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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 21st year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2019: 5.03, ranking Q1 in the medical informatics category. The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As a leading high-impact journal in its disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journals. 

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

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Recent Articles:

  • Source: Pixabay; Copyright: t_watanabe; URL:; License: Licensed by JMIR.

    Impact of the COVID-19 Pandemic on Partner Relationships and Sexual and Reproductive Health: Cross-Sectional, Online Survey Study


    Background: In the past few months, the coronavirus disease (COVID-19) pandemic has caused extensive economic and social damage. Objective: The purpose of this study was to assess the impact of COVID-19–related measures on partner relationships and sexual and reproductive health in China. Methods: From May 1 to 5, 2020, 3500 young Chinese individuals were recruited through WeChat or Weibo to participate in a survey to obtain information on sexual and reproductive health (eg, sexual desire, frequency of sexual intercourse, sexual satisfaction, etc). The questionnaire also collected demographic data (eg, age, race, education, current financial status, sexual orientation, relationship status, etc). Results: In total, 967 participants were included in the sexual health analysis. Due to the COVID-19 pandemic and related containment measures, 22% of participants (n=212) reported a decrease in sexual desire; 41% (n=396) experienced a decrease in the sexual intercourse frequency; 30% (n=291) reported an increase in the frequency of masturbation; 20% (n=192) reported a decrease in alcohol consumption before or during sexual activities, and 31% (n=298) reported a deterioration in partner relationships during the pandemic. The logistic regression analysis indicated that the following influenced partner relationships: accommodations during the pandemic (P=.046; odds ratio [OR] 0.59; 95% CI 0.30-0.86); exclusive relationship status (yes or no) (P<.001; OR 0.44; 95 % CI 0.27-0.73); sexual desire (P=.02; OR 2.01; 95% CI 1.38-2.97); and sexual satisfaction (P<.001; OR 1.92; 95% CI 1.54-2.50). COVID-19 also caused disruptions in reproductive health services such as prenatal and postnatal care, childbirth and abortion services, contraception availability, and the management of sexually transmitted infections. Conclusions: Our results show that many young people have wide-ranging issues affecting their sexual and reproductive health due to the COVID-19 pandemic and related containment measures. Strategies and guidelines are needed to safeguard the sexual and reproductive health of young people during this pandemic.

  • Source: Pexels; Copyright: Andrew Neel; URL:; License: Licensed by JMIR.

    Mental Health Disorders and Associated Risk Factors in Quarantined Adults During the COVID-19 Outbreak in China: Cross-Sectional Study


    Background: People undergoing mass home- and community-based quarantine are vulnerable to mental health disorders during outbreaks of coronavirus disease (COVID-19), but few studies have evaluated the associated psychosocial factors. Objective: This study aimed to estimate the prevalence of anxiety and depressive symptoms and identify associated demographic and psychosocial factors in the general Chinese population during the COVID-19 pandemic quarantine period. Methods: Participants aged 18 years or above were recruited in a cross-sectional online survey using snowball sampling from February 26-29, 2020. The survey included questions on demographics, family relationships, chronic diseases, quarantine conditions, lifestyle, COVID-19 infection, and anxiety and depressive symptoms. Logistic regression analyses were conducted to identify factors associated with elevated anxiety or depressive symptoms. Results: Out of 2331 participants, 762 (32.7%) experienced elevated anxiety or depressive symptoms. Nine risk factors associated with anxiety or depressive symptoms included younger age, reduced income, having cancer or other chronic diseases, having family members living with cancer, concerns related to COVID-19 infection for themselves or family members, living alone, having family conflicts, having <3 or >8 hours of sedentary time per day, and worsened sleep quality. Conclusions: The findings highlight an urgent need for psychological support for populations at high risk for elevated anxiety or depressive symptoms during the COVID-19 pandemic.

  • Source: Freepik; Copyright: user18526052; URL:; License: Licensed by JMIR.

