Numerical method for solving double exponential models of corpse cooling in the determination of the time of death

Nedugov, German V.

doi:10.17116/sudmed20216406125

Cited by 4 publications

(1 citation statement)

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“…The mathematical description of the process has not changed since the introduction of the Henssge formula; however, multiple solutions for fitting empirical data using diverse methodologies have been developed: nonlinear least squares [ 21 ], conditional probability [ 22 ], Bayesian estimation [ 23 , 24 ], finite element simulation [ 25 ], Laplace transformation [ 26 ], numerical simulations [ 27 , 28 , 29 ], and neural networks [ 30 ]. A triple exponential model was another strategy to take into account; however, it did not yield the desired outcomes [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Beyond Henssge’s Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine

et al. 2023

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Henssge’s nomogram is a commonly used method to estimate the time of death. However, uncertainties arising from the graphical solution of the original mathematical formula affect the accuracy of the resulting time interval. Using existing machine learning techniques/tools such as support vector machines (SVMs) and decision trees, we present a more accurate and adaptive method for estimating the time of death compared to Henssge’s nomogram. Using the Python programming language, we built a synthetic data-driven model in which the majority of the selected tools can estimate the time of death with low error rates even despite having only 3000 training cases. An SVM with a radial basis function (RBF) kernel and AdaBoost+SVR provided the best results in estimating the time of death with the lowest error with an estimated time of death accuracy of approximately ±20 min or ±9.6 min, respectively, depending on the SVM parameters. The error in the predicted time (tp[h]) was tp±0.7 h with a 94.45% confidence interval. Because training requires only a small quantity of data, our model can be easily customized to specific populations with varied anthropometric parameters or living in different climatic zones. The errors produced by the proposed method are a magnitude smaller than any previous result.

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