Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.
Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.
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DOI：
10.1371/journal.pone.0265254
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
TRACK-SCI Investigators
中科院分区：
文献类型：
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作者：
Chou A;Torres-Espin A;Kyritsis N;Huie JR;Khatry S;Funk J;Hay J;Lofgreen A;Shah R;McCann C;Pascual LU;Amorim E;Weinstein PR;Manley GT;Dhall SS;Pan JZ;Bresnahan JC;Beattie MS;Whetstone WD;Ferguson AR;TRACK-SCI Investigators
生物医学研究人员越来越容易获得人工智能和机器学习（AI/ML），这些研究人员具有通过优化高度准确的预测模型和更好地理解疾病生物学来改变生物医学的巨大潜力。特别是自动机器学习（AutoML），通过减少所需的人工输入和ML专业知识，使人工智能（AI）民主化。然而，AI/ML在生物医学中的成功转化需要超越仅为预测准确性进行优化，并建立可重复的临床和生物学推断。这对于罕见疾病的临床研究尤其具有挑战性，其中较小的患者队列和相应的样本量是可重现建模结果的障碍。在这里，我们提出了一个与模型无关的框架，使用可解释和可再现的AI策略和工具来加强AutoML，包括评估模型再现性的新指标。该框架使临床医生能够解释AutoML生成的模型，以实现临床和生物学验证，从而在模型开发过程中整合领域专业知识。我们将该框架应用于脊髓损伤诊断，以优化损伤相关手术期间的术中血流动力学范围，并进一步确定术中高血压与患者结局之间存在强烈的不利关系。此外，我们的分析捕捉了不断发展的临床实践，如更快的手术时间和血压管理如何影响临床模型的开发。总之，我们说明了专家增强的AutoML如何提高生物医学发现的推理再现性，并最终建立对AI过程的信任，以实现有效的临床整合。
Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.
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影响因子：
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作者：
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发表时间：
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影响因子：
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作者：
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影响因子：
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