Pub Date : 2024-06-01DOI: 10.1007/s10916-024-02073-z
Pei-Fu Chen, Franklin Dexter
Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, and coefficients of variation . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.
现代麻醉药物可确保全身麻醉的疗效。目标包括减少手术、气管拔管、麻醉后护理病房或术中反应恢复时间的变异性。基于对数正态分布的广义置信区间可比较组间变异性,特别是标准偏差比。另一种统计方法是进行稳健方差比较测试,给出的是 P 值,而不是标准差比率的点估计值或置信区间。我们进行了蒙特卡罗模拟,以了解当分析以对数正态分布为基础,而真实分布为Weibull时,麻醉相关时间标准差比率的置信区间会发生什么变化。我们使用的模拟条件与大多数麻醉随机试验的荟萃分析相当,即 n ≈ 25,变异系数≈ 0.30。标准偏差比率的估计值呈正偏差,但偏差较小,比率比标称值大 0.11% 至 0.33%。相反,95% 置信区间非常宽(即 P≥0.05 的 >95%)。从临床或管理的角度来看,置信区间的差异虽然是实质性的,但却很小,比率的最大绝对差异为 0.016。因此,P
{"title":"Generalized Confidence Intervals for Ratios of Standard Deviations Based on Log-Normal Distribution when Times Follow Weibull Distributions.","authors":"Pei-Fu Chen, Franklin Dexter","doi":"10.1007/s10916-024-02073-z","DOIUrl":"https://doi.org/10.1007/s10916-024-02073-z","url":null,"abstract":"<p><p>Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, <math><mrow><mi>n</mi> <mo>≈</mo> <mn>25</mn></mrow> </math> and coefficients of variation <math><mrow><mo>≈</mo> <mn>0.30</mn></mrow> </math> . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"58"},"PeriodicalIF":5.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141186208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-27DOI: 10.1007/s10916-024-02077-9
Luca Neri, Ivan Corazza, Matt T Oberdier, Jessica Lago, Ilaria Gallelli, Arrigo F G Cicero, Igor Diemberger, Alessandro Orro, Amir Beker, Nazareno Paolocci, Henry R Halperin, Claudio Borghi
Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.
{"title":"Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment.","authors":"Luca Neri, Ivan Corazza, Matt T Oberdier, Jessica Lago, Ilaria Gallelli, Arrigo F G Cicero, Igor Diemberger, Alessandro Orro, Amir Beker, Nazareno Paolocci, Henry R Halperin, Claudio Borghi","doi":"10.1007/s10916-024-02077-9","DOIUrl":"10.1007/s10916-024-02077-9","url":null,"abstract":"<p><p>Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as \"Good\", 12% as \"Acceptable\", and 18% as \"Not Readable\". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"57"},"PeriodicalIF":5.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11129969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-27DOI: 10.1007/s10916-024-02078-8
Yang Cao, Guochao Zhang, You Wu, Hang Yi
The rapid growth of internet users in China presents opportunities for advancing the "Healthy China 2030" initiative through online health education. Platforms like "Shanghai Health Cloud" and "National Health Information Platform" improve health literacy and management, enhancing overall public health. However, challenges such as the digital divide and the spread of unverified health information hinder progress. Addressing these issues requires enhancing digital infrastructure, employing advanced technologies for information validation, and setting high standards for online health services. Integrated efforts from various sectors are essential to maximize the benefits of online health education in China.
{"title":"Navigating Challenges and Seizing Opportunities in China's Era of Online Health Education.","authors":"Yang Cao, Guochao Zhang, You Wu, Hang Yi","doi":"10.1007/s10916-024-02078-8","DOIUrl":"https://doi.org/10.1007/s10916-024-02078-8","url":null,"abstract":"<p><p>The rapid growth of internet users in China presents opportunities for advancing the \"Healthy China 2030\" initiative through online health education. Platforms like \"Shanghai Health Cloud\" and \"National Health Information Platform\" improve health literacy and management, enhancing overall public health. However, challenges such as the digital divide and the spread of unverified health information hinder progress. Addressing these issues requires enhancing digital infrastructure, employing advanced technologies for information validation, and setting high standards for online health services. Integrated efforts from various sectors are essential to maximize the benefits of online health education in China.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"56"},"PeriodicalIF":5.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.1007/s10916-024-02072-0
Mohamad-Hani Temsah, Abdullah N Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B Alsulaim, Amr Jamal, Afnan Khayat, Mohammed Hussien Alghamdi, Rabih Halwani, Muhammad Khurram Khan, Ayman Al-Eyadhy, Rakan Nazer
Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.
