Background and objective

Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after heart disease and cancer. Identification of individuals at risk for developing AD is imperative for testing therapeutic interventions. The objective of the study was to determine could diagnostics of AD from EMR data alone (without relying on diagnostic imaging) be significantly improved by applying clinical domain knowledge in data preprocessing and positive dataset selection rather than setting naïve filters.

Methods

Data were extracted from the repository of heterogeneous ambulatory EMR data, collected from primary care medical offices all over the U.S. Medical domain knowledge was applied to build a positive dataset from data relevant to AD. Selected Clinically Relevant Positive (SCRP) datasets were used as inputs to a Long-Short-Term Memory (LSTM) Recurrent Neural Network (RNN) deep learning model to predict will the patient develop AD.

Results

Risk scores prediction of AD using the drugs domain information in an SCRP AD dataset of 2,324 patients achieved high out-of-sample score - 0.98-0.99 Area Under the Precision-Recall Curve (AUPRC) when using 90% of SCRP dataset for training. AUPRC dropped to 0.89 when training the model using less than 1,500 cases from the SCRP dataset. The model was still significantly better than when using naïve dataset selection.

Conclusion

The LSTM RNN method that used data relevant to AD performed significantly better when learning from the SCRP dataset than when datasets were selected naïvely. The integration of qualitative medical knowledge for dataset selection and deep learning technology provided a mechanism for significant improvement of AD prediction.

Accurate and early prediction of AD is significant in the identification of patients for clinical trials, which can possibly result in the discovery of new drugs for treatments of AD. Also, the contribution of the proposed predictions of AD is a better selection of patients who need imaging diagnostics for differential diagnosis of AD from other degenerative brain disorders.

中文翻译：

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医学领域知识对深度学习对阿尔茨海默氏病预测的影响

背景和目标

阿尔茨海默氏病（AD）是最常见的痴呆类型，它会严重影响一个人进行日常活动的能力。估计表明，在心脏病和癌症之后，AD可能是导致老年人死亡的第三位原因。鉴定有发展AD风险的个体对于测试治疗干预势在必行。这项研究的目的是确定通过将临床领域知识应用于数据预处理和积极的数据集选择而不是设置简单的过滤器，可以单独改善仅凭EMR数据对AD的诊断（不依赖诊断成像）。

方法

数据是从非门诊动态EMR数据存储库中提取的，这些数据是从全美各地的初级保健医疗机构收集的。医学领域的知识被用于从与AD相关的数据中构建阳性数据集。选定的临床相关阳性（SCRP）数据集用作长期记忆（LSTM）循环神经网络（RNN）深度学习模型的输入，以预测患者是否会发展AD。

结果

使用SCRP数据集的90％进行训练时，在2,324位患者的SCRP AD数据集中使用药物域信息对AD进行风险评分预测，实现了较高的样本外得分-精确召回曲线（AUPRC）下0.98-0.99面积。使用来自SCRP数据集的少于1,500个案例训练模型时，AUPRC降至0.89。该模型仍然比使用朴素的数据集选择要好得多。

结论

从SCRP数据集学习时，使用与AD相关的数据的LSTM RNN方法的性能要比单纯选择数据集时的性能好得多。定性医学知识用于数据集选择和深度学习技术的集成提供了显着改善AD预测的机制。

AD的准确和早期预测对于临床试验患者的识别具有重要意义，这可能会导致发现用于治疗AD的新药物。同样，建议的AD预测的贡献在于可以更好地选择需要影像学诊断以区别于其他变性性脑疾病的AD患者。