Mohammadi Ramin, Jain Sarthak, Namin Amir T, Scholem Heller Melissa, Palacholla Ramya, Kamarthi Sagar, Wallace Byron
Northeastern University, Boston, MA, United States.
Tufts University School of Medicine, Boston, MA, United States.
JMIR Med Inform. 2020 Nov 27;8(11):e19761. doi: 10.2196/19761.
Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.
This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task.
We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded.
The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability.
Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.
全关节置换手术数量多、成本高，应进行成本和质量控制监测。能够识别再入院高风险患者的模型，或许可通过建议哪些患者应纳入预防保健计划来帮助降低成本。以往的风险预测模型依赖于患者的结构化数据，而非电子健康记录（EHR）中的临床记录。前一种方法需要领域专家手动提取特征，这可能会限制这些模型的适用性。
本研究旨在开发并评估一种用于预测膝关节和髋关节置换手术后30天再入院风险的机器学习模型。这些模型的输入数据来自原始EHR。我们通过实证证明，非结构化的自由文本记录包含了针对此任务的合理预测信号。
我们对2006年至2016年期间在合作伙伴医疗保健公司收集的7174例患者的数据进行了回顾性分析。这些数据被分为训练集、验证集和测试集。这些数据集用于构建、验证和测试模型，以预测出院后30天内的非计划再入院情况。所提出的模型基于临床记录进行预测，无需领域专家和机器学习专家进行手动特征提取。作为模型输入的记录由诊断和治疗患者的医生、护士、病理学家及其他人员撰写，他们可能有自己的预测，即便这些预测未被记录。
所提出的模型为每位患者输出再入院风险评分（倾向）。最佳模型（在开发集上选择）对髋关节手术的受试者工作特征曲线下面积为0.846（95%CI 82.75 - 87.11），对膝关节手术为0.822（95%CI 80.94 - 86.22），表明具有合理的区分能力。
机器学习模型可根据EHR中的记录，以合理的区分能力预测哪些患者在髋关节和膝关节置换手术后30天内有再入院的高风险。在进一步验证并通过实证证明这些模型实现了高于临床判断可能提供的预测性能后，此类模型可用于构建自动化决策支持工具，以帮助护理人员识别高危患者。