    What Media Helps, What Media Hurts: A Mixed Methods Survey Study of Coping with COVID-19 Using the Media Repertoire Framework and the Appraisal Theory of Stress


    Background: Social and physical distancing in response to the coronavirus disease (COVID-19) pandemic has made screen-mediated information and communication technologies (media) indispensable. Whether an increase in screen use is a source of or a relief for stress remains to be seen. Objective: In the immediate aftermath of the COVID-19 lockdowns, we investigated the relation between subjective stress and changes in the pattern of media use. Based on Lazarus’s transactional model of appraisal and coping, and building on an earlier similar survey, we hypothesize that individual differences in the appraisal of media predict variations in approach or avoidance of media for coping with COVID-19 stress. Methods: Between March 20 and April 20, 2020, a brief snowball survey entitled: “What media helps, what media hurts: coping with COVID19 through screens” was distributed via Concordia University’s mailing lists and social media (PERFORM Centre, EngAGE Centre, and Media Health Lab). Using a media repertoire method, we asked questions about preferences, changes in use, and personal appraisal of media experiences (approach, avoid, and ignore) as a result of the COVID-19 pandemic and investigated interindividual differences in media use by factors such as subjective stress, age, gender, and self-reported mental health. Results: More than 90% of the survey respondents were in Canada and the east coast of the United States. From 685 completed responses, 169 respondents were “very stressed” and 452 were “slightly worried” about the pandemic. COVID-19 stress led to increased use of Facebook (χ23=11.76, P=.008), television (χ23=12.40, P=.006), YouTube (χ23=8.577, P=.04), and streaming services such as Netflix (χ23=10.71, P=.01). Respondents who considered their mental health “not good” were twice as likely to prefer streaming services as a coping tool for self-isolation. Women and nonbinary respondents were twice as likely than men to pick social media for coping. Individuals younger than 35 years were 3 times more likely to pick computer games, and individuals older than 55 years were more likely to pick network television or print media. Gender affected the appraisal of media (less in men than others) in terms of avoid (F1,637=5.84, P=.02) and approach scores (F1,637=14.31, P<.001). Subjective mental health affected the ignore score (less in those who said “good” than others; F1,637=13.88, P<.001). The appraisal score and use increase explained variations in worrying about physical and mental health stress due to increased screen time. A qualitative analysis of open-ended questions revealed that media (especially social networks) were important for coping if they provided support and connection through the dissemination of factual and positive information while avoiding the overflow of sensational and false news. Conclusions: The relationship between appraisal of media’s positive and negative facets vary with demographic differences in mental health resiliency. The media repertoire approach is an important tool in studies that focus on assessing the benefits and harms of screen overuse in different populations, especially in the context of the COVID-19 pandemic.

  • Doctor using internet hospital platform. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    The Internet Hospital Plus Drug Delivery Platform for Health Management During the COVID-19 Pandemic: Observational Study


    Background: Widespread access to the internet has boosted the emergence of online hospitals. A new outpatient service called “internet hospital plus drug delivery” (IHDD) has been developed in China, but little is known about this platform. Objective: The aim of this study is to investigate the characteristics, acceptance, and initial impact of IHDD during the outbreak of COVID-19 in a tertiary hospital in South China Methods: The total number of and detailed information on online prescriptions during the first 2 months after work resumption were obtained. Patients’ gender, age, residence, associated prescription department, time of prescription, payment, and drug delivery region were included in the analysis. Results: A total of 1380 prescriptions were picked up or delivered between March 2 and April 20, 2020. The largest group of patients were 36-59 years old (n=680, 49.3%), followed by the 18-35 years age category (n=573, 41.5%). In total, 39.4% (n=544) of the patients chose to get their medicine by self-pickup, while 60.6% (n=836) preferred to receive their medicine via drug delivery service. The top five online prescription departments were infectious diseases (n=572, 41.4%), nephrology (n=264, 19.1%), endocrinology (n=145, 10.5%), angiocardiopathy (n=107, 7.8%), and neurology (n=42, 3%). Of the 836 delivered prescriptions, 440 (52.6%) were sent to Guangdong Province (including 363 [43.4%] to Shenzhen), and 396 (47.4%) were sent to other provinces in China. Conclusions: The IHDD platform is efficient and convenient for various types of patients during the COVID-19 crisis. Although offline visits are essential for patients with severe conditions, IHDD can help to relieve pressure on hospitals by reducing an influx of patients with mild symptoms. Further efforts need to be made to improve the quality and acceptance of IHDD, as well as to regulate and standardize the management of this novel service.

  • Source: Shutterstock; Copyright: LeoWolfert; URL:; License: Licensed by the authors.

