Overview of Statistical and Machine Learning Techniques for Determining Causes of Death from Verbal Autopsies: A Systematic Literature Review

Abstract

Background: The process of determining causes of death in areas where there is limited clinical services using verbal autopsies has become a key issue in terms of accuracy on cause of death (prone to errors and subjective), quality of data among many drawbacks. This is mainly because there is no proper standard available in performing verbal autopsy, even though it is important for civil registration systems and strengthening of health priorities. Physician diagnosis is the only gold standard in reviewing verbal autopsy narratives. In practice, conventional statistical methods are used to perform verbal autopsies due to their simplicity and transparency. However, in literature complex machine learning models can be found that can replace the traditional statistical methods. There has not been much application of machine learning techniques in verbal autopsy to determine cause of death, despite the advances in technology. As such, there is a need for a thorough survey of recent literature on statistical and machine learning approaches applied in verbal autopsy to determine cause of death. Methods: A systematic review was conducted and included a search from six databases. Our study only included scientific articles published in last decade that reported on verbal autopsy and: (1) algorithms; (2) statistical techniques; (3) machine learning and (4) deep learning. The search yielded 110 articles, after meta analysis, we identified 85 articles as being relevant and discarded the other 25. We investigated and compared the most commonly used statistical and machine learning techniques in VAs, identified limitations of each of these techniques, proposed a guiding machine learning framework and pointed to future directions. Results: Eighty five studies met the inclusion criteria. Apart from physician diagnosis, statistical methods are the most currently applied tools to determine cause of death from verbal autopsies. However, there has been little application of traditional machine learning and emerging techniques, even though they have shown promising results in other domains. Conclusions: Technological application of machine learning to determine cause of death, should focus on effective ideal strategies of pre-processing, transparency, robust feature engineering techniques and data balancing in order to attain optimal model performance.

Does recall time matter in verbal autopsies? Evidence from urban informal settlements in Nairobi, Kenya

“…After completion of the VA interview, data from the VA questionnaire is then interpreted to assign the cause of death. Methods for assigning the cause of death from the VA interview data vary and include the use of either physician certified verbal autopsies(PCVA) or computer coded verbal autopsy (CCVA) systems that utilise algorithms, statistical techniques, machine learning and deep learning approaches 9 , 10 . A systematic review comparing PCVAs with various CCVA systems found that although the methods differed in the cause of death output, none of the VA interpretation methods reviewed was superior to the others 9 .…”

Robust Application of Supervised Machine Learning Techniques for Cause of Death Determination from Verbal Autopsies

“…In this study we present a robust machine learning framework for determining causes of death only from VA narratives. We apply effective data cleaning strategies, data balancing to achieve optimum transparency and accuracy through addressing most model limitations and applying recommendations that are reported in [6,7,8]. We assess the robustness of several classifiers including; random forest (RF), k-nearest neighbour (KNN), decision tree (DT), support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), Naive Bayes (NB) and bagging as an ensemble classifier.…”