An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens

Abstract

It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited resources to the most suitable place. Traditionally, the cause-of-death inference relies on manual methods, which require a large resource cost and are not so efficient. To address the challenges, in this work, we present a mixed inference method named Sink-CF. The Sink-CF algorithm is based on confidence measurement and is used to automatically infer the underlying cause of death of citizens. The method proposed in this paper combines a mathematical statistics method and a collaborative filtering and analysis algorithm in machine learning. Thus, our method can not only effectively achieve a certain accuracy, but also does not rely on a large quantity of manually labeled data to continuously optimize the model, which can save computer computing power and time, and has the characteristics of being simple, easy and efficient. The experimental results show that our method generates a reasonable precision (93.82%) and recall (90.11%) and outperforms other state-of-the-art machine learning algorithms.

Advancing Underlying Cause of Death Inference Through Wide and Deep Model

“…However, due to country-specific variations, validating the model for cross-country applicability remains an ongoing research challenge (4). Using an attention mechanism, the model exhibited an accuracy range of 80.9% to 81.7% (11). Researchers from the Beijing Institute of Technology and China CDC developed a hybrid inference model with the Sink-CF algorithm, improving precision and recall for determining the fundamental cause of death to 93.8% and 90.1%, respectively (12).…”