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1.
Preprint in English | EuropePMC | ID: ppcovidwho-296170

ABSTRACT

ABSTRACT Coronavirus disease of 2019 (COVID-19) has impacted the world in unprecedented ways since first emerging in December 2019. In the last two years, the scientific community has made an enormous effort to understand COVID-19 and potential interventions. As of June 15, 2021, there were more than 140,000 COVID-19 focused manuscripts on PubMed and preprint servers, such as medRxiv and BioRxiv . Preprints, which constitute more than 15% of all manuscripts, may contain more up-to-date research findings compared to published papers, due to the sometimes lengthy timeline between manuscript submission and publication. Including preprints in systematic reviews and meta-analyses thus has the potential to improve the timeliness of reviews. However, there is no clear guideline on whether preprints should be included in systematic reviews and meta-analyses. Using a prototypical example of a rapid systematic review examining the comparative effectiveness of COVID-19 therapeutics, we propose including all preprints in the systematic review by assigning them a weight we term the “confidence score”. Motivated by our observation that, unlike the traditional journal submission process which is unobserved, the timeline from submission to publication for a preprint can be observed and can be modeled as a time-to-event outcome. This observation provides a unique opportunity to model and quantify the probability that a preprint will be published, which can be used as a confidence score to weight preprints in systematic reviews and meta-analyses. To obtain the confidence score, we propose a novel survival cure model, which incorporates both the time from posting to publication for a preprint, and key characteristics of the study described in the content of the preprint. Using meta data from 158 preprints on evaluating therapeutic options for COVID-19 posted through 09/03/2020, we demonstrate the utility of the confidence score in weighting of preprints in a systematic review. Our proposed method has the potential to advance timely systematic reviews of the evidence examining COVID-19 and other clinical conditions with rapidly evolving evidence bases by providing an approach for inclusion of unpublished manuscripts.

2.
Preprint in English | Other preprints | ID: ppcovidwho-295803

ABSTRACT

ABSTRACT Background Predicting outcomes of COVID-19 patients at an early stage is critical for optimized clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, based on the need for extensive data pre-processing and feature engineering, these models have not been validated or implemented outside of the original study site. Methods In this study, we propose CovRNN, recurrent neural network (RNN)-based models to predict COVID-19 patients’ outcomes, using their available electronic health record (EHR) data on admission, without the need for specific feature selection or missing data imputation. CovRNN is designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and long length of stay (LOS >7 days). Predictions are made for time-to-event risk scores (survival prediction) and all-time risk scores (binary prediction). Our models were trained and validated using heterogeneous and de-identified data of 247,960 COVID-19 patients from 87 healthcare systems, derived from the Cerner® Real-World Dataset (CRWD). External validation was performed using three test sets (approximately 53,000 patients). Further, the transferability of CovRNN was validated using 36,140 de-identified patients’ data derived from the Optum® de-identified COVID-19 Electronic Health Record v. 1015 dataset (2007–2020). Findings CovRNN shows higher performance than do traditional models. It achieved an area under the receiving operating characteristic (AUROC) of 93% for mortality and mechanical ventilation predictions on the CRWD test set (vs. 91·5% and 90% for light gradient boost machine (LGBM) and logistic regression (LR), respectively) and 86.5% for prediction of LOS > 7 days (vs. 81·7% and 80% for LGBM and LR, respectively). For survival prediction, CovRNN achieved a C-index of 86% for mortality and 92·6% for mechanical ventilation. External validation confirmed AUROCs in similar ranges. Interpretation Trained on a large heterogeneous real-world dataset, our CovRNN model showed high prediction accuracy, good calibration, and transferability through consistently good performance on multiple external datasets. Our results demonstrate the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering.

3.
Preprint in English | EuropePMC | ID: ppcovidwho-295329

ABSTRACT

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, Interpretability and usability. Built upon our previous work, in this study, we proposed an open natural language processing development framework and evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The generated corpora derived out of the texts from multiple intuitions and gold standard annotation are tested on a single institution's rule set has the performances in F1 score of 0.876, 0.706 and 0.694, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study.