{"title":"Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.","authors":"Mohamad-Hani Temsah, Abdullah N Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B Alsulaim, Amr Jamal, Afnan Khayat, Mohammed Hussien Alghamdi, Rabih Halwani, Muhammad Khurram Khan, Ayman Al-Eyadhy, Rakan Nazer","doi":"10.1007/s10916-024-02072-0","DOIUrl":"https://doi.org/10.1007/s10916-024-02072-0","url":null,"abstract":"<p><p>Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"54"},"PeriodicalIF":5.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.1007/s10916-024-02066-y
Jianning Li, David G Ellis, Antonio Pepe, Christina Gsaxner, Michele R Aizenberg, Jens Kleesiek, Jan Egger
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
{"title":"Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.","authors":"Jianning Li, David G Ellis, Antonio Pepe, Christina Gsaxner, Michele R Aizenberg, Jens Kleesiek, Jan Egger","doi":"10.1007/s10916-024-02066-y","DOIUrl":"10.1007/s10916-024-02066-y","url":null,"abstract":"<p><p>Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"55"},"PeriodicalIF":5.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11116219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1007/s10916-024-02076-w
Mohammad Saiduzzaman Sayed, Mohammad Abu Tareq Rony, Mohammad Shariful Islam, Ali Raza, Sawsan Tabassum, Mohammad Sh Daoud, Hazem Migdady, Laith Abualigah
Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.
{"title":"A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.","authors":"Mohammad Saiduzzaman Sayed, Mohammad Abu Tareq Rony, Mohammad Shariful Islam, Ali Raza, Sawsan Tabassum, Mohammad Sh Daoud, Hazem Migdady, Laith Abualigah","doi":"10.1007/s10916-024-02076-w","DOIUrl":"https://doi.org/10.1007/s10916-024-02076-w","url":null,"abstract":"<p><p>Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"53"},"PeriodicalIF":5.3,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-18DOI: 10.1007/s10916-024-02074-y
Zhi-Qiang Li, Xue-Feng Wang, Jian-Ping Liu
This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which "digital health"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.
{"title":"Publication Trends and Hot Spots of ChatGPT's Application in the Medicine.","authors":"Zhi-Qiang Li, Xue-Feng Wang, Jian-Ping Liu","doi":"10.1007/s10916-024-02074-y","DOIUrl":"10.1007/s10916-024-02074-y","url":null,"abstract":"<p><p>This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which \"digital health\"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"52"},"PeriodicalIF":5.3,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.1007/s10916-024-02070-2
H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant
Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.
{"title":"An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.","authors":"H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant","doi":"10.1007/s10916-024-02070-2","DOIUrl":"10.1007/s10916-024-02070-2","url":null,"abstract":"<p><p>Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"51"},"PeriodicalIF":5.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.
Aim: To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.
Methods: A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.
Results: Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.
Conclusions: The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.
导言:目的:了解与传统心肺复苏术培训相比,虚拟现实技术在心肺复苏术培训或再培训课程中的应用是否能为患者(新生儿、儿童和成人)、医护人员和医疗机构带来益处:按照系统综述和元分析首选报告项目(PRISMA)指南进行系统综述(PROSPERO:CRD42023431768)。2023 年 6 月,对 PubMed、Cochrane Library、Scopus 和 Cumulative Index to Nursing and Allied Health Literature (CINAHL) 数据库进行了检索,并根据 Joanna Briggs Institute 的检查表对纳入研究的方法学质量进行了评估。对数据进行了叙述性总结:结果:共纳入 15 项研究,这些研究发表于 2013 年至 2023 年之间,总体质量尚可。没有研究调查了患者的治疗效果。在高级保健人员层面,虚拟学习环境被认为是吸引人、逼真的,并有助于记忆程序;然而,有限的决策、团队建设、心理压力和狂热的环境被强调为缺点。此外,据报告,使用除颤器和进行胸外按压的成绩普遍有所提高。在组织层面,一项研究进行了成本/效益评估,结果显示,与传统心肺复苏术培训相比,VR 更受青睐:结论:将 VR 用于心肺复苏术培训和再培训尚处于早期发展阶段。在心肺复苏术培训和再培训中使用 VR 尚处于早期发展阶段。然而,还需要进行更多标准化方法的研究,以确保逐步积累证据,并为该领域最佳培训方法的决策提供依据。
{"title":"Virtual Reality for Cardiopulmonary Resuscitation Healthcare Professionals Training: A Systematic Review.","authors":"Roberto Trevi, Stefania Chiappinotto, Alvisa Palese, Alessandro Galazzi","doi":"10.1007/s10916-024-02063-1","DOIUrl":"10.1007/s10916-024-02063-1","url":null,"abstract":"<p><strong>Introduction: </strong>Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.</p><p><strong>Aim: </strong>To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.</p><p><strong>Methods: </strong>A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.</p><p><strong>Results: </strong>Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.</p><p><strong>Conclusions: </strong>The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"50"},"PeriodicalIF":5.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140920678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-13DOI: 10.1007/s10916-024-02061-3
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.
{"title":"Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.","authors":"Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan","doi":"10.1007/s10916-024-02061-3","DOIUrl":"10.1007/s10916-024-02061-3","url":null,"abstract":"<p><p>Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"49"},"PeriodicalIF":5.3,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11090925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}