    A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping


    Background: The implementation of clinical decision support systems (CDSSs) as an intervention to foster clinical practice change is affected by many factors. Key factors include those associated with behavioral change and those associated with technology acceptance. However, the literature regarding these subjects is fragmented and originates from two traditionally separate disciplines: implementation science and technology acceptance. Objective: Our objective is to propose an integrated framework that bridges the gap between the behavioral change and technology acceptance aspects of the implementation of CDSSs. Methods: We employed an iterative process to map constructs from four contributing frameworks—the Theoretical Domains Framework (TDF); the Consolidated Framework for Implementation Research (CFIR); the Human, Organization, and Technology-fit framework (HOT-fit); and the Unified Theory of Acceptance and Use of Technology (UTAUT)—and the findings of 10 literature reviews, identified through a systematic review of reviews approach. Results: The resulting framework comprises 22 domains: agreement with the decision algorithm; attitudes; behavioral regulation; beliefs about capabilities; beliefs about consequences; contingencies; demographic characteristics; effort expectancy; emotions; environmental context and resources; goals; intentions; intervention characteristics; knowledge; memory, attention, and decision processes; patient–health professional relationship; patient’s preferences; performance expectancy; role and identity; skills, ability, and competence; social influences; and system quality. We demonstrate the use of the framework providing examples from two research projects. Conclusions: We proposed BEAR (BEhavior and Acceptance fRamework), an integrated framework that bridges the gap between behavioral change and technology acceptance, thereby widening the view established by current models.

  • Source: Pexels; Copyright: Edward Jenner; URL:; License: Licensed by the authors.

    Telemedicine in Germany During the COVID-19 Pandemic: Multi-Professional National Survey


    Background: In an effort to contain the effects of the coronavirus disease (COVID-19) pandemic, health care systems worldwide implemented telemedical solutions to overcome staffing, technical, and infrastructural limitations. In Germany, a multitude of telemedical systems are already being used, while new approaches are rapidly being developed in response to the crisis. However, the extent of the current implementation within different health care settings, the user’s acceptance and perception, as well as the hindering technical and regulatory obstacles remain unclear. Objective: The aim of this paper is to assess the current status quo of the availability and routine use of telemedical solutions, user acceptance, and the subjectively perceived burdens on telemedical approaches. Furthermore, we seek to assess the perception of public information quality among professional groups and their preferred communication channels. Methods: A national online survey was conducted on 14 consecutive days in March and April 2020, and distributed to doctors, nurses, and other medical professionals in the German language. Results: A total of 2827 medical professionals participated in the study. Doctors accounted for 65.6% (n=1855) of the professionals, 29.5% (n=833) were nursing staff, and 4.9% (n=139) were identified as others such as therapeutic staff. A majority of participants rated the significance of telemedicine within the crisis as high (1065/2730, 39%) or neutral (n=720, 26.4%); however, there were significant differences between doctors and nurses (P=.01) as well as between the stationary sector compared to the ambulatory sector (P<.001). Telemedicine was already in routine use for 19.6% (532/2711) of German health care providers and in partial use for 40.2% (n=1090). Participants working in private practices (239/594, 40.2%) or private clinics (23/59, 39.0%) experienced less regulatory or technical obstacles compared to university hospitals (586/1190, 49.2%). A majority of doctors rated the public information quality on COVID-19 as good (942/1855, 50.8%) or very good (213/1855, 11.5%); nurses rated the quality of public information significantly lower (P<.001). Participant’s age negatively correlated with the perception of telemedicine’s significance (ρ=–0.23; P<.001). Conclusions: Telemedicine has a broad acceptance among German medical professionals. However, to establish telemedical structures within routine care, technical and regulatory burdens must be overcome.

  • Source: Pressfoto / Freepik; Copyright: Pressfoto; URL:; License: Licensed by JMIR.

    Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study


    Background: Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. Objective: We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. Methods: Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. Results: All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). Conclusions: This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

  • Source: Mark Thyrring / The Copenhagen Center for Cancer and Health; Copyright: Mark Thyrring / The Copenhagen Center for Cancer and Health; URL:; License: Licensed by JMIR.

    Cancer Survivors’ Receptiveness to Digital Technology–Supported Physical Rehabilitation and the Implications for Design: Qualitative Study