4.
World J Pediatr ; 2021 Nov 22.
Article in English | MEDLINE | ID: covidwho-1527517

ABSTRACT

BACKGROUND: This study aimed to explore the imaging characteristics, diversity and changing trend in CT scans of pediatric patients infected with Delta-variant strain by studying imaging features of children infected with Delta and comparing the results to those of children with original COVID-19. METHODS: A retrospective, comparative analysis of initial chest CT manifestations between 63 pediatric patients infected with Delta variant in 2021 and 23 pediatric patients with COVID-19 in 2020 was conducted. Corresponding imaging features were analyzed. In addition, the changing trend in imaging features of COVID-19 Delta-variant cases were explored by evaluating the initial and follow-up CT scans. RESULTS: Among 63 children with Delta-variant COVID-19 in 2021, 34 (53.9%) showed positive chest CT presentation; and their CT score (1.10 ± 1.41) was significantly lower than that in 2020 (2.56 ± 3.5) (P = 0.0073). Lesion distribution: lung lesions of Delta cases appear mainly in the lower lungs on both sides. Most children had single lobe involvement (18 cases, 52.9%), 14 (41.2%) in the right lung alone, and 14 (41.2%) in both lungs. A majority of Delta cases displayed initially ground glass (23 cases, 67.6%) and nodular shadows (13 cases, 38.2%) in the first CT scan, with few extrapulmonary manifestations. The 34 children with abnormal chest CT for the first time have a total of 92 chest CT examinations. These children showed a statistically significant difference between the 0-3 day group and the 4-7 day group (P = 0.0392) and a significant difference between the 4-7 day group and the more than 8 days group (P = 0.0003). CONCLUSIONS: The early manifestations of COVID-19 in children with abnormal imaging are mostly small subpleural nodular ground glass opacity. The changes on the Delta-variant COVID-19 chest CT were milder than the original strain. The lesions reached a peak on CT in 4-7 days and quickly improved and absorbed after a week. Dynamic CT re-examination can achieve a good prognosis.

7.
BMJ Open ; 11(10): e052609, 2021 10 25.
Article in English | MEDLINE | ID: covidwho-1484032

ABSTRACT

OBJECTIVE: This study aimed to describe the epidemiological and clinical features and potential factors related to the time to return negative reverse transcriptase (RT)-PCR in discharged paediatric patients with COVID-19. DESIGN: Retrospective cohort study. SETTING: Unscheduled admissions to 12 tertiary hospitals in China. PARTICIPANTS: Two hundred and thirty-three clinical charts of paediatric patients with confirmed diagnosis of COVID-19 admitted from 1 January 2020 to 17 April 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measures: factors associated with the time to return negative RT-PCR from COVID-19 in paediatric patients. SECONDARY OUTCOME MEASURES: epidemiological and clinical features and laboratory results in paediatric patients. RESULTS: The median age of patients in our cohort was 7.50 (IQR: 2.92-12.17) years, and 133 (57.1%) patients were male. 42 (18.0%) patients were evaluated as asymptomatic, while 162 (69.5%) and 25 (10.7%) patients were classified as mild or moderate, respectively. In Cox regression analysis, longer time to negative RT-PCR was associated with the presence of confirmed infection in family members (HR (95% CI): 0.56 (0.41 to 0.79)). Paediatric patients with emesis symptom had a longer time to return negative (HR (95% CI): 0.33 (0.14 to 0.78)). During hospitalisation, the use of traditional Chinese medicine (TCM) and antiviral drugs at the same time is less conducive to return negative than antiviral drugs alone (HR (95% CI): 0.85 (0.64 to 1.13)). CONCLUSIONS: The mode of transmission might be a critical factor determining the disease severity of COVID-19. Patients with emesis symptom, complications or confirmed infection in family members may have longer healing time than others. However, there were no significant favourable effects from TCM when the patients have received antiviral treatment.


Subject(s)
COVID-19 , Child , Child, Preschool , Cohort Studies , Humans , Male , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2
9.
Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics ; 23(1):61-66, 2021.
Article in English | PMC | ID: covidwho-1389769

ABSTRACT

OBJECTIVE: To study the medication in children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Wuhan, China, and to provide a reference for rational drug use in clinical practice. METHODS: A retrospective analysis was performed on the medical data of the children who were diagnosed with SARS-CoV-2 infection from January 26 to March 5, 2020. The children were divided into an asymptomatic group with 41 children and a symptomatic group with 73 children. A subgroup analysis was performed to investigate the effect of different antiviral regimens (monotherapy, double therapy, or triple therapy) and whether interferon α-1b was used in combination with azithromycin on the length of hospital stay and the clearance time of SARS-CoV-2 nucleic acid. RESULTS: A total of 114 children with SARS-CoV-2 infection (72 boys and 42 girls) were enrolled. The median age of the children was 7.1 years. The median length of hospital stay was 10 days and the clearance time of SARS-CoV-2 nucleic acid was 6 days. In either group, the subgroup analysis showed no significance differences in the length of hospital stay and the clearance time of SARS-CoV-2 nucleic acid between the subgroups treated with different combinations of antiviral drugs and the subgroups treated with interferon α-1b alone or in combination with azithromycin (P > 0.05). CONCLUSIONS: It is not recommended to use the routine combinations of antiviral drugs for children with SARS-COV-2 infection or combine with azithromycin for the purpose of antiviral therapy.

