序列数据机器学习模型的可操作解释：与痴呆相关的躁动用例
Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case
摘要：
机器学习已经显示出对于复杂学习问题的成功，其中数据/参数可以是多维的并且对于基于第一原理的分析来说太复杂。一些利用机器学习的应用程序需要人类的可解释性，而不仅仅是理解特定的结果（分类，检测等）。也让人类根据结果采取行动。人们已经研究了黑盒机器学习模型解释，但最近的工作重点是验证和提高模型性能。在这项工作中，一个可操作的解释黑箱机器学习模型。所提出的技术侧重于提取可操作的措施，以帮助用户做出决定或采取行动。可操作的解释可以在大多数传统的黑盒机器学习模型中实现。它使用已经训练过的模型、使用过的训练数据和数据处理技术，从模型结果及其时间序列输入中提取可操作项。可操作的解释的实现示出了一个用例：痴呆症相关的激动预测和周围环境。它示出了，可操作的项目可以被提取，如在家里的光水平，这是触发一个激动发作的减少。这种可操作解释的用例可以帮助痴呆症护理人员采取行动进行干预和预防激动。
英文摘要：
Machine learning has shown successes for complex learning problems in which data/parameters can be multidimensional and too complex for a first-principles based analysis. Some applications that utilize machine learning require human interpretability, not just to understand a particular result (classification, detection, etc.) but also for humans to take action based on that result. Black-box machine learning model interpretation has been studied, but recent work has focused on validation and improving model performance. In this work, an actionable interpretation of black-box machine learning models is presented. The proposed technique focuses on the extraction of actionable measures to help users make a decision or take an action. Actionable interpretation can be implemented in most traditional black-box machine learning models. It uses the already trained model, used training data, and data processing techniques to extract actionable items from the model outcome and its time-series inputs. An implementation of the actionable interpretation is shown with a use case: dementia-related agitation prediction and the ambient environment. It is shown that actionable items can be extracted, such as the decreasing of in-home light level, which is triggering an agitation episode. This use case of actionable interpretation can help dementia caregivers take action to intervene and prevent agitation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
