Abstract:
Nearly 57 million people die annually, including over 2.7 million in the US. During the COVID-19 pandemic, institutions and government agencies use death reports to respond to communicable diseases. Even experienced doctors have trouble diagnosing death. Doctors and experts are still studying COVID-related complications, which may complicate the task. An advanced Artificial Intelligence (AI) approach is presented to help physicians accurately report causes of death based on the decedent’s last hospital discharge record. The main goal is to identify death-related conditions and clinical code causality. Clinical coding systems, medical domain knowledge constraints, and data interoperability are three issues. First, neural machine translation models with various attention mechanisms generate death cause sequences. We evaluate sequence quality using the BLEU score with three accuracy metrics. Second, we constrain death causal sequences with expert-verified medical domain knowledge. Finally, we create a Fast Healthcare Interoperability Resources (FHIR) interface to demonstrate clinical applicability. Our findings match state-of-the-art reporting and can aid physicians and public health experts during pandemics like COVID-19.
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