Identifying factors that help improve existing decomposition-based PMI estimation methods.

Anna-Maria Nau, Phillip Ditto, Dawnie Wolfe Steadman, Audris Mockus
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Abstract

Accurately assessing the postmortem interval (PMI) remains a challenging task in forensic science. Existing regression models use the decomposition score to predict the PMI or accumulated degree days (ADD) but are often imprecise and rely on small sample sizes. This study explores if we can construct more accurate outdoor PMI estimation models using (a) a larger sample, (b) more sophisticated statistical models, and (c) additional predictors derived from demographic and environmental factors. Using a sample of 213 human subjects from a human decomposition photographic dataset collected at the [removed for double-blind review], we evaluated existing outdoor PMI and ADD formulae developed by Gelderman et al. [Int J Legal Med, 2018, 132, 863] and also developed more sophisticated models that incorporate additional predictors. Models using the total decomposition score (TDS), demographic factors (age, sex, and BMI), and weather-related factors (season and humidity history) reduced PMI and ADD prediction errors by over 50%. The best PMI model, incorporating TDS, demographic, and weather predictors, achieved an adjusted R-squared of 0.42 and an RMSE of 0.76. It had a 15% lower RMSE than the TDS-only model to predict PMI and a 54% lower RMSE than the pre-existing PMI formula. Similarly, the best ADD model, using the same predictors, achieved an adjusted R-squared of 0.54 and an RMSE of 0.73. It had a 10% lower RMSE than the TDS-only model to predict the ADD and a 55% lower RMSE than the pre-existing ADD formula. These results demonstrate that significant improvements in accuracy can be achieved using readily available predictors.

识别有助于改进现有基于分解的PMI估计方法的因素。
在法医科学中,准确地评估死后时间间隔(PMI)是一项具有挑战性的任务。现有的回归模型使用分解分数来预测PMI或累积度天(ADD),但往往不精确,并且依赖于小样本量。本研究探讨了我们是否可以使用(a)更大的样本,(b)更复杂的统计模型,以及(c)来自人口和环境因素的额外预测因子来构建更准确的户外PMI估计模型。我们使用了213名受试者的样本,这些受试者来自于[删除为双盲审查]收集的人体分解照片数据集,我们评估了Gelderman等人开发的现有户外PMI和ADD公式[J Legal Med, 2018, 132, 863],并开发了包含其他预测因子的更复杂的模型。使用总分解分数(TDS)、人口因素(年龄、性别和BMI)和天气相关因素(季节和湿度历史)的模型将PMI和ADD预测误差降低了50%以上。结合TDS、人口统计和天气预测因素的最佳PMI模型,调整后的r平方为0.42,RMSE为0.76。它比预测PMI的tds模型的RMSE低15%,比预先存在的PMI公式的RMSE低54%。同样,使用相同预测因子的最佳ADD模型,调整后的r平方为0.54,RMSE为0.73。它比tds模型预测ADD的RMSE低10%,比预先存在的ADD公式的RMSE低55%。这些结果表明,使用现成的预测器可以显著提高准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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