    Background: Physical activity is associated with a positive prognosis in cancer survivors and may decrease the risk of adverse effects of treatment. Accordingly, physical activity programs are recommended as a part of cancer rehabilitation services. Digital technology may support cancer survivors in increasing their level of physical activity and increase the reach or efficiency of cancer rehabilitation services, yet it also comes with a range of challenges. Objective: The aim of this qualitative study was to explore cancer survivors’ receptiveness to using digital technology as a mode of support to increase their physical activity in a municipality-based cancer rehabilitation setting. Methods: Semistructured interviews were conducted with 11 cancer survivors (3 males, 8 females, age range 32-82 years) who were referred for cancer rehabilitation and had participated in a questionnaire survey using the Readiness and Enablement Index for Health Technology (READHY) questionnaire. Data analysis was based on the content analysis method. Results: Two themes were identified as important for the interviewees’ receptiveness to using digital technology services in connection with their physical activity during rehabilitation: their attitude toward physical activity and their attitude toward digital technology–assisted physical activity. Our results indicated that it is important to address the cancer survivors’ motivation for using technology for physical activity and their individual preferences in terms of the following: (1) incidental or structured (eg, cardiovascular and strength exercises or disease-specific rehabilitative exercises) physical activity; (2) social or individual context; and (3) instruction (know-how) or information (know-why). Conclusions: The identified preferences provide new insight that complements the cancer survivors’ readiness level and can likely help designers, service providers, and caregivers provide solutions that increase patient receptiveness toward technology-assisted physical activity. Combining digital technology informed by cancer survivors’ needs, preferences, and readiness with the capacity building of the workforce can aid in tailoring digital solutions to suit not only individuals who are receptive to using such technologies but also those reluctant to do so.

  • Source: Image created by the Authors/Placeit; Copyright: The Authors/Placeit; URL:; License: Licensed by JMIR.

    Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation


    Background: Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely compared or reproduced. Objective: The goal of the research was to generate reproducible machine learning modules for lung cancer detection and compare the approaches and performances of the award-winning algorithms developed in the Kaggle Data Science Bowl. Methods: We obtained the source codes of all award-winning solutions of the Kaggle Data Science Bowl Challenge, where participants developed automated CT evaluation methods to detect lung cancer (training set n=1397, public test set n=198, final test set n=506). The performance of the algorithms was evaluated by the log-loss function, and the Spearman correlation coefficient of the performance in the public and final test sets was computed. Results: Most solutions implemented distinct image preprocessing, segmentation, and classification modules. Variants of U-Net, VGGNet, and residual net were commonly used in nodule segmentation, and transfer learning was used in most of the classification algorithms. Substantial performance variations in the public and final test sets were observed (Spearman correlation coefficient = .39 among the top 10 teams). To ensure the reproducibility of results, we generated a Docker container for each of the top solutions. Conclusions: We compared the award-winning algorithms for lung cancer detection and generated reproducible Docker images for the top solutions. Although convolutional neural networks achieved decent accuracy, there is plenty of room for improvement regarding model generalizability.

  • Source: Pixabay; Copyright: ElenaBuzmakova_Borisova; URL:; License: Licensed by JMIR.

    Effects of Internet Hospital Consultations on Psychological Burdens and Disease Knowledge During the Early Outbreak of COVID-19 in China: Cross-Sectional...


    Background: Coronavirus disease (COVID-19) has become a global threat to human health. Internet hospitals have emerged as a critical technology to bring epidemic-related web-based services and medical support to the public. However, only a few very recent scientific literature reports have explored the effects of internet hospitals on psychological burden and disease knowledge in major public health emergencies such as the COVID-19 pandemic. Objective: The aim of this study was to explore the role of internet hospitals in relieving psychological burden and increasing disease knowledge during the early outbreak of the COVID-19 pandemic. Methods: This survey was conducted from January 26 to February 1, 2020, during the early outbreak of COVID-19 in China. The platform used for the consultation was the WeChat public account of our hospital. To participate in the study, the patient was required to answer a list of questions to exclude the possibility of COVID-19 infection and confirm their willingness to participate voluntarily. Next, the participant was directed to complete the self-report questionnaire. After the internet consultation, the participant was directed to complete the self-report questionnaire again. The questionnaire included sections on general information, the General Health Questionnaire-28 (GHQ-28), and the participant’s worries, disease knowledge, and need for hospital treatment. Results: The total number of internet consultations was 4120. The consultation topics mainly included respiratory symptoms such as cough, expectoration, and fever (2489/4120, 60.4%) and disease knowledge, anxiety, and fear (1023/4120, 24.8%). A total of 1530 people filled out the questionnaires before and after the internet consultation. Of these people, 1398/1530 (91.4%) experienced psychological stress before the internet consultation, which significantly decreased after consultation (260/1530, 17.0%) (χ21=1704.8, P<.001). There was no significant difference in the number of people who expressed concern about the COVID-19 pandemic before and after the internet consultation (χ21=0.7, P=.43). However, the degree of concern after the internet consultation was significantly alleviated (t2699=90.638, P<.001). The main worries before and after consultation were the dangers posed by the disease and the risk of infection of family members. The scores of the self-assessment risk after the internet consultation were significantly lower than those before consultation (t3058=95.694, P<.001). After the consultation, the participants’ knowledge of the symptoms, transmission routes, and preventive measures of COVID-19 was significantly higher than before the consultation (t3058=–106.105, –80.456, and –152.605, respectively; all P<.001). The hospital treatment need score after the internet consultation decreased from 3.3 (SD 1.2) to 1.6 (SD 0.8), and the difference was statistically significant (t3058=45.765, P<.001). Conclusions: During the early outbreak of COVID-19, internet hospitals could help relieve psychological burdens and increase disease awareness through timely and rapid spread of knowledge regarding COVID-19 prevention and control. Internet hospitals should be an important aspect of a new medical model in public health emergency systems.

