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Mr. Taoran Liu
Department of Public Health and Preventive Medicine, School of Medicine, Jinan University

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0 Epidemic Modelling
0 Cost analysis and economic evaluation of health interventions

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Journal article
Published: 14 June 2021 in Vaccines
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Objectives: To investigate the differences in vaccine hesitancy and preference of the currently available COVID-19 vaccines between two countries, namely, China and the United States (U.S.). Method: A cross-national survey was conducted in both China and the United States, and discrete choice experiments, as well as Likert scales, were utilized to assess vaccine preference and the underlying factors contributing to vaccination acceptance. Propensity score matching (PSM) was performed to enable a direct comparison between the two countries. Results: A total of 9077 (5375 and 3702 from China and the United States, respectively) respondents completed the survey. After propensity score matching, over 82.0% of respondents from China positively accepted the COVID-19 vaccination, while 72.2% of respondents from the United States positively accepted it. Specifically, only 31.9% of Chinese respondents were recommended by a doctor to have COVID-19 vaccination, while more than half of the U.S. respondents were recommended by a doctor (50.2%), local health board (59.4%), or friends and families (64.8%). The discrete choice experiments revealed that respondents from the United States attached the greatest importance to the efficacy of COVID-19 vaccines (44.41%), followed by the cost of vaccination (29.57%), whereas those from China held a different viewpoint, that the cost of vaccination covered the largest proportion in their trade-off (30.66%), and efficacy ranked as the second most important attribute (26.34%). Additionally, respondents from China tended to be much more concerned about the adverse effect of vaccination (19.68% vs. 6.12%) and have a lower perceived severity of being infected with COVID-19. Conclusion: Although the overall acceptance and hesitancy of COVID-19 vaccination in both countries are high, underpinned distinctions between these countries were observed. Owing to the differences in COVID-19 incidence rates, cultural backgrounds, and the availability of specific COVID-19 vaccines in the two countries, vaccine rollout strategies should be nation-dependent.

ACS Style

Taoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola Akinwunmi; Babatunde Akinwunmi; Casper Zhang; Yibo Wu; Wai-Kit Ming. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines 2021, 9, 649 .

AMA Style

Taoran Liu, Zonglin He, Jian Huang, Ni Yan, Qian Chen, Fengqiu Huang, Yuejia Zhang, Omolola Akinwunmi, Babatunde Akinwunmi, Casper Zhang, Yibo Wu, Wai-Kit Ming. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines. 2021; 9 (6):649.

Chicago/Turabian Style

Taoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola Akinwunmi; Babatunde Akinwunmi; Casper Zhang; Yibo Wu; Wai-Kit Ming. 2021. "A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States." Vaccines 9, no. 6: 649.

