Uncertainty in the estimation of the postmortem interval based on rectal temperature measurements: A Bayesian approach

Giana, Fabián Eduardo; Onetto, Martín A.; Pregliasco, R. G.

doi:10.1016/j.forsciint.2020.110505

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Confidence intervals of 95% were not reported for PMI estimation in this study. They are arguably not applicable to PMI estimations in real forensic cases because they are derived from specific experimental samples and not the whole population [37]. An accurate PMI estimation within the 95% confidence interval does not mean that there is 95% probability of finding the true PMI in an unknown case.…”

Section: Discussionmentioning

confidence: 99%

Decomposition variability between the scene and autopsy examination and implications for post-mortem interval estimations

Giles

Errickson

Márquez‐Grant

2022

Journal of Forensic and Legal Medicine

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Section: Discussionmentioning

confidence: 99%

Decomposition variability between the scene and autopsy examination and implications for post-mortem interval estimations

Giles

Errickson

Márquez‐Grant

2022

Journal of Forensic and Legal Medicine

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“…Applied techniques in human medicine. Mathematical models have been established 29,65,123 for the bi-exponential rectal cooling curve, 36 which take into consideration the initial plateau followed by an exponential drop of rectal temperature. After the plateau, the rectal cooling curve has a sigmoid shape that can be modeled.…”

Section: Body Coolingmentioning

confidence: 99%

Review of the current and potential use of biological and molecular methods for the estimation of the postmortem interval in animals and humans

et al. 2023

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We provide here an overview of the state of applied techniques in the estimation of the early period of the postmortem interval (PMI). The biological methods included consist of body cooling, CSF potassium, body cooling combined with CSF potassium, and tissue autolysis. For each method, we present its application in human and veterinary medicine and provide current methodology, strengths, and weaknesses, as well as target areas for improvement. We examine current and future molecular methods as they pertain to DNA and primarily to messenger RNA degradation for the estimation of the PMI, as well as the use of RNA in aging wounds, aging blood stains, and the identification of body fluids. Various types of RNA have different lengths, structures, and functions in cells. These differences in RNAs determine various intrinsic properties, such as their half-lives in cells, and, hence, their decay rate as well as their unique use for specific forensic tests. Future applications and refinements of RNA-based techniques provide opportunities for the use of molecular methods in the estimation of PMI and other general forensic applications.

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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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Uncertainty in the estimation of the postmortem interval based on rectal temperature measurements: A Bayesian approach

Cited by 6 publications

References 18 publications

Decomposition variability between the scene and autopsy examination and implications for post-mortem interval estimations

Decomposition variability between the scene and autopsy examination and implications for post-mortem interval estimations

Review of the current and potential use of biological and molecular methods for the estimation of the postmortem interval in animals and humans

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

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