10.
Microbiol Spectr ; 9(1): e0032721, 2021 09 03.
Article in English | MEDLINE | ID: covidwho-1361971

ABSTRACT

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% were female. A total of 83% smoked within the prior year, 61% were overweight/obese, 83% had immunocompromising conditions, and 96% had ≥2 comorbidities. The median interval between the two positive tests was 77 days. Among the 19 patients with 60 to 89 days between positive tests, 17 (89%) exhibited symptoms or clinical manifestations consistent with COVID-19 at the time of the second positive test and 14 (74%) were hospitalized at the second positive test. Of the four patients with ≥90 days between two positive tests (patient 2 [PT2], PT8, PT14, and PT19), two had mild or no symptoms at the second positive test and one, an immunocompromised patient, had a brief hospitalization at the first diagnosis, followed by intensive care unit (ICU) admission at the second diagnosis 3 months later. Our study demonstrated a high prevalence of compromised immune systems, comorbidities, obesity, and smoking among patients with repeatedly positive SARS-CoV-2 tests. Despite limitations, including a lack of semiquantitative estimates of viral load, these data may help prioritize suspected cases of reinfection for investigation and continued surveillance. IMPORTANCE The comprehensive characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and clinical data for patients with repeatedly positive SARS-CoV-2 tests can help prioritize suspected cases of reinfection for investigation in the absence of genome sequencing data and for continued surveillance of the potential long-term health consequences of SARS-CoV-2 infection.


Subject(s)
COVID-19 Testing , COVID-19/diagnosis , COVID-19/epidemiology , Electronic Health Records , SARS-CoV-2/isolation & purification , Adult , Aged , Comorbidity , Databases, Factual , Female , Health Surveys , Humans , Immune System , Male , Middle Aged , Obesity , Polymerase Chain Reaction , Risk Factors , Smoking , Viral Load
11.
Int J Inj Contr Saf Promot ; : 1-5, 2021 Aug 17.
Article in English | MEDLINE | ID: covidwho-1360276

ABSTRACT

Chinese mainland launched the 60-day first-level response to major public health emergencies during the COVID-19 outbreak. This study aimed to determine the incidence and describe the characteristics and predictors of patients who presented to the emergency department (ED) with facial trauma during this period. A retrospective review was conducted on the basis of data on facial trauma presented at the ED of XXX Hospital from 24 January 2020 to 23 March 2020 and the same period in the previous two years. Multivariate linear regression model was employed to explore potential determinants associated with daily number of facial trauma. Significant reduction was observed in the amount of facial trauma during the COVID-19 level I emergency response. The trauma volume evenly distributed over the week. The declined most significantly by age group, 20-29 years, and by time range of visit, 00:00-08:00. Multivariate regression analyses revealed positive relationship between daily minimum temperature and facial trauma volume. The number of facial injuries decreased significantly during the COVID-19 Level 1 emergency response, with the least reduction in total daytime facial trauma and in infant and child facial trauma. And a higher minimum temperature may lead to increased number of facial trauma presentations.

12.
Brief Bioinform ; 22(2): 800-811, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343640

ABSTRACT

OBJECTIVE: This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations. METHODS: We downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets. RESULTS: We found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets. CONCLUSION: PubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited.


Subject(s)
COVID-19/epidemiology , Datasets as Topic , Information Dissemination/methods , PubMed , SARS-CoV-2/isolation & purification , Humans
13.
Front Endocrinol (Lausanne) ; 12: 611526, 2021.
Article in English | MEDLINE | ID: covidwho-1305635

ABSTRACT

Background: It has been reported that dyslipidemia is related to coronavirus-related diseases. Critical patients with coronavirus disease 2019 (COVID-19) who suffered from multiple organ dysfunctions were treated in the intensive care unit (ICU) in Wuhan, China. Whether the lipids profile was associated with the prognosis of COVID-19 in critical patients remained unclear. Methods: A retrospective study was performed in critical patients (N=48) with coronavirus disease 2019 in Leishenshan hospital between February and April 2020 in Wuhan. The parameters including lipid profiles, liver function, and renal function were collected on admission day, 2-3days after the admission, and the day before the achievement of clinical outcome. Results: Albumin value and creatine kinase (ck) value were statistically decreased at 2-3 days after admission compared with those on admission day (P<0.05). Low density lipoprotein (LDL-c), high density lipoprotein (HDL-c), apolipoprotein A (ApoA), and apolipoprotein A (Apo B) levels were statistically decreased after admission (P<0.05). Logistic regression showed that HDL-c level both on admission day and the day before the achievement of clinical outcome were negatively associated with mortality in critical patients with COVID-19. Total cholesterol (TC) level at 2-3days after admission was related to mortality in critical patients with COVID-19. Conclusions: There were lipid metabolic disorders in the critical patients with COVID-19. Lower levels of HDL-c and TC were related to the progression of critical COVID-19.