Preprint content
Published: 04 June 2021
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BACKGROUND The COVID-19 epidemic is still far from over, and vaccination is considered an effective tool to curb its spread. Middle-aged and elderly people are more likely to be infected with the virus because of their frality and weaker immune systems. Therefore, it worth exploring their acceptance and preference of COVID-19 vaccines. Such data will help to develop and promote the COVID-19 vaccines. OBJECTIVE To qualify the acceptance and attribute preference of COVID-19 vaccines among middle-aged and elderly people in China and the United States. METHODS Quota sampling was used to investigate the demographic information and vaccine acceptance of middle-aged and elderly populations in China and the US. Through a discrete selection experiment, various attributes were set to quantify respondents’ preference in a vaccine trade-off. Propensity score matching methods were used to eliminate demographic difference between the two countries. RESULTS After propensity score matching, a total of 1604 respondents (802 from China and 802 from the US) were included. 71.7% and 74.7% of respondents in China and the US were willing to be vaccinated, and the social environment in China hinders vaccination, while the United States does the opposite. The CLOGIT showed that efficacy and the cost of vaccination are the two most important attributes for the public in China and the US (“efficacy” for the US, “cost” for China). Respondents preferred vaccines with 95% efficacy (China: odds ratio[OR] 1.82, 95%CI 1.71–1.94, P < .001; the US: odds ratio[OR] 6.40, 95% CI 5.97–6.85, P < .001; reference: 55% efficacy) and free (China: odds ratio[OR] 2.02, 95% CI 1.89–2.16, P < .001; the US: odds ratio[OR] 2.85, 95%CI 2.65-3.06, P < .001; reference: $200). Also, milder adverse effects and longer duration of the vaccine working were positively correlated with the public acceptance and willingness to receive the COVID-19 vaccine. Through the analysis of willingness to pay, the public was most willing to pay for reducing the vaccine’s adverse effects (37.476USD for the US, 140.503USD for China). However, the American public was willing to pay for a prolonged time of the vaccine working(1.375 USD per day increase). CONCLUSIONS Efforts should be made to increase vaccine acceptance among the middle-aged and elderly in China and the US. We propose that the two countries’ governments should strengthen the popularization of scientific and promote reasonable vaccine-related information to reduce the hindrance of the social environment to the public boycott of the vaccine. To make the public more willing to be vaccinated and achieve herd immunity, the two countries should reasonably regulate vaccine pricing, and scientists and pharmaceutical companies should remain committed to improving the efficacy of the vaccine, reducing adverse effects, prolonging the duration of vaccine works, and shortening the time for the vaccine to start working.

ACS Style

Xialei Li; Bojunhao Feng; Xiaocen Jia; Xiayi Guo; Zonglin He; Taoran Liu; Wenli Yu; Wai-Kit Ming; Yibo Wu. COVID-19 vaccine acceptance among Chinese and American middle-aged and elderly adults:A discrete choice experiment and propensity score matching study (Preprint). 2021, 1 .

AMA Style

Xialei Li, Bojunhao Feng, Xiaocen Jia, Xiayi Guo, Zonglin He, Taoran Liu, Wenli Yu, Wai-Kit Ming, Yibo Wu. COVID-19 vaccine acceptance among Chinese and American middle-aged and elderly adults:A discrete choice experiment and propensity score matching study (Preprint). . 2021; ():1.

Chicago/Turabian Style

Xialei Li; Bojunhao Feng; Xiaocen Jia; Xiayi Guo; Zonglin He; Taoran Liu; Wenli Yu; Wai-Kit Ming; Yibo Wu. 2021. "COVID-19 vaccine acceptance among Chinese and American middle-aged and elderly adults:A discrete choice experiment and propensity score matching study (Preprint)." , no. : 1.

Preprint content
Published: 02 May 2021
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Objectives To investigate the differences in vaccine hesitancy and preference of the currently available COVID-19 vaccines between two countries, viz. China and the United States (US). Method A cross-national survey was conducted in both China and the US, and discrete choice experiments as well as Likert scales were utilized to assess vaccine preference and the underlying factors contributing to the vaccination acceptance. A propensity score matching (PSM) was performed to enable a direct comparison between the two countries. Results A total of 9,077 (5,375 and 3,702, respectively, from China and the US) respondents have completed the survey. After propensity score matching, over 82.0% respondents from China positively accept the COVID-19 vaccination, while 72.2% respondents form the US positively accept it. Specifically, only 31.9% of Chinese respondents were recommended by a doctor to have COVID-19 vaccination, while more than half of the US respondents were recommended by a doctor (50.2%), local health board (59.4%), or friends and families (64.8%). The discrete choice experiments revealed that respondents from the US attached the greatest importance to the efficacy of COVID-19 vaccines (44.41%), followed by the cost of vaccination (29.57%), whereas those from China held a different viewpoint that the cost of vaccination covers the largest proportion in their trade-off (30.66%), and efficacy ranked as the second most important attribute (26.34%). Also, respondents from China tend to concerned much more about the adverse effect of vaccination (19.68% vs 6.12%) and have lower perceived severity of being infected with COVID-19. Conclusion While the overall acceptance and hesitancy of COVID-19 vaccination in both countries are high, underpinned distinctions between countries are observed. Owing to the differences in COVID-19 incidence rates, cultural backgrounds, and the availability of specific COVID-19 vaccines in two countries, the vaccine rollout strategies should be nation-dependent.

