An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/12/8/1134 |
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| author | Xu Yang Hongsheng Ma Keyan Gao Hui Ge |
| author_facet | Xu Yang Hongsheng Ma Keyan Gao Hui Ge |
| author_sort | Xu Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:13:00Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T04:13:00Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-805cac60db0342778c75eb3aa4dc7ad82023-12-03T13:58:27ZengMDPI AGLife2075-17292022-07-01128113410.3390/life12081134An Automated Method of Causal Inference of the Underlying Cause of Death of CitizensXu Yang0Hongsheng Ma1Keyan Gao2Hui Ge3School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaThe Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaIt 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.https://www.mdpi.com/2075-1729/12/8/1134cause-of-death inferenceconfidence measurementpublic heathmedical service |
| spellingShingle | Xu Yang Hongsheng Ma Keyan Gao Hui Ge An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens Life cause-of-death inference confidence measurement public heath medical service |
| title | An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens |
| title_full | An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens |
| title_fullStr | An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens |
| title_full_unstemmed | An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens |
| title_short | An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens |
| title_sort | automated method of causal inference of the underlying cause of death of citizens |
| topic | cause-of-death inference confidence measurement public heath medical service |
| url | https://www.mdpi.com/2075-1729/12/8/1134 |
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