Subject(s)
COVID-19/mortality , Dyslipidemias/epidemiology , Hospital Mortality , Multiple Organ Failure/mortality , Aged , Aged, 80 and over , Apolipoproteins A/blood , Apolipoproteins B/blood , COVID-19/blood , COVID-19/epidemiology , China/epidemiology , Cholesterol/blood , Cholesterol, HDL/blood , Cholesterol, LDL/blood , Critical Illness , Dyslipidemias/blood , Female , Humans , Male , Middle Aged , Multiple Organ Failure/blood , Multiple Organ Failure/epidemiology , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index
14.
J Am Med Inform Assoc ; 28(9): 2050-2067, 2021 08 13.
Article in English | MEDLINE | ID: covidwho-1276186

ABSTRACT

OBJECTIVE: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. MATERIALS AND METHODS: We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. RESULTS: In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. DISCUSSION: Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. CONCLUSION: There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.


Subject(s)
Artificial Intelligence , Biomedical Research/trends , COVID-19 , Algorithms , Databases as Topic , Humans , National Institutes of Health (U.S.) , Proteomics , United States , World Health Organization
15.
PLoS One ; 16(6): e0247235, 2021.
Article in English | MEDLINE | ID: covidwho-1256018

ABSTRACT

Understanding sociodemographic, behavioral, clinical, and laboratory risk factors in patients diagnosed with COVID-19 is critically important, and requires building large and diverse COVID-19 cohorts with both retrospective information and prospective follow-up. A large Health Information Exchange (HIE) in Southeast Texas, which assembles and shares electronic health information among providers to facilitate patient care, was leveraged to identify COVID-19 patients, create a cohort, and identify risk factors for both favorable and unfavorable outcomes. The initial sample consists of 8,874 COVID-19 patients ascertained from the pandemic's onset to June 12th, 2020 and was created for the analyses shown here. We gathered demographic, lifestyle, laboratory, and clinical data from patient's encounters across the healthcare system. Tobacco use history was examined as a potential risk factor for COVID-19 fatality along with age, gender, race/ethnicity, body mass index (BMI), and number of comorbidities. Of the 8,874 patients included in the cohort, 475 died from COVID-19. Of the 5,356 patients who had information on history of tobacco use, over 26% were current or former tobacco users. Multivariable logistic regression showed that the odds of COVID-19 fatality increased among those who were older (odds ratio = 1.07, 95% CI 1.06, 1.08), male (1.91, 95% CI 1.58, 2.31), and had a history of tobacco use (2.45, 95% CI 1.93, 3.11). History of tobacco use remained significantly associated (1.65, 95% CI 1.27, 2.13) with COVID-19 fatality after adjusting for age, gender, and race/ethnicity. This effort demonstrates the impact of having an HIE to rapidly identify a cohort, aggregate sociodemographic, behavioral, clinical and laboratory data across disparate healthcare providers electronic health record (HER) systems, and follow the cohort over time. These HIE capabilities enable clinical specialists and epidemiologists to conduct outcomes analyses during the current COVID-19 pandemic and beyond. Tobacco use appears to be an important risk factor for COVID-19 related death.


Subject(s)
COVID-19/mortality , Health Information Exchange/statistics & numerical data , Health Information Exchange/trends , Age Factors , Cohort Studies , Comorbidity , Healthcare Disparities , Hospitalization , Humans , Pandemics , Prospective Studies , Retrospective Studies , Risk Factors , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Sex Factors , Smoking , Texas
16.
J Am Med Inform Assoc ; 28(8): 1765-1776, 2021 07 30.
Article in English | MEDLINE | ID: covidwho-1246728

ABSTRACT

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.


Subject(s)
Algorithms , COVID-19 , Computer Communication Networks , Confidentiality , Electronic Health Records , Information Storage and Retrieval/methods , Natural Language Processing , Common Data Elements , Female , Humans , Logistic Models , Male , Registries
18.
J Am Med Inform Assoc ; 28(6): 1275-1283, 2021 06 12.
Article in English | MEDLINE | ID: covidwho-1120596

ABSTRACT

The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership common data model. A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 SignSym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the research community as a downloadable package (https://clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcare organizations to support clinical research of COVID-19.


Subject(s)
COVID-19/diagnosis , Electronic Health Records , Information Storage and Retrieval/methods , Natural Language Processing , Deep Learning , Humans , Symptom Assessment/methods
19.
International Journal of Healthcare Management ; : 1-6, 2021.
Article in English | Taylor & Francis | ID: covidwho-1114799
20.
J Am Med Inform Assoc ; 27(11): 1721-1726, 2020 11 01.
Article in English | MEDLINE | ID: covidwho-1024117

ABSTRACT

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.


Subject(s)
Biomedical Research , Computer Security , Coronavirus Infections , Information Dissemination , Pandemics , Pneumonia, Viral , Privacy , COVID-19 , Humans , Information Dissemination/ethics , Internationality , Machine Learning
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