LitCovid-sentences  

PMC:7782580 / 42107-64393 JSONTXT 3 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T312 0-7 Sentence denotes Methods
T313 9-28 Sentence denotes Data sets splitting
T314 29-205 Sentence denotes We used the multi-modal data sets from four public data sets and one hospital (Youan hospital) in our research and split the hybrid data set in the following manner.For X-data:
T315 206-348 Sentence denotes The CXR images of COVID-19 cases collected from the public CCD52 contained 212 patients diagnosed with COVID-19 and were resized to 512 × 512.
T316 349-422 Sentence denotes Each image contained 1–2 suspected areas with inflammatory lesions (SAs).
T317 423-522 Sentence denotes We also collected 5100 normal cases and 3100 pneumonia cases from another public data set (RSNA)53.
T318 523-744 Sentence denotes In addition, The CXR images collected from the Youan hospital contained 45 cases diagnosed with COVID-19, 503 normal cases, 435 cases diagnosed with pneumonia (not COVID-19 patients), and 145 cases diagnosed as influenza.
T319 745-851 Sentence denotes The CXR images collected from the Youan hospital were obtained using the Carestream DRX-Revolution system.
T320 852-969 Sentence denotes All the CXR images of COVID-19 cases were analyzed by the two experienced radiologists to determine the lesion areas.
T321 970-1149 Sentence denotes The X-data of the normal cases (XNPDS), that of the pneumonia cases (XPPDS), and that of the COVID-19 cases (XCPDS) from public data sets constituted the X public data set (XPDS).
T322 1150-1333 Sentence denotes The X-data of the normal cases (XNHDS), that of the pneumonia cases (XPHDS), and that of the COVID-19 cases (XCHDS) from the Youan hospital constituted the X hospital data set (XHDS).
T323 1334-1346 Sentence denotes For CT-data:
T324 1347-1829 Sentence denotes We collected CT-data of 120 normal cases from a public lung CT-data set (LUNA16, a large data set for automatic nodule detection in the lungs54), which was a subset of LIDC-IDRI (The LIDC-IDRI contains a total of 1018 helical thoracic CT scans collected using manufacturers from eight medical imaging companies including AGFA Healthcare, Carestream Health, Inc., Fuji Photo Film Co., GE Healthcare, iCAD, Inc., Philips Healthcare, Riverain Medical, and Siemens Medical Solutions)55.
T325 1830-1995 Sentence denotes It was confirmed by the two experienced radiologists from the Youan Hospital that no lesion areas of COVID-19, pneumonia, or influenza were present in the 120 cases.
T326 1996-2138 Sentence denotes We also collected the CT-data of pneumonia cases from a public data set (images of COVID-19 positive and negative pneumonia patients: ICNP)56.
T327 2139-2311 Sentence denotes The CT-data collected from the Youan hospital contained 95 patients diagnosed with COVID-19, 50 patients diagnosed with influenza and 215 patients diagnosed with pneumonia.
T328 2312-2480 Sentence denotes The images of the CT scans collected from the Youan hospital were obtained using the PHILIPS Brilliance iCT 256 system (Which was also used for the LIDC-IDRI data set).
T329 2481-2594 Sentence denotes The slice thickness of the CT scans was 5 mm, and the CT-data images were grayscale images with 512 × 512 pixels.
T330 2595-2785 Sentence denotes Areas with 2–5 SAs were annotated by the two experienced radiologists using a rapid keystroke-entry format in the images for each case, and these areas ranged from 16 × 16 to 64 × 64 pixels.
T331 2786-2937 Sentence denotes The CT-data of the normal cases (CTNPDS) and that of the pneumonia cases (CTPPDS) from the public data sets constituted the CT public data set (CTPDS).
T332 2938-3182 Sentence denotes The CT-data of the COVID-19 cases from the Youan hospital (CTCHDS), the influenza cases from the Youan hospital (CTIHDS), and the normal cases from the Youan hospital (CTNHDS) constituted the CT hospital (clinically-diagnosed) data set (CTHDS).
T333 3183-3211 Sentence denotes For clinical indicator data:
T334 3212-3438 Sentence denotes Five clinical indicators (white blood cell count, neutrophil percentage, lymphocyte percentage, procalcitonin, C-reactive protein) of 95 COVID-19 cases were obtained from the Youan hospital, as shown in Supplementary Table 20.
T335 3439-3695 Sentence denotes A total of 95 data pairs from the 95 COVID-19 cases (369 images of the lesion area and the 95 × 5 clinical indicators) were collected from the Youan hospital for the correlation analysis of the lesion areas of the COVID-19 and the five clinical indicators.
T336 3696-3803 Sentence denotes The images of the SAs and the clinical indicator data constituted the correlation analysis data set (CADS).
T337 3804-3923 Sentence denotes We split the XPDS, XHDS, CTPDS, CTHDS, and CADS into the training-validation (train-val) and test data sets using TTSF.
