Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation

TL;DR

This paper introduces a neural machine translation approach to accurately determine the causal sequence of death from hospital records, improving public health data quality and aiding physicians in death reporting.

Contribution

It proposes a novel sequence-to-sequence neural model incorporating medical domain constraints and interoperability features to identify causal death sequences from clinical data.

Findings

Achieved BLEU score of 16.04/100 for sequence quality

Successfully integrated medical knowledge constraints

Demonstrated FHIR interface for practical usability

Abstract

Each year there are nearly 57 million deaths around the world, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical in public health, as institutions and government agencies rely on death reports to analyze vital statistics and to formulate responses to communicable diseases. Inaccurate death reporting may result in potential misdirection of public health policies. Determining the causes of death is, nevertheless, challenging even for experienced physicians. To facilitate physicians in accurately reporting causes of death, we present an advanced AI approach to determine a chronically ordered sequence of clinical conditions that lead to death, based on decedent's last hospital discharge record. The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); in line with the ICD-9-CM Official Guidelines for Coding and Reporting, the priority-ordered clinical conditions on the discharge record are coded in ICD-9. We identify three challenges in proposing the causal chain of death: two versions of coding system in clinical codes, medical domain knowledge conflict, and data interoperability. To overcome the first challenge in this sequence-to-sequence problem, we apply neural machine translation models to generate target sequence. Along with three accuracy metrics, we evaluate the quality of generated sequences with the BLEU (BiLingual Evaluation Understudy) score and achieve 16.04 out of 100. To address the second challenge, we incorporate expert-verified medical domain knowledge as constraint in generating output sequence to exclude infeasible causal chains. Lastly, we demonstrate the usability of our work in a Fast Healthcare Interoperability Resources (FHIR) interface to address the third challenge.