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首页> 外文期刊>BMC Medical Informatics and Decision Making >Automatically determining cause of death from verbal autopsy narratives
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Automatically determining cause of death from verbal autopsy narratives

机译：通过口头尸检叙述自动确定死亡原因

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A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person’s death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone. After preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: na?ve Bayes, random forest, support vector machines, and a neural network. For individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods. Our narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.
机译：口头尸检（VA）是在医生未确定官方死亡原因（CoD）的情况下，对人死亡前症状的事后书面访谈报告。当前领先的自动VA编码方法主要使用VA中的结构化数据来分配CoD类别。我们提出一种仅根据VA自由文本叙述自动确定CoD类别的方法。经过预处理和拼写校正后，我们的方法从叙述中提取词频计数，并将其用作四个不同机器学习分类器的输入：朴素贝叶斯，随机森林，支持向量机和神经网络。对于单独的CoD分类，我们的最佳分类器对15种CoD类别的成年人死亡灵敏度为770（与目前最佳报道的灵敏度为57）相比，对于48种WHO类别，灵敏度为662。当在人口水平上预测CoD分布时，我们的最佳分类器可实现15种类别的962种特定原因死亡率分数准确性和48种类别的908种原因致死率准确性，与领先的CoD分布估计方法相当。我们基于叙事的机器学习分类器的性能与基于个体级别的结构化数据的分类器一样好。此外，我们的方法证明了VA叙事提供了重要信息，机器学习系统可以使用这些信息进行CoD自动分类。与基于结构化问卷的方法不同，此方法可以应用于任何口头尸检数据集，而无论收集过程或原籍国如何。

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