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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
44 232-243 Species denotes coronavirus Tax:11118
45 368-373 Species denotes human Tax:9606
46 484-490 Species denotes people Tax:9606
47 884-898 Species denotes COVID-19 virus Tax:2697049
48 2263-2270 Species denotes patient Tax:9606
49 13-37 Disease denotes Coronavirus disease 2019 MESH:C000657245
50 39-47 Disease denotes COVID-19 MESH:C000657245
51 59-77 Disease denotes infectious disease MESH:D003141
52 501-509 Disease denotes infected MESH:D007239
53 513-521 Disease denotes COVID-19 MESH:C000657245
54 549-555 Disease denotes deaths MESH:D003643
55 658-666 Disease denotes COVID-19 MESH:C000657245
56 1030-1038 Disease denotes COVID-19 MESH:C000657245
57 1098-1106 Disease denotes COVID-19 MESH:C000657245
58 1587-1595 Disease denotes COVID-19 MESH:C000657245
59 1735-1743 Disease denotes COVID-19 MESH:C000657245
60 1935-1943 Disease denotes COVID-19 MESH:C000657245
61 2046-2054 Disease denotes COVID-19 MESH:C000657245
62 2301-2309 Disease denotes COVID-19 MESH:C000657245
63 2425-2433 Disease denotes COVID-19 MESH:C000657245
64 2434-2443 Disease denotes infection MESH:D007239
65 2512-2520 Disease denotes COVID-19 MESH:C000657245
79 2582-2590 Disease denotes COVID-19 MESH:C000657245
80 2599-2604 Disease denotes fever MESH:D005334
81 2606-2615 Disease denotes dry cough MESH:D003371
82 2617-2624 Disease denotes dyspnea MESH:D004417
83 2630-2639 Disease denotes pneumonia MESH:D011014
84 2801-2822 Disease denotes respiratory infection MESH:D012141
85 2845-2853 Disease denotes COVID-19 MESH:C000657245
86 2886-2894 Disease denotes COVID-19 MESH:C000657245
87 2977-2991 Disease denotes lung infection MESH:D012141
88 2995-3003 Disease denotes COVID-19 MESH:C000657245
89 3113-3121 Disease denotes COVID-19 MESH:C000657245
90 3942-3950 Disease denotes COVID-19 MESH:C000657245
91 4661-4669 Disease denotes COVID-19 MESH:C000657245
96 5438-5449 Disease denotes lung cancer MESH:D008175
97 5468-5477 Disease denotes pneumonia MESH:D011014
98 5501-5521 Disease denotes diabetic retinopathy MESH:D003920
99 5527-5553 Disease denotes retinal fundus photographs MESH:D012173
101 6635-6643 Disease denotes COVID-19 MESH:C000657245
104 7342-7350 Disease denotes COVID-19 MESH:C000657245
105 7524-7532 Disease denotes COVID-19 MESH:C000657245
107 8102-8110 Species denotes patients Tax:9606

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T2 2599-2604 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T3 2606-2615 Phenotype denotes dry cough http://purl.obolibrary.org/obo/HP_0031246
T4 2617-2624 Phenotype denotes dyspnea http://purl.obolibrary.org/obo/HP_0002094
T5 2630-2639 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T6 2801-2822 Phenotype denotes respiratory infection http://purl.obolibrary.org/obo/HP_0011947
T7 5438-5449 Phenotype denotes lung cancer http://purl.obolibrary.org/obo/HP_0100526
T8 5468-5477 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T9 5510-5521 Phenotype denotes retinopathy http://purl.obolibrary.org/obo/HP_0000488

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T12 0-12 Sentence denotes Introduction
T13 13-338 Sentence denotes Coronavirus disease 2019 (COVID-19), a highly infectious disease with the basic reproductive number (R0) of 5.7 (reported by the US Centers for Disease Control and Prevention), is caused by the most recently discovered coronavirus1 and was declared a global pandemic by the World Health Organization (WHO) on March 11, 20202.
T14 339-448 Sentence denotes It poses a serious threat to human health worldwide, as well as substantial economic losses to all countries.
