论文标题
使用基于LSTM的多标签分类来预测特定用户的未来活动
Predicting User-specific Future Activities using LSTM-based Multi-label Classification
论文作者
论文摘要
基于先前活动的医疗保健领域中特定于用户的未来活动预测可以大大改善护士提供的服务。这是具有挑战性的，因为与其他领域不同，医疗保健的活动涉及护士和患者，并且它们的变化范围为小时。在本文中，我们采用各种数据处理技术来组织和修改数据结构和基于LSTM的多标签分类器，用于一种新型的2阶段训练方法（用户 - 不合稳定的预培训和用户特定于用户特定的微调）。我们的实验实现了31.58 \％的验证精度，精度为57.94％，召回68.31％，F1得分为60.38％。我们得出的结论是，适当的数据预处理和2阶段的培训过程可提高性能。该实验是我们团队“不喜欢本地Minima的粉丝”的“第四届护士护理活动识别挑战”的一部分。
User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses. It is challenging because, unlike other domains, activities in healthcare involve both nurses and patients, and they also vary from hour to hour. In this paper, we employ various data processing techniques to organize and modify the data structure and an LSTM-based multi-label classifier for a novel 2-stage training approach (user-agnostic pre-training and user-specific fine-tuning). Our experiment achieves a validation accuracy of 31.58\%, precision 57.94%, recall 68.31%, and F1 score 60.38%. We concluded that proper data pre-processing and a 2-stage training process resulted in better performance. This experiment is a part of the "Fourth Nurse Care Activity Recognition Challenge" by our team "Not A Fan of Local Minima".