  • Source: freepik; Copyright: pressfoto; URL:; License: Licensed by the authors.

    Landscape of Participant-Centric Initiatives for Medical Research in the United States, the United Kingdom, and Japan: Scoping Review


    Background: Information and communication technology (ICT) has made remarkable progress in recent years and is being increasingly applied to medical research. This technology has the potential to facilitate the active involvement of research participants. Digital platforms that enable participants to be involved in the research process are called participant-centric initiatives (PCIs). Several PCIs have been reported in the literature, but no scoping reviews have been carried out. Moreover, detailed methods and features to aid in developing a clear definition of PCIs have not been sufficiently elucidated to date. Objective: The objective of this scoping review is to describe the recent trends in, and features of, PCIs across the United States, the United Kingdom, and Japan. Methods: We applied a methodology suggested by Levac et al to conduct this scoping review. We searched electronic databases—MEDLINE (Medical Literature Analysis and Retrieval System Online), Embase (Excerpta Medica Database), CINAHL (Cumulative Index of Nursing and Allied Health Literature), PsycINFO, and Ichushi-Web—and sources of grey literature, as well as internet search engines—Google and Bing. We hand-searched through key journals and reference lists of the relevant articles. Medical research using ICT was eligible for inclusion if there was a description of the active involvement of the participants. Results: Ultimately, 21 PCIs were identified that have implemented practical methods and modes of various communication activities, such as patient forums and use of social media, in the field of medical research. Various methods of decision making that enable participants to become involved in setting the agenda were also evident. Conclusions: This scoping review is the first study to analyze the detailed features of PCIs and how they are being implemented. By clarifying the modes and methods of various forms of communication and decision making with patients, this review contributes to a better understanding of patient-centric involvement, which can be facilitated by PCIs.

  • Source: freepik; Copyright: jannoon028; URL:; License: Licensed by JMIR.

    Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study


    Background: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not available for use in general wards, and thus, many clinicians usually rely on practical senses to diagnose postoperative infection. Objective: This study aims to evaluate the performance of continuous monitoring with a deep learning model for early detection of bacteremia for surgical in-patients in the general ward and the intensive care unit (ICU). Methods: In this retrospective cohort study, we included 36,023 consecutive patients who underwent general surgery between October and December 2017 at a tertiary referral hospital in South Korea. The primary outcome was the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) for detecting bacteremia by the deep learning model, and the secondary outcome was the feature explainability of the model by occlusion analysis. Results: Out of the 36,023 patients in the data set, 720 cases of bacteremia were included. Our deep learning–based model showed an AUROC of 0.97 (95% CI 0.974-0.981) and an AUPRC of 0.17 (95% CI 0.147-0.203) for detecting bacteremia in surgical in-patients. For predicting bacteremia within the previous 24-hour period, the AUROC and AUPRC values were 0.93 and 0.15, respectively. Occlusion analysis showed that vital signs and laboratory measurements (eg, kidney function test and white blood cell group) were the most important variables for detecting bacteremia. Conclusions: A deep learning model based on time series electronic health records data had a high detective ability for bacteremia for surgical in-patients in the general ward and the ICU. The model may be able to assist clinicians in evaluating infection among in-patients, ordering blood cultures, and prescribing antibiotics with real-time monitoring.

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    Date Submitted: Aug 6, 2020

    Open Peer Review Period: Aug 6, 2020 - Oct 1, 2020

    Background: Background: Since January 2020, clinical trials associated with Coronavirus Disease 2019 (COVID-19) have sprung up in large numbers. However, there is very little understanding of the curr...

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    A reply to Lin and colleague’s article on the effect of mass and social media on the psycho behavioral responses of medical students. As medical students, we questioned the quality of the informatio...

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    Open Peer Review Period: Aug 3, 2020 - Sep 28, 2020

    Background: As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent...

