Classifying injury narratives of large administrative databases for surveillance—A practical approach combining machine learning ensembles and human review
研究了用支持向量机等机器学习方法对大型行政数据库中的伤害叙述进行分类，发现SVM性能很高，仅需人工审核41%的叙述，提出人机结合方法可提高效率。
=SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding.