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2.
JMIR Public Health Surveill ; 7(3): e26719, 2021 03 24.
Article in English | MEDLINE | ID: covidwho-2197901

ABSTRACT

BACKGROUND: Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. OBJECTIVE: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. METHODS: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. RESULTS: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. CONCLUSIONS: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.


Subject(s)
Communicable Diseases, Emerging/diagnosis , Electronic Health Records , Information Storage and Retrieval/methods , Public Health Surveillance/methods , Travel/statistics & numerical data , Algorithms , COVID-19/epidemiology , Communicable Diseases, Emerging/epidemiology , Feasibility Studies , Female , Humans , Machine Learning , Male , Middle Aged , Natural Language Processing , Reproducibility of Results , United States/epidemiology
3.
J Travel Med ; 28(6)2021 08 27.
Article in English | MEDLINE | ID: covidwho-2189286

ABSTRACT

BACKGROUND: In 2016, the travel subcommittee of the UK Joint Committee on Vaccination and Immunisation (JCVI) recommended that 13-valent PCV (PCV13) could be offered to travellers aged over 65 years, visiting countries without infant PCV immunization programmes. This study aimed to identify, collate and review the available evidence to identify specific countries where UK travellers might be at an increased risk of developing pneumococcal infection. The data were then used to develop an algorithm, which could be used to facilitate implementation of the JCVI recommendation. METHODS: We conducted a systematic search of the published data available for pneumococcal disease, PCV vaccine implementation, coverage data and programme duration by country. The primary data sources used were World Health Organization databases and the International Vaccine Access Centre Vaccine Information and Epidemiology Window-hub database. Based on the algorithm, the countries were classified into 'high overall risk', 'intermediate overall risk' and 'low overall risk' from an adult traveller perspective. This could determine whether PCV13 should be recommended for UK adult travellers. RESULTS: A data search for a total of 228 countries was performed, with risk scores calculated for 188 countries. Overall, 45 countries were classified as 'high overall risk', 86 countries as 'intermediate overall risk', 57 countries as 'low overall risk' and 40 countries as 'unknown'. CONCLUSION: To our knowledge this is the first attempt to categorize the risk to UK adult travellers of contracting pneumococcal infection in each country, globally. These findings could be used by national travel advisory bodies and providers of travel vaccines to identify travellers at increased risk of pneumococcal infection, who could be offered PCV immunization.


Subject(s)
Pneumococcal Infections , Pneumococcal Vaccines , Adult , Aged , Algorithms , Humans , Infant , Pneumococcal Infections/epidemiology , Pneumococcal Infections/prevention & control , United Kingdom/epidemiology , Vaccination , Vaccines, Conjugate
4.
Front Cell Infect Microbiol ; 12: 887754, 2022.
Article in English | MEDLINE | ID: covidwho-2154665

ABSTRACT

Candida auris continues to be a global threat for infection and transmission in hospitals and long-term care facilities. The emergence of SARS-CoV-2 has rerouted attention and resources away from this silent pandemic to the frontlines of the ongoing COVID-19 disease. Cases of C. auris continue to rise, and clinical laboratories need a contingency plan to prevent a possible outbreak amid the COVID-19 pandemic. Here, we introduce a two-tier Candida auris surveillance program that includes, first, a rapid qualitative rt-PCR for the identification of high-risk patients and, second, a method to analyze the isolated C. auris for strain typing using the Fourier-Transform Infrared spectroscopy. We have performed this two-tier surveillance for over 700 at-risk patients being admitted into our hospital and have identified 28 positive specimens (4%) over a 1-year period. Strain typing analysis by the IR spectrum acquisition typing method, supplemented by whole genome sequencing, has shown grouping of two significant clusters. The majority of our isolates belong to circulating African lineage associated with C. auris Clade III and an isolated strain grouping differently belonging to South Asian lineage C. auris Clade I. Low numbers of genomic variation point to local and ongoing transmission within the Los Angeles area not specifically within the hospital setting. Collectively, clinical laboratories having the ability to rapidly screen high-risk patients for C. auris and to participate in outbreak investigations by offering strain typing will greatly assist in the control of C. auris transmission within the hospital setting.


