Rosenthal Sara, Das Subhro, Hsueh Pei-Yun Sabrina, Barker Ken, Chen Ching-Hua
IBM Research, Yorktown Heights, New York, USA.
MIT-IBM Watson AI Lab, IBM Research, Cambridge, Massachusetts, USA.
J Am Med Inform Assoc. 2020 Mar 6;3(1):62-9. doi: 10.1093/jamiaopen/ooaa001.
To improve efficient goal attainment of patients by analyzing the unstructured text in care manager (CM) notes (CMNs). Our task is to determine whether the goal assigned by the CM can be achieved in a timely manner.
Our data consists of CM structured and unstructured records from a private firm in Orlando, FL. The CM data is based on phone interactions between the CM and the patient. A portion of the data has been manually annotated to indicate engagement. We present 2 machine learning classifiers: an engagement model and a goal attainment model.
We can successfully distinguish automatically between engagement and lack of engagement. Subsequently, incorporating engagement and features from textual information from the unstructured notes significantly improves goal attainment classification.
Two key challenges in this task were the time-consuming annotation effort for engagement classification and the limited amount of data for the more difficult goal attainment class (specifically, for people who take a long time to achieve their goals). We successfully explore domain adaptation and transfer learning techniques to improve performance on the under-represented classes. We also explore the value of using features from unstructured notes to improve the model and interpretability.
Unstructured CMNs can be used to improve accuracy of our classification models for predicting patient self-management goal attainment. This work can be used to help identify patients who may require special attention from CMs to improve engagement in self-management.
通过分析护理经理（CM）记录（CMNs）中的非结构化文本，提高患者有效目标达成率。我们的任务是确定CM分配的目标是否能及时实现。
我们的数据包括来自佛罗里达州奥兰多市一家私人公司的CM结构化和非结构化记录。CM数据基于CM与患者之间的电话互动。一部分数据已进行人工标注以表明参与情况。我们展示了两种机器学习分类器：一种参与模型和一种目标达成模型。
我们能够成功自动区分参与和未参与情况。随后，将参与情况与非结构化记录中的文本信息特征相结合，显著提高了目标达成分类的准确性。
这项任务中的两个关键挑战是参与分类的耗时标注工作以及更困难的目标达成类别（特别是对于那些需要很长时间才能实现目标的人）的数据量有限。我们成功探索了领域适应和迁移学习技术，以提高在代表性不足类别上的性能。我们还探索了使用非结构化记录中的特征来改进模型和可解释性的价值。
非结构化CMNs可用于提高我们预测患者自我管理目标达成情况的分类模型的准确性。这项工作可用于帮助识别可能需要CM特别关注以提高自我管理参与度的患者。
