Abstract：
OBJECTIVE: To utilize natural language processing (NLP) of MRI reports and various clinical variables to develop a preliminary model predictive of the need for surgery in patients with low back and neck pain. Such a model would be beneficial for informing clinical practice decisions and help reduce the number of unnecessary surgical referrals, streamlining the surgical process.
METHODS: A historical cohort study was conducted using de-identified data from patients referred to a spine assessment clinic. Various demographic, clinical, and radiological variables were included as potential predictors. Full-text radiology reports of patients\' MRI findings were vectorized using NLP before applying machine learning algorithms to develop models predicting who underwent surgery. Outputs from these models were then entered into a logistic regression model with clinical variables to develop a preliminary model predictive of surgical recommendations.
RESULTS: Of the 398 patients assessed, 71 underwent spine surgery. NLP variables were significant predictors in univariate analysis but did not remain in the final logistic regression model. An outcome of receiving surgery was predicted by a primary symptom of low back and leg pain (adjusted odds ratio 2.81), distal pain indicated by a pain diagram (adjusted odds ratio 2.49) and self-reported difficulties walking (adjusted odds ratio 2.73).
CONCLUSIONS: A logistic regression model was created to predict which patients may require spine surgery. Simple clinical variables appeared more predictive than variables created using NLP. However, additional research with more data samples is needed to validate this model and fully evaluate the usefulness of NLP for this task.
METHODS: A historical cohort study was conducted using de-identified data from patients referred to a spine assessment clinic. Various demographic, clinical, and radiological variables were included as potential predictors. Full-text radiology reports of patients\' MRI findings were vectorized using NLP before applying machine learning algorithms to develop models predicting who underwent surgery. Outputs from these models were then entered into a logistic regression model with clinical variables to develop a preliminary model predictive of surgical recommendations.
RESULTS: Of the 398 patients assessed, 71 underwent spine surgery. NLP variables were significant predictors in univariate analysis but did not remain in the final logistic regression model. An outcome of receiving surgery was predicted by a primary symptom of low back and leg pain (adjusted odds ratio 2.81), distal pain indicated by a pain diagram (adjusted odds ratio 2.49) and self-reported difficulties walking (adjusted odds ratio 2.73).
CONCLUSIONS: A logistic regression model was created to predict which patients may require spine surgery. Simple clinical variables appeared more predictive than variables created using NLP. However, additional research with more data samples is needed to validate this model and fully evaluate the usefulness of NLP for this task.
摘要：
目的：利用MRI报告的自然语言处理（NLP）和各种临床变量来开发预测下背部和颈部疼痛患者是否需要手术的初步模型。这样的模型将有利于为临床实践决策提供信息，并有助于减少不必要的手术转诊次数。简化手术过程.
方法：使用转诊到脊柱评估诊所的患者的去识别数据进行了一项历史队列研究。各种人口统计，临床,和放射学变量作为潜在预测因子。在应用机器学习算法开发预测谁接受手术的模型之前，使用NLP对患者的MRI发现的全文放射学报告进行矢量化。然后将这些模型的输出输入到具有临床变量的逻辑回归模型中，以开发预测手术建议的初步模型。
结果：在评估的398名患者中，71例接受了脊柱手术。NLP变量是单变量分析中的重要预测因子，但未保留在最终的逻辑回归模型中。接受手术的结果由下腰和腿部疼痛的主要症状预测（调整后的比值比2.81），疼痛图(调整后的比值比2.49)和自我报告的行走困难(调整后的比值比2.73)显示远端疼痛.
结论：建立了一个逻辑回归模型来预测哪些患者可能需要脊柱手术。简单的临床变量似乎比使用NLP创建的变量更具预测性。然而,需要更多数据样本的额外研究来验证该模型,并全面评估NLP对这项任务的有用性.
方法：使用转诊到脊柱评估诊所的患者的去识别数据进行了一项历史队列研究。各种人口统计，临床,和放射学变量作为潜在预测因子。在应用机器学习算法开发预测谁接受手术的模型之前，使用NLP对患者的MRI发现的全文放射学报告进行矢量化。然后将这些模型的输出输入到具有临床变量的逻辑回归模型中，以开发预测手术建议的初步模型。
结果：在评估的398名患者中，71例接受了脊柱手术。NLP变量是单变量分析中的重要预测因子，但未保留在最终的逻辑回归模型中。接受手术的结果由下腰和腿部疼痛的主要症状预测（调整后的比值比2.81），疼痛图(调整后的比值比2.49)和自我报告的行走困难(调整后的比值比2.73)显示远端疼痛.
结论：建立了一个逻辑回归模型来预测哪些患者可能需要脊柱手术。简单的临床变量似乎比使用NLP创建的变量更具预测性。然而,需要更多数据样本的额外研究来验证该模型,并全面评估NLP对这项任务的有用性.