T15 449-611 Sentence denotes As of 7 September 2020, 27,032,617 people have been infected by COVID-19 after testing, and 881,464 deaths have occurred, according to the statistics of the WHO3.
T16 612-755 Sentence denotes The Wall Street banks have estimated that the COVID-19 pandemic may cause losses of $5.5 trillion to the global economy over the next 2 years4.
T17 756-982 Sentence denotes The WHO recommends using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) for laboratory confirmation of the COVID-19 virus in respiratory specimens obtained by the preferred method of nasopharyngeal swabs5.
T18 983-1108 Sentence denotes Laboratories performing diagnostic testing for COVID-19 should strictly comply with the WHO biosafety guidance for COVID-196.
T19 1109-1397 Sentence denotes It is also necessary to follow the standard operating procedures (SOPs) for specimen collection, storage, packaging, and transport because the specimens should be regarded as potentially infectious, and the testing process can only be performed in a Biosafety Level 3 (BSL-3) laboratory7.
T20 1398-1495 Sentence denotes Not all cities worldwide have adequate medical facilities to follow the WHO biosafety guidelines.
T21 1496-1755 Sentence denotes According to an early report (Feb 17, 2020), the sensitivity of tests for the detection of COVID-19 using rRT-PCR analysis of nasopharyngeal swab specimens is around 30–60% due to irregularities during the collection and transportation of COVID-19 specimens8.
T22 1756-1857 Sentence denotes Recent studies reported a higher sensitivity range from 71% (Feb 19, 2020) to 91% (Mar 27, 2020)9,10.
T23 1858-2093 Sentence denotes A recent systematic review reported that the sensitivity of the PCR test for COVID-19 might be in the range of 71–98% (Apr 21, 2020), whereas the specificity of tests for the detection of COVID-19 using rRT-PCR analysis is about 95%11.
T24 2094-2325 Sentence denotes Yang et al.8 discovered that although no viral ribonucleic acid (RNA) was detected by rRT-PCR in the first three or all nasopharyngeal swab specimens in mild cases, the patient was eventually diagnosed with COVID-19 (Feb 17, 2020).
T25 2326-2446 Sentence denotes Therefore, the WHO has stated that one or more negative results do not rule out the possibility of COVID-19 infection12.
T26 2447-2543 Sentence denotes Additional auxiliary tests with relatively higher sensitivity to COVID-19 are urgently required.
T27 2544-2693 Sentence denotes The clinical symptoms associated with COVID-19 include fever, dry cough, dyspnea, and pneumonia, as described in the guideline released by the WHO13.
T28 2694-2856 Sentence denotes It has been recommended to use the WHO’s case definition for influenza-like illness (ILI) and severe acute respiratory infection (SARI) for monitoring COVID-1913.
T29 2857-3012 Sentence denotes As reported by the CHINA-WHO COVID-19 joint investigation group (February 28, 2020)14, autopsies showed the presence of lung infection in COVID-19 victims.
T30 3013-3191 Sentence denotes Therefore, medical imaging of the lungs might be a suitable auxiliary diagnostic testing method for COVID-19 since it uses available medical technology and clinical examinations.
T31 3192-3362 Sentence denotes Chest radiography (CXR) and chest computed tomography (CT) are the most common medical imaging examinations for the lungs and are available in most hospitals worldwide15.
T32 3363-3539 Sentence denotes Different tissues of the body absorb X-rays to different degrees16, resulting in grayscale images that allow for the detection of anomalies based on the contrast in the images.
T33 3540-3660 Sentence denotes CT differs from normal CXR in that it has superior tissue contrast with different shades of gray (about 32–64 levels)17.
T34 3661-3749 Sentence denotes The CT images are digitally processed18 to create a three-dimensional image of the body.
T35 3750-3818 Sentence denotes However, CT examinations are more expensive than CXR examinations19.
T36 3819-3956 Sentence denotes Recent studies reported that the use of CXR and CT images resulted in improved diagnostic sensitivity for the detection of COVID-1920,21.
T37 3957-4051 Sentence denotes The interpretation of medical images is time-consuming, labor-intensive, and often subjective.