ACS Style

Taoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola M Akinwunmi; Babatunde Akinwunmi; Casper J.P Zhang; Yibo Wu; Wai-Kit Ming. The comparison of vaccine hesitancy of COVID-19 vaccination in China and the United States. 2021, 1 .

AMA Style

Taoran Liu, Zonglin He, Jian Huang, Ni Yan, Qian Chen, Fengqiu Huang, Yuejia Zhang, Omolola M Akinwunmi, Babatunde Akinwunmi, Casper J.P Zhang, Yibo Wu, Wai-Kit Ming. The comparison of vaccine hesitancy of COVID-19 vaccination in China and the United States. . 2021; ():1.

Chicago/Turabian Style

Taoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola M Akinwunmi; Babatunde Akinwunmi; Casper J.P Zhang; Yibo Wu; Wai-Kit Ming. 2021. "The comparison of vaccine hesitancy of COVID-19 vaccination in China and the United States." , no. : 1.

Journal article
Published: 02 March 2021 in Journal of Medical Internet Research
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Background Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people’s preferences for AI clinicians and traditional clinicians are worth exploring. Objective We aimed to quantify and compare people’s preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people’s preferences were affected by the pressure of pandemic. Methods We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people’s preferences for different diagnosis methods. Results In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. Conclusions Individuals’ preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.

ACS Style

Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study. Journal of Medical Internet Research 2021, 23, e26997 .

AMA Style

Taoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study. Journal of Medical Internet Research. 2021; 23 (3):e26997.

Chicago/Turabian Style

Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2021. "Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study." Journal of Medical Internet Research 23, no. 3: e26997.

Journal article
Published: 23 February 2021 in Journal of Medical Internet Research
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Background Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. Objective This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. Methods A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. Results A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. Conclusions Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

ACS Style

Taoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment. Journal of Medical Internet Research 2021, 23, e22841 .

AMA Style

Taoran Liu, Winghei Tsang, Fengqiu Huang, Oi Ying Lau, Yanhui Chen, Jie Sheng, Yiwei Guo, Babatunde Akinwunmi, Casper Jp Zhang, Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment. Journal of Medical Internet Research. 2021; 23 (2):e22841.

Chicago/Turabian Style

Taoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. 2021. "Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment." Journal of Medical Internet Research 23, no. 2: e22841.

Preprint content
Published: 07 January 2021
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BACKGROUND Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people’s preferences for AI clinicians and traditional clinicians are worth exploring. OBJECTIVE We aimed to quantify and compare people’s preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people’s preferences were affected by the pressure of pandemic. METHODS We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people’s preferences for different diagnosis methods. RESULTS In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. CONCLUSIONS Individuals’ preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.

ACS Style

Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint). 2021, 1 .

AMA Style

Taoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint). . 2021; ():1.

Chicago/Turabian Style

Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2021. "Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint)." , no. : 1.

