Refining a probabilistic model for interpreting verbal autopsy data
Refining a probabilistic model for interpreting verbal autopsy data
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DOI：
10.1080/14034940510032202
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发表时间：
2006-02-01
影响因子：
3.4
通讯作者：
Van, DD
中科院分区：
文献类型：
--
作者：
Byass, P;Fottrell, E;Van, DD
目的：为了建立在以前报道的发展的贝叶斯概率模型解释死因推断（VA）的数据，试图提高模型的性能，在确定死亡原因，并重新评估it. Design：一个专家组的临床医生，来自广泛的地理和专业化方面，召开。在四天的时间里，详细审查了先前概率模型的内容，并根据需要进行了调整，以反映小组的共识。修订后的模型与来自越南的相同的189例VA病例进行了测试，由两名当地临床医生进行评估，用于测试初步模型。结果：修订后的模型共包含104个指标，可以从VA数据和34个可能的死亡原因。当应用于189例越南病例时，142例（75.1%）在模型的输出与先前的临床共识之间实现了一致性。其余47例病例（24.9%）提交给另一名独立临床医生进行重新评估。结果，28例（14.8%）临床再评估与模型输出之间达成一致; 8例（4.2%）临床再评估与原始临床意见一致，其余11例（5.8%）临床再评估、模型和原始临床意见均不同。因此，总体而言，认为该模型在170例病例（89.9%）中表现良好。结论：这种解释VA数据的方法继续显示出希望。接下来的步骤将是根据其他VA数据来源对其进行评估。专家组确定所需概率基础的办法似乎在改进模型的性能方面卓有成效。
Objective: To build on the previously reported development of a Bayesian probabilistic model for interpreting verbal autopsy ( VA) data, attempting to improve the model's performance in determining cause of death and to reassess it. Design: An expert group of clinicians, coming from a wide range geographically and in terms of specialization, was convened. Over a four-day period the content of the previous probabilistic model was reviewed in detail and adjusted as necessary to reflect the group consensus. The revised model was tested with the same 189 VA cases from Vietnam, assessed by two local clinicians, that were used to test the preliminary model. Results: The revised model contained a total of 104 indicators that could be derived from VA data and 34 possible causes of death. When applied to the 189 Vietnamese cases, 142 ( 75.1%) achieved concordance between the model's output and the previous clinical consensus. The remaining 47 cases ( 24.9%) were presented to a further independent clinician for reassessment. As a result, consensus between clinical reassessment and the model's output was achieved in 28 cases ( 14.8%); clinical reassessment and the original clinical opinion agreed in 8 cases ( 4.2%), and in the remaining 11 cases ( 5.8%) clinical reassessment, the model, and the original clinical opinion all differed. Thus overall the model was considered to have performed well in 170 cases ( 89.9%). Conclusions: This approach to interpreting VA data continues to show promise. The next steps will be to evaluate it against other sources of VA data. The expert group approach to determining the required probability base seems to have been a productive one in improving the performance of the model.