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 admission 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); 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. We evaluate the quality of generated sequences with the BLEU (BiLingual Evaluation Understudy) score and achieve 16.44 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.

中文翻译：

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公共卫生信息学：使用神经机器翻译提出死亡因果序列

全世界每年有近 5700 万人死亡，其中美国超过 270 万人。及时、准确和完整的死亡报告对公共卫生至关重要，因为机构和政府机构依靠死亡报告来分析生命统计数据并制定对传染病的应对措施。不准确的死亡报告可能会导致公共卫生政策的潜在误导。然而，即使对于有经验的医生来说，确定死因也是一项挑战。为了便于医生准确报告死因，我们提出了一种先进的人工智能方法，根据死者的最后一次入院出院记录，确定导致死亡的临床状况的长期有序序列。死亡报告上的临床代码序列称为死亡因果链，编码在国际疾病统计分类第十版（ICD-10）中；出院记录上优先排序的临床状况在 ICD-9 中编码。我们确定了提出死亡因果链的三个挑战：临床代码中的两个版本编码系统、医学领域知识冲突和数据互操作性。为了克服这个序列到序列问题中的第一个挑战，我们应用神经机器翻译模型来生成目标序列。我们使用 BLEU (BiLingual Evaluation Understudy) 分数评估生成序列的质量，并获得 16.44 分（满分 100 分）。为了应对第二个挑战，我们将经过专家验证的医学领域知识作为约束生成输出序列以排除不可行的因果链。最后，