Background: The first case of COVID 19 illness was detected on 31st December 2019 and the disease has progressed globally causing significant morbidity and mortality. The disease initially thought to ...be a respiratory virus soon showed manifestations involving other systems and diagnosis and treatment of the disease became more complicated.
Aims and Objective: This study aims to derive a scoring system based on health records of patients suffering from COVID 19, to help in early triaging of the illness and therefore allowing for early institution of treatment. To establish a scoring system inclusive of clinical, laboratory and radiological parameters to assist in the prognosis of patients afflicted with COVID 19 illness.
Materials and Methods: Health records of 138 COVID patients has been included in the study. The scoring system comprises of parameters including Age, Co-morbidities, Shortness of breath, Saturation, Pulse Rate, Respiratory rate, temperature, D dimer, Neutrophil Lymphocyte ratio, Troponin I, Organ involvement, Radiology. The cumulative scoring ranges from 0-16. The mortality rate among the subjects included was 25.4%.
Results: All parameters involved were found to be independent risk factors for mortality. Patients were effectively categorizedbased on the scoring system and mortality found to be associated with increasing scores. This model displayed good discrimination (AUC =0.875) and the sensitivity and specificity of the model was found to be 0.857 and 0.767 respectively.
Conclusion: This scoring system has been designed to categorize based on the systemic involvement of the disease and thus would serve as a reliable indicator for prognostic assessment in patients.
Introduction : PRICOV-19 est une étude transversale européenne basée sur un questionnaire en ligne, décrivant l’impact de la pandémie sur les structures de soins primaires (SSP). En France, les SSP ...sont les cabinets solo (CS), les cabinets de groupe mono ou pluriprofessionnels (CG), les structures d’exercice coordonné (SEC) : maisons de santé et centres de santé. Le triage, qu’il soit numérique (TN), téléphonique (TT) ou à l’arrivée (TA) est indispensable pour réduire le risque infectieux, et fait partie des pratiques d’organisation recommandées. But de l’étude : En s’appuyant sur les données françaises de l’étude PRICOV-19, l’objectif est de décrire la fréquence et les facteurs associés au triage dans les SSP pendant la pandémie de COVID 19. Résultats : 1 100 structures ont répondu au questionnaire. Le TN a été mis en œuvre dans 64 % des SSP (53,3 % des CS, 64,9 % des CG, 73,2 % des SEC). Le TT a été mis en œuvre dans 76 % des structures (72,7 % des CS, 75,4 % des CG et 81 % des SEC). Enfin, le TA a été mis en œuvre dans 52 % des structures (37,7 % des CS, 52 % des CG et 67 % des SEC). Les autres facteurs positivement associés sont le territoire urbain et la charge de travail moins importante pour le TN, et la présence d’une réceptionniste pour le TA. Conclusion : Les pratiques de triage semblent clairement associés à l’organisation et aux conditions d’exercice dans les SSP, et en premier lieu au type de structure.
Cervical cancer is by far the most common HPV-related disease. About 99.7% of cervical cancer cases are caused by persistent genital high-risk human papillomavirus (HPV) infection. Worldwide, ...cervical cancer is one of the most common cancers in women with an estimated 528,000 new cases reported in 2012. Most HPV infections clear spontaneously but persistent infection with the oncogenic or high-risk types may cause cancer of the oropharynx and anogenital regions. The virus usually infects the mucocutaneous epithelium and produces viral particles in matured epithelial cells and then causes a disruption in normal cell-cycle control and the promotion of uncontrolled cell division leading to the accumulation of genetic damage. There are currently two effective prophylactic vaccines against HPV infection, and these comprise of HPV types 16 and 18, and HPV types 6, 11, 16 and 18 virus-like particles. HPV testing in the secondary prevention of cervical cancer is clinically valuable in triaging low-grade cytological abnormalities and is also more sensitive than cytology as a primary screening. If these prevention strategies can be implemented in both developed and developing countries, many thousands of lives could be saved.
Issue tracking systems are widely used for collecting bug reports. A target of intelligent software engineering is to automate assigning bugs to appropriate developers. Recently, the momentum of ...artificial intelligence has brought many successful studies that triage bugs by classifying their reports with NLP-based methods. Some studies also try to introduce context information to represent developers. Nevertheless, they take a fundamental assumption that developers and bugs, closely related entities in real-world scenarios, should be modeled independently.
To capture the bug-developer correlations in bug triaging activities, we propose a Graph Collaborative filtering-based Bug Triaging framework: (1) bug-developer correlations are modeled as a bipartite graph; (2) natural language processing-based pre-training is implemented on bug reports to initialize bug nodes; (3) spatial–temporal graph convolution strategy is designed to learn the representation of developer nodes; (4) information retrieval-based classifier is proposed to match bugs and developers. Extensive experiments across mainstream datasets show the competence of our GCBT. Moreover, We believe that GCBT could generally benefit the modeling of correlations in other software engineering scenarios.
•Bug-developer correlations are modeled as a bipartite graph.•Natural language processing-based pre-training is implemented on bug reports to initialize bug nodes.•Spatial–temporal graph convolution strategy is designed to learn the representation of developer nodes.•Information retrieval-based classifier is proposed to match bugs and developers.•Extensive experiments across mainstream datasets show the competence of our GCBT.
Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers ...increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair TopicMiner with MTM ( TopicMiner<inline-formula><tex-math notation="LaTeX">^{MTM}</tex-math> <inline-graphic xlink:href="xia-ieq1-2576454.gif"/> </inline-formula> ). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that TopicMiner<inline-formula><tex-math notation="LaTeX"> ^{MTM}</tex-math> <inline-graphic xlink:href="xia-ieq2-2576454.gif"/> </inline-formula> can achieve top-1 and top-5 prediction accuracies of 0.4831-0.6868, and 0.7686-0.9084, respectively. We also compare TopicMiner<inline-formula><tex-math notation="LaTeX">^{MTM}</tex-math> <inline-graphic xlink:href="xia-ieq3-2576454.gif"/> </inline-formula> with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show that TopicMiner<inline-formula> <tex-math notation="LaTeX">^{MTM}</tex-math> <inline-graphic xlink:href="xia-ieq4-2576454.gif"/> </inline-formula> on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48 and 53.22 percent, LDA-KL by 262.91 and 105.97 percent, SVM-LDA by 205.89 and 110.48 percent, LDA-Activity by 377.60 and 176.32 percent, and Yang et al.'s approach by 59.88 and 13.70 percent, respectively.
Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate ...discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability.
The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers.
We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods.
Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910).
RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Consequentialist life‐maximizing approaches to triaging prescribe that everyone ought to have an equal chance of living a typical lifespan, through the saving more life‐years (or saving most lives) ...principle, which emphasizes the youngest‐first principle and in some cases a lottery approach, often at the expense of the old and the sick. Although this approach has already been criticized by several bioethicists, this article provides a different kind of criticism to the life‐cycle viewpoint, one that has not yet been explored at length; namely, we contend that the life‐maximizing approach entails a form of racism without racists in its attitude towards Black people. More specifically, we contend that by neglecting the idea that current societies are not post‐racial, it privileges White individuals and disadvantages Black people in the triaging process, curtails equal opportunities for Black people, reinforces white normativity, and neglects African culture. We end the article by pointing towards an Afro‐communitarian relational triaging approach that does not face the same difficulties as consequentialist life‐maximizing approaches do.
Efficient triaging and referral assessments are critical in ensuring prompt medical intervention in the community healthcare (CHC) system. However, the existing triaging systems in many community ...health services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms which might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision making. The model was trained on medical records from a dataset of patients with “Heart Failure”, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using a variety of machine learning algorithms, where XGBoost demonstrated the best performance among the other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, a precision of 0.9986, a recall of 0.9958, and an F1-score of 0.9972. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.
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