Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data
ObjectivesInjury prevention can be achieved through various interventions, but it faces challenges due to its comprehensive nature and susceptibility to external environmental factors, making it difficult to detect risk signals. Moreover, the reliance on standardized systems leads to the constructio...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1326457/full |
| _version_ | 1797293115928739840 |
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| author | Jin-Young Won Yu-Rim Lee Myeong-Heum Cho Yun-Tae Kim Ji-Hyang Lee |
| author_facet | Jin-Young Won Yu-Rim Lee Myeong-Heum Cho Yun-Tae Kim Ji-Hyang Lee |
| author_sort | Jin-Young Won |
| collection | DOAJ |
| description | ObjectivesInjury prevention can be achieved through various interventions, but it faces challenges due to its comprehensive nature and susceptibility to external environmental factors, making it difficult to detect risk signals. Moreover, the reliance on standardized systems leads to the construction and statistical analysis of numerous injury surveillance data, resulting in significant temporal delays before being utilized in policy formulation. This study was conducted to quickly identify substantive injury risk problems by employing text mining analysis on national emergency response data, which have been underutilized so far.MethodsWith emerging issue and topic analyses, commonly used in science and technology, we detected problematic situations and signs by deriving injury keywords and analyzing time-series changes.ResultsIn total, 65 injury keywords were identified, categorized into hazardous, noteworthy, and diffusion accidents. Semantic network analysis on hazardous accident terms refined the injury risk issues.ConclusionAn increased risk of winter epidemic fractures due to extreme weather, self-harm due to depression (especially drug overdose and self-mutilation), and falls was observed in older adults. Thus, establishing effective injury prevention strategies through inter-ministerial and interagency cooperation is necessary. |
| first_indexed | 2024-03-07T20:08:14Z |
| format | Article |
| id | doaj.art-b301fd6caaad4dacb68eda960cca2f5c |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-07T20:08:14Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-b301fd6caaad4dacb68eda960cca2f5c2024-02-28T04:26:36ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-02-011210.3389/fpubh.2024.13264571326457Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response dataJin-Young Won0Yu-Rim Lee1Myeong-Heum Cho2Yun-Tae Kim3Ji-Hyang Lee4National Disaster Management Research Institute, Ulsan, Republic of KoreaNational Disaster Management Research Institute, Ulsan, Republic of KoreaNational Disaster Management Research Institute, Ulsan, Republic of KoreaNational Disaster Management Research Institute, Ulsan, Republic of KoreaNational Fire Research Institute of Korea, Asan, Republic of KoreaObjectivesInjury prevention can be achieved through various interventions, but it faces challenges due to its comprehensive nature and susceptibility to external environmental factors, making it difficult to detect risk signals. Moreover, the reliance on standardized systems leads to the construction and statistical analysis of numerous injury surveillance data, resulting in significant temporal delays before being utilized in policy formulation. This study was conducted to quickly identify substantive injury risk problems by employing text mining analysis on national emergency response data, which have been underutilized so far.MethodsWith emerging issue and topic analyses, commonly used in science and technology, we detected problematic situations and signs by deriving injury keywords and analyzing time-series changes.ResultsIn total, 65 injury keywords were identified, categorized into hazardous, noteworthy, and diffusion accidents. Semantic network analysis on hazardous accident terms refined the injury risk issues.ConclusionAn increased risk of winter epidemic fractures due to extreme weather, self-harm due to depression (especially drug overdose and self-mutilation), and falls was observed in older adults. Thus, establishing effective injury prevention strategies through inter-ministerial and interagency cooperation is necessary.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1326457/fullinjurytext-miningEMS datanoveltyscalabilityhealth policy |
| spellingShingle | Jin-Young Won Yu-Rim Lee Myeong-Heum Cho Yun-Tae Kim Ji-Hyang Lee Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data Frontiers in Public Health injury text-mining EMS data novelty scalability health policy |
| title | Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data |
| title_full | Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data |
| title_fullStr | Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data |
| title_full_unstemmed | Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data |
| title_short | Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data |
| title_sort | ascertaining injury risk issues through big data analysis text mining based analysis of national emergency response data |
| topic | injury text-mining EMS data novelty scalability health policy |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1326457/full |
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