TY - JOUR
T1 - Interpretable Deep Learning for Verbal Autopsy Analysis: A Stacked Ensemble Approach to Cause-of-Death Determination
AU - Amira Khalil
AU - Sara Fathy
AU - Mahmoud Adel
AU - Hany Nabil
AU - Rania Mostafa
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2025
VL - 4
IS - 1
SP - 64
N2 - Verbal autopsies (VA) serve as a frequent mechanism within Low- and Medium-Income Countries (LMIC) to ascertain the cause of death (CoD) for individuals who pass away outside of clinical environments, relative to the established international gold standard of physician medical certification. Field interviewers gather detailed testimonies from the families of the deceased concerning the events and circumstances preceding the fatality. This gathered information is documented in text format, referred to as VA narratives. These textual records contain granular details suitable for identifying the CoD. Nonetheless, this methodology continues to be a manual operation characterized by high costs, inconsistency, prolonged processing times, and subjectivity (making it susceptible to errors), among other limitations. Consequently, these disadvantages undermine the VA reporting infrastructure, which is otherwise crucial for defining public health agendas and populating civil registration frameworks.To address this operational gap, the present study introduces interpretable deep learning (DL) methodologies designed to analyze VA narratives and deliver CoD predictions in an efficient, transparent, economical, and precise manner. The verification of our DL models is conducted via optimization and performance accuracy machine learning (ML) curves mapped against the volume of training samples. Our models achieved training set accuracies of (LSTM = 76.11%, CNN = 76.35%, and SEDL = 82.1%), validation accuracies of (LSTM = 67.05%, CNN = 66.16%, and SEDL = 82%), and test set accuracies of (LSTM = 67%, CNN = 66.2%, and SEDL = 82%). Additionally, Local Interpretable Model-agnostic Explanations (LIME) are utilized to enhance the interpretability of the outcomes, thereby fostering confidence in automated healthcare solutions.This work offers robust deep learning strategies for CoD determination via VAs, where the stacked ensemble deep learning (SEDL) model achieved optimal performance, outperforming both Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Our empirical evidence suggests that ensemble DL paradigms can be successfully integrated into the CoD classification workflow to assist medical experts in reaching a diagnosis. Ultimately, this integration aims to compress the diagnostic turnaround time for physicians reviewing the narratives, while simultaneously reducing operational expenses and mitigating human error. The scope of this study was constrained by the volume of training data required for model optimization and the substantial lexical diversity present within the unstructured text.
UR - https://tipublications.com/s908462159
ER -