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geoFOR: A collaborative forensic taphonomy database for estimating the postmortem interval.
Weisensee, Katherine E; Tica, Cristina I; Atwell, Madeline M; Ehrett, Carl; Smith, D Hudson; Carbajales-Dale, Patricia; Claflin, Patrick; Nisbet, Noah.
Afiliación
  • Weisensee KE; Department of Sociology, Anthropology and Criminal Justice, Clemson University, Clemson, SC, USA. Electronic address: kweisen@clemson.edu.
  • Tica CI; Department of Anthropology and Applied Archaeology, Eastern New Mexico University, Portales, NM, USA.
  • Atwell MM; Department of Sociology, Anthropology and Criminal Justice, Clemson University, Clemson, SC, USA.
  • Ehrett C; Watt Family Innovation Center, Clemson University, Clemson, SC, USA.
  • Smith DH; Watt Family Innovation Center, Clemson University, Clemson, SC, USA.
  • Carbajales-Dale P; Clemson Center for Geospatial Technologies, Clemson University, Clemson, SC, USA.
  • Claflin P; Clemson Center for Geospatial Technologies, Clemson University, Clemson, SC, USA.
  • Nisbet N; Watt Family Innovation Center, Clemson University, Clemson, SC, USA.
Forensic Sci Int ; 355: 111934, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38277912
ABSTRACT
Accurately assessing the postmortem interval (PMI), or the time since death, remains elusive within forensic science research and application. This paper introduces geoFOR, a web-based collaborative application that utilizes ArcGIS and machine learning to deliver improved PMI predictions. The geoFOR application provides a standardized, collaborative forensic taphonomy database that gives practitioners a readily available tool to enter case information that automates the collection of environmental data and delivers a PMI prediction using statistically robust methods. After case submission, the cross-validating machine learning PMI predictive model results in a R² value of 0.82. Contributors receive a predicted PMI with an 80% confidence interval. The geoFOR database currently contains 2529 entries from across the U.S. and includes cases from medicolegal investigations and longitudinal studies from human decomposition facilities. We present the overall findings of the data collected so far and compare results from medicolegal cases and longitudinal studies to highlight previously poorly understood limitations involved in the difficult task of PMI estimation. This novel approach for building a reference dataset of human decomposition is forensically and geographically representative of the realities in which human remains are discovered which allows for continual improvement of PMI estimations as more data is captured. It is our goal that the geoFOR data repository follow the principles of Open Science and be made available to forensic researchers to test, refine, and improve PMI models. Mass collaboration and data sharing can ultimately address enduring issues associated with accurately estimating the PMI within medicolegal death investigations.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Paleontología / Cambios Post Mortem Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Forensic Sci Int Año: 2024 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Paleontología / Cambios Post Mortem Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Forensic Sci Int Año: 2024 Tipo del documento: Article Pais de publicación: Irlanda