倍估计只能提高3%的性能。最后，我们展示了当前模型如何支持临床决策，强调可以识别出植入后有94%的机会将WRS改善至少10%的个体子集，这可能具有临床意义。我们讨论了这种分析的几个影响，重点是需要改进和标准化的数据收集。
While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual’s hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance. We conduct a thorough examination of machine learning approaches to predict word recognition scores (WRS) measured approximately 12 months after implantation in adults with post-lingual hearing loss. This is the largest retrospective study of cochlear implant outcomes to date, evaluating 2,489 cochlear implant recipients from three clinics. We demonstrate that while machine learning models significantly outperform linear models in prediction of WRS, their overall accuracy remains limited (mean absolute error: 17.9-21.8). The models are robust across clinical cohorts, with predictive error increasing by at most 16% when evaluated on a clinic excluded from the training set. We show that predictive improvement is unlikely to be improved by increasing sample size alone, with doubling of sample size estimated to only increasing performance by 3% on the combined dataset. Finally, we demonstrate how the current models could support clinical decision making, highlighting that subsets of individuals can be identified that have a 94% chance of improving WRS by at least 10% points after implantation, which is likely to be clinically meaningful. We discuss several implications of this analysis, focusing on the need to improve and standardize data collection.
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影响因子：
2.7
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Ching TYC;Dillon H;Leigh G;Cupples L
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Cupples L
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