  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.
