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                            Library & Multimedia
 Machine Learning Performance in a Microbial Molecular Autopsy Context: A Cross-sectional Postmortem Human Population Study
NCJ Number
              254118
Journal

*PLoS ONE* Volume: 14 Issue: 4 Dated: 2019
Author(s)
          Yu Zhang;
                        Jennifer L. Pechal;
                        Carl J. Schmidt;
                        Heahter R. Jordan;
                        Wesley W. Wang;
                        M. E. Barlow;
                        Sing-Hoi Sze
Date Published

2019
Length
              10 pages
Annotation
              This project compared three machine learning methods (boosted algorithms, random forests, and neural networks) for their abilities to predict the following aspects of a death investigation: postmortem interval (PMI), location where death occurred, and manner of death.
Abstract
              The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze and interpret. Machine learning methods can be useful in overcoming this analytical challenge; however, different methods use distinctive strategies to handle complex datasets. It is unclear whether one method is more appropriate than others for modeling postmortem microbiomes and their ability to predict attributes of interest in death investigations, which require understanding of how the microbial communities change after death and may represent those of the once living host. The current study collected postmortem microbiomes by swabbing five anatomical areas during routine death investigation, sequenced and analyzed from 188 death cases. A comparison of the processing of the three machine learning methods found that all algorithms performed well, but with distinct features to their performance. Xgboost often produced the most accurate predictions, but may also be more prone to overfitting. Random forest was the most stable across predictions that included more anatomic areas. Analysis of postmortem microbiota from more than three anatomic areas appears to yield limited returns on accuracy, with the eyes and rectum providing the most useful information correlating with circumstances of death in most cases for this dataset. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2014-DN-BX-K008


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Utilization of microbial structure and function in investigations of criminal justice
    
  
  
Date Published: January 1, 2019
    
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                Medicolegal death investigations
        DNA typing
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