Preprint content
Published: 25 December 2020
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BACKGROUND Due to the COVID-19 pandemic, health information related to COVID-19 has spread across the news media worldwide. Google is among the most used Internet search engines, and the Google Trends tool can reflect how the public seek COVID-related health information during this period. OBJECTIVE To understand health communication through Google Trends and news coverage and to explore their relationship with early prevention and control of COVID-19 in the early epidemic stage. METHODS To achieve the study objectives, we analyzed the public’s information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries for eight countries (United States, United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1, 2020 and April 29, 2020, and depicted the trend of news coverage on COVID-19 over time, as well as search trends on the topics of COVID-19 related diseases, treatments and medical resources, symptoms and signs, and public measures. The characteristics of various trends in different countries were described and analyzed. RESULTS Across all search trends in eight countries, search peaks were formed almost between March and April 2020, and declines occurred in April 2020. Regarding COVID-19 related diseases, the searched peak for all terms occurred near or after March 11, 2020 across all countries, except Singapore. For treatments and medical resources, the term “mask” formed multiple search peaks while “ventilator” fluctuated modestly. In the topic of symptoms and signs, “fever” and “cough” were the most searched terms. The topic of public measures was the least searched. Besides, when combing the search trends with news coverage, there were mainly three patterns: the American pattern, the Singapore pattern, and the other-countries pattern. The Singapore pattern mainly saw two search peaks, while the trends of news coverage and search queries in the American pattern were in opposite directions. CONCLUSIONS Our findings reveal public concern about facemasks, disease control, and public health measures. As a source of information, news media can influence the search behaviors of the public. According to public concerns, news media can be used to spread more valuable information, and thus achieve more effective health communication. Also, because public concerns varied in different countries and periods, news media can deliver more effective health communication based on the change of public interest. Governments and health care systems can recognize Google Trends as public needs in the early stages of a COVID-19 crisis, and can translate public needs into practices to better control the spread of COVID-19. Therefore, news coverage trends and Google search trends also contribute to the prevention and control of epidemics in the early epidemic stage.

ACS Style

Qian Liu; Wai-Kit Ming; Fengqiu Huang; Qiuyi Chen; AoAo Jiao; Taoran Liu; Huailiang Wu; Babatunde Akinwunmi; Jia Li; Guan Liu; Casper Jp Zhang; Jian Huang. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint). 2020, 1 .

AMA Style

Qian Liu, Wai-Kit Ming, Fengqiu Huang, Qiuyi Chen, AoAo Jiao, Taoran Liu, Huailiang Wu, Babatunde Akinwunmi, Jia Li, Guan Liu, Casper Jp Zhang, Jian Huang. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint). . 2020; ():1.

Chicago/Turabian Style

Qian Liu; Wai-Kit Ming; Fengqiu Huang; Qiuyi Chen; AoAo Jiao; Taoran Liu; Huailiang Wu; Babatunde Akinwunmi; Jia Li; Guan Liu; Casper Jp Zhang; Jian Huang. 2020. "Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint)." , no. : 1.

Journal article
Published: 15 September 2020 in Journal of Medical Internet Research
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Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

ACS Style

Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research 2020, 22, e21573 .

AMA Style

Jiayi Shen, Jiebin Chen, Zequan Zheng, Jiabin Zheng, Zherui Liu, Jian Song, Sum Yi Wong, Xiaoling Wang, Mengqi Huang, Po-Han Fang, Bangsheng Jiang, Winghei Tsang, Zonglin He, Taoran Liu, Babatunde Akinwunmi, Chi Chiu Wang, Casper J P Zhang, Jian Huang, Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research. 2020; 22 (9):e21573.

Chicago/Turabian Style

Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. 2020. "An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study." Journal of Medical Internet Research 22, no. 9: e21573.

Preprint content
Published: 24 July 2020
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BACKGROUND Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. OBJECTIVE This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. METHODS A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. RESULTS A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. CONCLUSIONS Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

ACS Style

Taoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint). 2020, 1 .

AMA Style

Taoran Liu, Winghei Tsang, Fengqiu Huang, Oi Ying Lau, Yanhui Chen, Jie Sheng, Yiwei Guo, Babatunde Akinwunmi, Casper Jp Zhang, Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint). . 2020; ():1.

Chicago/Turabian Style

Taoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. 2020. "Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint)." , no. : 1.

Preprint content
Published: 18 June 2020
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BACKGROUND Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

ACS Style

Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint). 2020, 1 .

AMA Style

Jiayi Shen, Jiebin Chen, Zequan Zheng, Jiabin Zheng, Zherui Liu, Jian Song, Sum Yi Wong, Xiaoling Wang, Mengqi Huang, Po-Han Fang, Bangsheng Jiang, Winghei Tsang, Zonglin He, Taoran Liu, Babatunde Akinwunmi, Chi Chiu Wang, Casper J P Zhang, Jian Huang, Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint). . 2020; ():1.

Chicago/Turabian Style

Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. 2020. "An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint)." , no. : 1.