Hassan Farhat, Guillaume Alinier, Mariana Helou, Ionnais Galatis, Nidaa Bajow, Denis Jose, Sarra Jouini, Sermet Sezigen, Samia Hafi, Sheena Mccabe, Naoufel Somrani, Kawther El Aifa, Henda Chebbi, Asma Ben Amor, Yosra Kerkeni, Ahmed M Al-Wathinani, Nassem Mohammed Abdulla, Ammar Abdulrahman Jairoun, Brendon Morris, Nicholas Castle, Loua Al-Sheikh, Walid Abougalala, Mohamed Ben Dhiab, James Laughton
{"title":"中东和北非地区防备化学、生物、辐射和核威胁的视角:人工智能技术的应用。","authors":"Hassan Farhat, Guillaume Alinier, Mariana Helou, Ionnais Galatis, Nidaa Bajow, Denis Jose, Sarra Jouini, Sermet Sezigen, Samia Hafi, Sheena Mccabe, Naoufel Somrani, Kawther El Aifa, Henda Chebbi, Asma Ben Amor, Yosra Kerkeni, Ahmed M Al-Wathinani, Nassem Mohammed Abdulla, Ammar Abdulrahman Jairoun, Brendon Morris, Nicholas Castle, Loua Al-Sheikh, Walid Abougalala, Mohamed Ben Dhiab, James Laughton","doi":"10.1089/hs.2023.0093","DOIUrl":null,"url":null,"abstract":"<p><p>Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. Findings revealed negative and fearful sentiments about a lack of accessibility to preparedness information, as well as positive sentiments toward CBRN preparedness concepts raised by the modified interview method. The artificial intelligence analysis techniques revealed a common consensus among experts about the importance of having accessible and effective plans and improved health sector preparedness in MENA, especially for potential chemical and biological incidents. Findings from this study can inform policymakers in the region to converge their efforts to build collaborative initiatives to strengthen CBRN preparedness capabilities in the healthcare sector.</p>","PeriodicalId":12955,"journal":{"name":"Health Security","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques.\",\"authors\":\"Hassan Farhat, Guillaume Alinier, Mariana Helou, Ionnais Galatis, Nidaa Bajow, Denis Jose, Sarra Jouini, Sermet Sezigen, Samia Hafi, Sheena Mccabe, Naoufel Somrani, Kawther El Aifa, Henda Chebbi, Asma Ben Amor, Yosra Kerkeni, Ahmed M Al-Wathinani, Nassem Mohammed Abdulla, Ammar Abdulrahman Jairoun, Brendon Morris, Nicholas Castle, Loua Al-Sheikh, Walid Abougalala, Mohamed Ben Dhiab, James Laughton\",\"doi\":\"10.1089/hs.2023.0093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. 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Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques.
Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. Findings revealed negative and fearful sentiments about a lack of accessibility to preparedness information, as well as positive sentiments toward CBRN preparedness concepts raised by the modified interview method. The artificial intelligence analysis techniques revealed a common consensus among experts about the importance of having accessible and effective plans and improved health sector preparedness in MENA, especially for potential chemical and biological incidents. Findings from this study can inform policymakers in the region to converge their efforts to build collaborative initiatives to strengthen CBRN preparedness capabilities in the healthcare sector.
期刊介绍:
Health Security is a peer-reviewed journal providing research and essential guidance for the protection of people’s health before and after epidemics or disasters and for ensuring that communities are resilient to major challenges. The Journal explores the issues posed by disease outbreaks and epidemics; natural disasters; biological, chemical, and nuclear accidents or deliberate threats; foodborne outbreaks; and other health emergencies. It offers important insight into how to develop the systems needed to meet these challenges. Taking an interdisciplinary approach, Health Security covers research, innovations, methods, challenges, and ethical and legal dilemmas facing scientific, military, and health organizations. The Journal is a key resource for practitioners in these fields, policymakers, scientific experts, and government officials.