US Army Institute of Surgical Research, JBSA Fort Sam Houston, Texas.
Shock. 2017 Nov;48(5):504-510. doi: 10.1097/SHK.0000000000000898.
To date, there are no reviews on machine learning (ML) for predicting outcomes in trauma. Consequently, it remains unclear as to how ML-based prediction models compare in the triage and assessment of trauma patients. The objective of this review was to survey and identify studies involving ML for predicting outcomes in trauma, with the hypothesis that models predicting similar outcomes may share common features but the performance of ML in these studies will differ greatly. MEDLINE and other databases were searched for studies involving trauma and ML. Sixty-five observational studies involving ML for the prediction of trauma outcomes met inclusion criteria. In total 2,433,180 patients were included in the studies. The studies focused on prediction of the following outcome measures: survival/mortality (n = 34), morbidity/shock/hemorrhage (n = 12), hospital length of stay (n = 7), hospital admission/triage (n = 6), traumatic brain injury (n = 4), life-saving interventions (n = 5), post-traumatic stress disorder (n = 4), and transfusion (n = 1). Six studies were prospective observational studies. Of the 65 studies, 33 used artificial neural networks for prediction. Importantly, most studies demonstrated the benefits of ML models. However, algorithm performance was assessed differently by different authors. Sensitivity-specificity gap values varied greatly from 0.035 to 0.927. Notably, studies shared many features for model development. A common ML feature base may be determined for predicting outcomes in trauma. However, the impact of ML will require further validation in prospective observational studies and randomized clinical trials, establishment of common performance criteria, and high-quality evidence about clinical and economic impacts before ML can be widely accepted in practice.
迄今为止，尚无关于机器学习 (ML) 在创伤预后预测方面的综述。因此，尚不清楚基于 ML 的预测模型在创伤患者分诊和评估中的比较情况。本综述的目的是调查和确定涉及创伤 ML 预测结果的研究，假设预测相似结果的模型可能具有共同特征，但这些研究中 ML 的性能将有很大差异。我们在 MEDLINE 和其他数据库中搜索了涉及创伤和 ML 的研究。共有 65 项涉及 ML 预测创伤结局的观察性研究符合纳入标准。这些研究共纳入 2,433,180 名创伤患者。这些研究主要集中在以下预后指标的预测上：生存/死亡率 (n = 34)、发病率/休克/出血 (n = 12)、住院时间 (n = 7)、住院入院/分诊 (n = 6)、创伤性脑损伤 (n = 4)、救命干预措施 (n = 5)、创伤后应激障碍 (n = 4) 和输血 (n = 1)。其中 6 项研究为前瞻性观察性研究。在 65 项研究中，33 项研究使用人工神经网络进行预测。重要的是，大多数研究都证明了 ML 模型的优势。然而，不同作者对算法性能的评估方式不同。灵敏度-特异性差距值从 0.035 到 0.927 差异很大。值得注意的是，研究在模型开发方面具有许多共同特征。可能会为创伤预后预测确定一个共同的 ML 特征基础。然而，在实践中广泛接受 ML 之前，还需要前瞻性观察性研究和随机临床试验进一步验证其影响、建立共同的性能标准以及关于临床和经济影响的高质量证据。