Subject(s)
COVID-19 , Candidiasis , Algorithms , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Candida , Humans , Pandemics , Real-Time Polymerase Chain Reaction , SARS-CoV-2/genetics
5.
Technol Health Care ; 30(6): 1299-1314, 2022.
Article in English | MEDLINE | ID: covidwho-2154631

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.


Subject(s)
COVID-19 , Humans , COVID-19/diagnostic imaging , Tomography, X-Ray Computed/methods , Machine Learning , Lung/diagnostic imaging , Algorithms , Retrospective Studies
6.
Nat Methods ; 19(11): 1376-1382, 2022 11.
Article in English | MEDLINE | ID: covidwho-2151063

ABSTRACT

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.


Subject(s)
Algorithms , Proteins , Models, Molecular , Cryoelectron Microscopy/methods , Proteins/chemistry , Machine Learning , Protein Conformation
7.
Am J Bioeth ; 22(12): W1-W4, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2134455
8.
PLoS One ; 17(11): e0277625, 2022.
Article in English | MEDLINE | ID: covidwho-2140655

ABSTRACT

Face masks, recently adopted to reduce the spread of COVID-19, have had the unintended consequence of increasing the difficulty of face recognition. In security applications, face recognition algorithms are used to identify individuals and present results for human review. This combination of human and algorithm capabilities, known as human-algorithm teaming, is intended to improve total system performance. However, prior work has shown that human judgments of face pair similarity-confidence can be biased by an algorithm's decision even in the case of an error by that algorithm. This can reduce team effectiveness, particularly for difficult face pairs. We conducted two studies to examine whether face masks, now routinely present in security applications, impact the degree to which this cognitive bias is experienced by humans. We first compared the influence of algorithm's decisions on human similarity-confidence ratings in the presence and absence of face masks and found that face masks more than doubled the influence of algorithm decisions on human similarity-confidence ratings. We then investigated if this increase in cognitive bias was dependent on perceived algorithm accuracy by also presenting algorithm accuracy rates in the presence of face masks. We found that making humans aware of the potential for algorithm errors mitigated the increase in cognitive bias due to face masks. Our findings suggest that humans reviewing face recognition algorithm decisions should be made aware of the potential for algorithm errors to improve human-algorithm team performance.


Subject(s)
COVID-19 , Facial Recognition , Humans , Masks , COVID-19/prevention & control , Algorithms , Judgment
9.
JMIR Public Health Surveill ; 7(4): e26460, 2021 04 06.
Article in English | MEDLINE | ID: covidwho-2141312

ABSTRACT

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.


Subject(s)
Algorithms , Blockchain , Contact Tracing , Coronavirus Infections , Privacy , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Female , Humans , Male , Public Health
10.
Comput Intell Neurosci ; 2022: 1307944, 2022.
Article in English | MEDLINE | ID: covidwho-2121528

ABSTRACT

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.


Subject(s)
COVID-19 , Crows , Deep Learning , Algorithms , Animals , COVID-19/diagnosis , COVID-19 Testing , Humans , Pandemics
11.
Sensors (Basel) ; 22(22)2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2116265

ABSTRACT

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.


Subject(s)
COVID-19 , Pandemics , Humans , Algorithms , Students
12.
Sensors (Basel) ; 22(22)2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2110220

ABSTRACT

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.


Subject(s)
COVID-19 , Humans , Bayes Theorem , Machine Learning , Algorithms , Delivery of Health Care
13.
Sci Rep ; 12(1): 19211, 2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2117718

ABSTRACT

The classical theory of rough set was established by Pawlak, which mainly focusses on the approximation of sets characterized by a single equivalence relation over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multigranulation rough sets approach can better deal with the problems, where data might be spread over various locations. In this article, we present the idea of soft preference and soft dominance relation for the development of soft dominance rough set in an incomplete information system. Subsequently, several important structural properties and results of the proposed model are carefully analyzed. After employing soft dominance based rough set approach to it for any times, we can only get six different sets at most in an incomplete information system. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Based on soft dominance relation, we introduce logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximations in an incomplete information. Meanwhile, to measure the uncertainty of soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximation and some of their interesting properties are examined. Thereafter, a novel multi attribute with multi decision making problem approach based on logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough sets approach are developed to solve the selection of medicine to treat the coronavirus disease (COVID-19). The basic principle and the detailed steps of the decision making model (algorithms) are presented in detail. To demonstrate the applicability and potentiality of the proposed model, we present a practical example of a medical diagnosis is given to validate the practicality of the technique.