T38 4052-4151 Sentence denotes The medical images are first annotated by experts to generate a report of the radiography findings.
T39 4152-4265 Sentence denotes Subsequently, the radiography findings are analyzed, and clinical factors are considered to obtain a diagnosis15.
T40 4266-4504 Sentence denotes However, during the current pandemic, the frontline expert physicians are faced with a massive workload and lack of time, which increases the physical and psychological burden on staff and might adversely affect the diagnostic efficiency.
T41 4505-4706 Sentence denotes Since modern hospitals have advanced digital imaging technology, medical image processing methods may have the potential for fast and accurate diagnosis of COVID-19 to reduce the burden on the experts.
T42 4707-5046 Sentence denotes Deep learning (DL) methods, especially convolutional neural networks (CNNs), are effective approaches for representation learning using multilayer neural networks22 and have provided excellent performance solutions to many problems in image classification23,24, object detection25, games and decisions26, and natural language processing27.
T43 5047-5177 Sentence denotes A deep residual network28 is a type of CNN architecture that uses the strategy of skip connections to avoid degradation of models.
T44 5178-5349 Sentence denotes However, the applications of DL for clinical diagnoses remains limited due to the lack of interpretability of the DL model and the multi-modal properties of clinical data.
T45 5350-5556 Sentence denotes Some studies have demonstrated excellent performance of DL methods for the detection of lung cancer with CT images29, pneumonia with CXR images30, and diabetic retinopathy with retinal fundus photographs31.
T46 5557-5714 Sentence denotes To the best of our knowledge, the DL method has been validated only on single modal data, and no correlation analysis with clinical indicators was performed.
T47 5715-5863 Sentence denotes Traditional machine learning methods are more constrained and better suited than DL methods to specific, practical computing tasks using features32.
T48 5864-6098 Sentence denotes As demonstrated by Jin et al., the traditional machine learning algorithm using the scale-invariant feature transform (SIFT)33 and random sample consensus (RANSAC)34 may outperform the state-of-the-art DL methods for image matching35.
T49 6099-6283 Sentence denotes We designed a general end-to-end DL framework for information extraction from CXR images (X-data) and CT images (CT-data) that can be considered a cross-domain transfer learning model.
T50 6284-6531 Sentence denotes In this study, we developed a custom platform for rapid expert annotation and proposed the modular CNN-based multi-stage framework (classification framework and regression framework) consisting of basic component units and special component units.
T51 6532-6644 Sentence denotes The framework represents an auxiliary examination method for high precision and automated detection of COVID-19.
T52 6645-6690 Sentence denotes This study makes the following contributions:
T53 6691-6914 Sentence denotes First, a multi-stage CNN-based classification framework consisting of two basic units (ResBlock-A and ResBlock-B) and a special unit (control gate block) was established for use with multi-modal images (X-data and CT-data).
T54 6915-7020 Sentence denotes The classification results were compared with evaluations by experts with different levels of experience.
T55 7021-7197 Sentence denotes Different optimization goals were established for the different stages in the framework to obtain good performances, which were evaluated using multiple statistical indicators.
T56 7198-7367 Sentence denotes Second, principal component analysis (PCA) was used to determine the characteristics of the X-data and CT-data of different categories (normal, COVID-19, and influenza).
T57 7368-7533 Sentence denotes Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the salient features in the images and extract the lesion areas associated with COVID-19.
T58 7534-7776 Sentence denotes Third, data preprocessing methods, including pseudo-coloring and dimension normalization, were developed to facilitate the interpretability of the medical images and adapt the proposed framework to the multi-modal images (X-data and CT-data).
T59 7777-7955 Sentence denotes Fourth, A knowledge distillation method was adopted as a training strategy to obtain high performance with low computational requirements and improve the usability of the method.
T60 7956-8111 Sentence denotes Last, The CNN-based regression framework was used to describe the relationships between the radiography findings and the clinical symptoms of the patients.
T61 8112-8241 Sentence denotes Multiple evaluation indicators were used to assess the correlations between the radiography findings and the clinical indicators.