BACKGROUND In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledge base of clinical guidelines, is insufficient to support definitive optimal treatment. OBJECTIVE The goal of this study is to test the feasibility of using deep learning predictive models to identify optimal hypertension treatment pathways for individual patients, based on empirical data available from an electronic health record database. MATERIALS AND METHODS This study used data on 245,499 unique patients who were initially diagnosed with essential hypertension and received anti-hypertensive treatment from January 1, 2001 to December 31, 2010 in ambulatory care settings. We used recurrent neural networks (RNN), including long short-term memory (LSTM) and bi-directional LSTM, to create risk-adapted models to predict the probability of reaching the BP control targets associated with different BP treatment regimens. The ratios for the training set, the validation set, and the test set were 6:2:2. The samples for each set were independently randomly drawn from individual years with corresponding proportions. RESULTS The LSTM models achieved high accuracy when predicting individual probability of reaching BP goals on different treatments: for systolic BP (<140 mmHg), diastolic BP (<90 mmHg), and both systolic BP and diastolic BP (<140/90 mmHg), F1-scores were 0.928, 0.960, and 0.913, respectively. CONCLUSIONS The results demonstrated the potential of using predictive models to select optimal hypertension treatment pathways. Along with clinical guidelines and guideline-based CDS systems, the LSTM models could be used as a powerful decision-support tool to form risk-adapted, personalized strategies for hypertension treatment plans, especially for difficult-to-treat patients.

中文翻译：

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使用递归神经网络预测最佳高血压治疗途径。

背景技术在非卧床护理环境中，医生在很大程度上依赖于临床指南和基于指南的临床决策支持（CDS）系统来做出有关高血压治疗的决策。但是，目前的临床证据是临床指南的知识基础，不足以支持确定的最佳治疗。目的本研究的目的是根据可从电子病历数据库获得的经验数据，测试使用深度学习预测模型为个别患者确定最佳高血压治疗途径的可行性。材料与方法本研究使用了245499名最初被诊断为原发性高血压并在2001年1月1日至2010年12月31日期间在非卧床护理环境中接受抗高血压治疗的独特患者的数据。我们使用包括长期短期记忆（LSTM）和双向LSTM在内的递归神经网络（RNN）创建适应风险的模型，以预测达到与不同BP治疗方案相关的BP控制目标的可能性。训练集，验证集和测试集的比率为6：2：2。每组的样本均从各个年份以相应比例独立地随机抽取。结果当预测在不同治疗方法下达到血压目标的个体概率时，LSTM模型获得了很高的准确性：对于收缩压（<140 mmHg），舒张压（<90 mmHg），以及收缩压和舒张压（<140/90 mmHg） ，F1分数分别为0.928、0.960和0.913。结论结果证明了使用预测模型选择最佳高血压治疗途径的潜力。结合临床指南和基于指南的CDS系统，LSTM模型可以用作强大的决策支持工具，从而为高血压治疗计划（尤其是对于难以治疗的患者）形成适应风险的个性化策略。