Subject(s)
COVID-19 , Humans , Algorithms , Uncertainty , Information Systems
14.
PLoS One ; 17(11): e0277081, 2022.
Article in English | MEDLINE | ID: covidwho-2109327

ABSTRACT

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.


Subject(s)
COVID-19 , Humans , COVID-19/diagnosis , Signal Processing, Computer-Assisted , Pandemics , Algorithms , Neural Networks, Computer , Electrocardiography
15.
Sci Rep ; 12(1): 18559, 2022 Nov 03.
Article in English | MEDLINE | ID: covidwho-2106456

ABSTRACT

Both the USA and Europe experienced substantial excess mortality in 2020 and 2021 related to the COVID-19 pandemic. Methods used to estimate excess mortality vary, making comparisons difficult. This retrospective observational study included data on deaths from all causes occurring in the USA and 25 European countries or subnational areas participating in the network for European monitoring of excess mortality for public health action (EuroMOMO). We applied the EuroMOMO algorithm to estimate excess all-cause mortality in the USA and Europe during the first two years of the COVID-19 pandemic, 2020-2021, and compared excess mortality by age group and time periods reflecting three primary waves. During 2020-2021, the USA experienced 154.5 (95% Uncertainty Interval [UI]: 154.2-154.9) cumulative age-standardized excess all-cause deaths per 100,000 person years, compared with 110.4 (95% UI: 109.9-111.0) for the European countries. Excess all-cause mortality in the USA was higher than in Europe for nearly all age groups, with an additional 44.1 excess deaths per 100,000 person years overall from 2020-2021. If the USA had experienced an excess mortality rate similar to Europe, there would have been approximately 391 thousand (36%) fewer excess deaths in the USA.


Subject(s)
COVID-19 , Humans , United States/epidemiology , COVID-19/epidemiology , Pandemics , Europe/epidemiology , Public Health , Algorithms , Mortality
16.
Nature ; 611(7936): 570-577, 2022 11.
Article in English | MEDLINE | ID: covidwho-2106425

ABSTRACT

Expanding our global testing capacity is critical to preventing and containing pandemics1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies21-29. Here we use a swarm of millimetre-sized magnets as mobile robotic agents ('ferrobots') for precise and robust handling of magnetized sample droplets and high-fidelity delivery of flexible workflows based on nucleic acid amplification tests to overcome these limitations. Within a palm-sized printed circuit board-based programmable platform, we demonstrated the myriad of laboratory-equivalent operations involved in pooled testing. These operations were guided by an introduced square matrix pooled testing algorithm to identify the samples from infected patients, while maximizing the testing efficiency. We applied this automated technology for the loop-mediated isothermal amplification and detection of the SARS-CoV-2 virus in clinical samples, in which the test results completely matched those obtained off-chip. This technology is easily manufacturable and distributable, and its adoption for viral testing could lead to a 10-300-fold reduction in reagent costs (depending on the viral prevalence) and three orders of magnitude reduction in instrumentation cost. Therefore, it is a promising solution to expand our testing capacity for pandemic preparedness and to reimagine the automated clinical laboratory of the future.


Subject(s)
Automation , COVID-19 Testing , Magnets , Molecular Diagnostic Techniques , Nucleic Acid Amplification Techniques , Robotics , SARS-CoV-2 , Humans , COVID-19/diagnosis , COVID-19/virology , COVID-19 Testing/methods , Molecular Diagnostic Techniques/economics , Molecular Diagnostic Techniques/methods , Nucleic Acid Amplification Techniques/economics , Nucleic Acid Amplification Techniques/methods , Pandemics/prevention & control , RNA, Viral/analysis , RNA, Viral/genetics , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Algorithms , Automation/economics , Automation/methods , Robotics/methods , Indicators and Reagents/economics
17.
J Phys Chem B ; 126(46): 9465-9475, 2022 Nov 24.
Article in English | MEDLINE | ID: covidwho-2106303

ABSTRACT

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Markov Chains , Protein Conformation , Algorithms , Molecular Dynamics Simulation
18.
Brief Bioinform ; 23(6)2022 Nov 19.
Article in English | MEDLINE | ID: covidwho-2107362

ABSTRACT

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.