    Background: As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery, but this information is scarce and at times incomplete. Objective: While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. Methods: We use a combination of a large-scale COVID-19 Twitter chatter dataset, natural language processing, and clinician reviews to identify self-reported symptoms on a set of Twitter users, publicly posting under the LongCovid hashtag. Results: A total of 7,781 unique tweets were identified from 4,607 users. After annotation, 2,603 tweets were manually reviewed, resulting in 150 eligible tweets from 107 users. A total of 192 reports including 34 distinct ICD-10 codes were identified. Conclusions: We have shown that researchers can leverage social media data, specifically Twitter, to conduct long-term post-COVID studies of patient-relevant and self-reported symptoms. By leveraging the #longcovid hashtag movement, we found similar reports to those obtained from clinical adjudication in recent publications.

  • Prognostic Assessment of COVID-19 in ICU by Machine Learning Methods: A Retrospective Study

    Date Submitted: Aug 2, 2020

    Open Peer Review Period: Aug 2, 2020 - Sep 27, 2020

    Background: Patients with coronavirus disease (COVID-19) in ICU have a high mortality rate, and how to early assess the prognosis and carry out precise treatment is of great significance. Objective: T...

    Background: Patients with coronavirus disease (COVID-19) in ICU have a high mortality rate, and how to early assess the prognosis and carry out precise treatment is of great significance. Objective: To use machine learning to construct a model for the analysis of risk factors and prediction of death among ICU patients with COVID-19. Methods: In this retrospective study, 123 COVID-19 patients in ICU were selected, and data were randomly divided into a training data set (n = 98) and test data set (n = 25) with a 4:1 ratio. Significance tests, analysis of correlation and factor analysis were used to screen the 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for COVID-19 patients in ICU. Performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Model interpretation and model evaluation of the risk prediction model were performed to ensure its stability and reliability. Results: Layer-by-layer screening of 100 potential risk factors revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage (LYM%), prothrombin time (PT), lactate dehydrogenase (LDH), total bilirubin (T-Bil), percentage of eosinophils (EOS%), creatinine(Cr), neutrophil percentage (NEUT%), albumin (ALB) level. Finally, eXtreme Gradient Boosting (XGBoost) established by 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. The model has been translated into an online risk calculator that is freely available to the public ( Conclusions: The XGBoost model predicts risk of death in ICU patients with COVID-19 well, and 8 factors help significantly to achieve good predictive effects. After algorithm verification, the model initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients. Clinical Trial: na

  • The Geographic Distribution of Mental health among Chinese Young Adults: A Nationwide Survey during COVID-19 Pandemic

    Date Submitted: Aug 2, 2020

    Open Peer Review Period: Aug 2, 2020 - Sep 27, 2020

    Background: Since the outbreak of novel coronavirus disease 2019 (COVID-19), mental health problems among young adults are concerned Objective: We assess the prevalence of mental health problems and e...

    Background: Since the outbreak of novel coronavirus disease 2019 (COVID-19), mental health problems among young adults are concerned Objective: We assess the prevalence of mental health problems and examine their associations with geographic location Methods: A nationwide cross-sectional survey was conducted in 16 provinces and autonomous regions among Chinese college students from Feb 4 to Feb 12, 2019. An online survey was adopted to collect information including demographics, perceived risk of infection, attitudes toward epidemic and control, and mental health status. Depression symptoms and anxiety were assessed by scales. A total of 11 787 participants were involved in the current study. A Chi-square test was used to compare the percentage of the perceived risk of infection and attitude toward COVID-19 between different geographic locations. The binary logistic models were used to identify associations between associations of geographic location and mental health problems after controlling for covariates Results: The prevalence of anxiety, depression symptoms were 17.8% and 25.9% for college students. After controlling for covariates, current living or college geographic location in Wuhan was positively associated with anxiety symptoms (OR=1.38, 95%CI: 1.12-1.69; OR=1.17, 95%CI: 1.03-1.32) and depression symptoms (OR=1.32, 95%CI: 1.10-1.59; OR=1.16, 95%CI: 1.04-1.30) compared with other areas. Living or travel history in Wuhan in the latest month was also positively associated with anxiety symptoms (OR=1.65, 95%CI: 1.49-1.83) and depression symptoms (OR=1.51, 95%CI: 1.34-1.6). Conclusions: Mental health problems are prevalent in Chinese young adults vary geographically during the COVID-19 pandemic. Our results provide further insight into developing targeted intervention strategies