Lehto M R, Sorock G S
School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.
Methods Inf Med. 1996 Dec;35(4-5):309-16.
Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection) > or = 0.9, and P (false positive) < or = 0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p > 0.5 and p > 0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.
作为一种机器学习技术，贝叶斯推理被用于从描述3686起机动车碰撞事故的事故叙述中识别碰撞前活动和碰撞类型。研究假设，贝叶斯模型可以从计算机搜索与事故类别相关的63个关键词中学习。学习是根据准确分类以前无法分类的、不包含原始关键词的叙述的能力来描述的。当叙述包含关键词时，使用贝叶斯模型和关键词搜索获得的结果与专家评级密切对应（检测概率P(detection)≥0.9，误报概率P(false positive)≤0.05）。对于不包含关键词的叙述，当贝叶斯模型使用的阈值在p>0.5和p>0.9之间变化时，检测到专家指定类别的总体概率在67%至12%之间变化。误报率相应地在32%至3%之间变化。后一组结果表明，贝叶斯系统从关键词搜索的结果中进行了学习。