Subject(s)
COVID-19 , Humans , Data Mining/methods , Publications , Knowledge , Algorithms , Proteins
19.
In Vivo ; 36(6): 2986-2992, 2022.
Article in English | MEDLINE | ID: covidwho-2100684

ABSTRACT

BACKGROUND/AIM: To report long-term survival results after trimodal approach for locally advanced rectal cancer (LARC) in the Covid-19 era. We herein illustrate a clinical application of Covid-death mean-imputation (CoDMI) algorithm in LARC patients with Covid-19 infection. PATIENTS AND METHODS: We analyzed 94 patients treated for primary LARC. Overall survival was calculated in months from diagnosis to first event (last follow-up/death). Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events, the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using the CoDMI algorithm. Patients who died of Covid-19 (DoC) are mean-imputed by the Kaplan-Meier estimator. Under this approach, the observed lifetime of each DoC patient is considered as an "incomplete data" and is extended by an additional expected lifetime computed using the classical Kaplan-Meier model. RESULTS: Sixteen patients were dead of disease (DoD), 1 patient was DoC and 77 cases were censored (Cen). The DoC patient died of Covid-19 52 months after diagnosis. The CoDMI algorithm computed the expected future lifetime provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, the CoDMI algorithm estimated that this patient would have died after 79.5 months of follow-up. CONCLUSION: The CoDMI algorithm leads to "unbiased" probability of overall survival in LARC patients with Covid-19 infection, compared to that provided by a naïve application of Kaplan-Meier approach. This allows for a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.


Subject(s)
COVID-19 , Radiation Oncology , Humans , Kaplan-Meier Estimate , COVID-19/epidemiology , Survival Analysis , Algorithms
20.
PLoS One ; 17(10): e0276923, 2022.
Article in English | MEDLINE | ID: covidwho-2098766

ABSTRACT

OBJECTIVE: Identifying the time of SARS-CoV-2 viral infection relative to specific gestational weeks is critical for delineating the role of viral infection timing in adverse pregnancy outcomes. However, this task is difficult when it comes to Electronic Health Records (EHR). In combating the COVID-19 pandemic for maternal health, we sought to develop and validate a clinical information extraction algorithm to detect the time of clinical events relative to gestational weeks. MATERIALS AND METHODS: We used EHR from the National COVID Cohort Collaborative (N3C), in which the EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We performed EHR phenotyping, resulting in 270,897 pregnant women (June 1st, 2018 to May 31st, 2021). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity, and extreme length of gestation (<150 or >300). RESULTS: The algorithm identified 296,194 pregnancies (16,659 COVID-19, 174,744 without COVID-19) in 270,897 pregnant women. For inferring gestational age, 95% cases (n = 40) have moderate-high accuracy (Cohen's Kappa = 0.62); 100% cases (n = 40) have moderate-high granularity of temporal information (Cohen's Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen's Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen's Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity or overweight (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (0.2% vs. 0.1%), respiratory distress syndrome or acute respiratory failure (1.8% vs. 0.2%). DISCUSSION: We explored the characteristics of pregnant women by different gestational weeks of SARS-CoV-2 infection with our algorithm. TED-PC is the first to infer the exact gestational week linked with every clinical event from EHR and detect the timing of SARS-CoV-2 infection in pregnant women. CONCLUSION: The algorithm shows excellent clinical validity in inferring gestational age and delivery dates, which supports multiple EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.


Subject(s)
COVID-19 , Pregnancy Complications, Infectious , Premature Birth , Female , Pregnancy , Humans , COVID-19/epidemiology , Pandemics , Pregnant Women , Gestational Age , SARS-CoV-2 , Electronic Health Records , Pregnancy Complications, Infectious/diagnosis , Pregnancy Complications, Infectious/epidemiology , Pregnancy Outcome , Algorithms , Premature Birth/epidemiology
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