T338 3924-4030 Sentence denotes The details of the hybrid data sets for the public data sets and Youan hospital data are shown in Table 1.
T339 4031-4118 Sentence denotes The train-val part of CTHDS is referred to as CTHTS, and the test part is called CTHVS.
T340 4119-4260 Sentence denotes The same naming scheme was adopted for XPDS, XHDS, CTPDS, and CADS, i.e., XPTS, XPVS, XHTS, XHVS, CTPTS, CTPVS, CATS, and CAVS, respectively.
T341 4261-4445 Sentence denotes The training-validation part of the four public data sets and the hospital (Youan Hospital) data set were mixed for X-data and CT-data, which were named as XMTS and CTMTS respectively.
T342 4446-4519 Sentence denotes While the test parts were split in the same way and named XMVS and CTMVS.
T343 4521-4540 Sentence denotes Image preprocessing
T344 4541-4723 Sentence denotes All image data (X-data and CT-data) in the DICOM format were loaded using the Pydicom library (version 1.4.0) and processed as arrays using the Numpy library (version 1.16.0).X-data:
T345 4724-4907 Sentence denotes The two-dimensional array (x axis and y axis) of the image of the X-data (size of 512 × 512) was normalized to pixel values of 0–255 and stored in png format using the OpenCV library.
T346 4908-4976 Sentence denotes Each preprocessed image was resized to 512 × 512 and had 3 channels.
T347 4977-4985 Sentence denotes CT-data:
T348 4986-5147 Sentence denotes The array of the CT-data was three-dimensional (x axis, y axis, and z axis), and the length of the z axis was ~300, which represented the number of image slices.
T349 5148-5224 Sentence denotes Each image slice was two-dimensional (x axis and y axis, size of 512 × 512).
T350 5225-5412 Sentence denotes As shown in Fig. 1b, the array of the image was divided into three groups in the z axis direction, and each group contained 100 image slices (each case was resampled to 300 image slices).
T351 5413-5543 Sentence denotes The image slices in each group were processed using a window center of −600 and a window width of 2000 to extract the lung tissue.
T352 5544-5682 Sentence denotes The images of the CT-data with 300 image slices were normalized to pixel values of 0–255 and stored in npy format using the Numpy library.
T353 5683-5892 Sentence denotes A convolution filter was applied with three 1 × 1 convolution kernels to preprocess the CT-data, which is a trainable layer with the aim of normalizing the input; the image size was 512 × 512, with 3 channels.
T354 5894-5928 Sentence denotes Annotation tool for medical images
T355 5929-6054 Sentence denotes The server program of the annotation tool was deployed in a computer with large network bandwidth and abundant storage space.
T356 6055-6190 Sentence denotes The client program of the annotation tool was deployed in the office computer of the experts, who were given unique user IDs for login.
T357 6191-6351 Sentence denotes The interface of the client program had a built-in image viewer with a window size of 512 × 512 and an export tool for obtaining the annotations in text format.
T358 6352-6575 Sentence denotes Multiple drawing tools were provided to annotate the lesion area in the images, including a rectangle tool for drawing a bounding box around the target, a polygon tool for outlining the target, and a circle tool the target.
T359 6576-6646 Sentence denotes Multiple categories could be defined and assigned to the target areas.
T360 6647-6873 Sentence denotes All annotations were stored in a structured query language (SQL) database, and the export tool was used to export the annotations to two common file formats (comma-separated values (csv) and JavaScript object notation (json)).
T361 6874-6921 Sentence denotes The experts could share the annotation results.
T362 6922-7059 Sentence denotes Since the size of the X-data and the CT slice-data were identical, the annotations for both data were performed with the annotation tool.
T363 7060-7155 Sentence denotes Here we use one image slice of the CT-data as an example to demonstrate the annotation process.
T364 7156-7225 Sentence denotes In this study, two experts were asked to annotate the medical images.
T365 7226-7286 Sentence denotes The normal cases were reviewed and confirmed by the experts.
T366 7287-7381 Sentence denotes The abnormal cases, including the COVID-19 and influenza cases, were annotated by the experts.
T367 7382-7472 Sentence denotes Bounding boxes of the lesion areas in the images were annotated using the annotation tool.
T368 7473-7533 Sentence denotes In general, each case contained 2–5 slices with annotations.
T369 7534-7677 Sentence denotes The cases with the annotated slices were considered positive cases, and each case was assigned to a category (COVID-19 case or influenza case).
T370 7678-7743 Sentence denotes The pipeline of the annotation was shown in Supplementary Fig. 1.
T371 7745-7776 Sentence denotes Model architecture and training
T372 7777-8008 Sentence denotes In this study, we proposed a modular CNNCF to identify the COVID-19 cases in the medical images and a CNNRF to determine the relationships between the lesion areas in the medical images and the five clinical indicators of COVID-19.
T373 8009-8085 Sentence denotes Both proposed frameworks consisted of two units (ResBlock-A and ResBlock-B).
T374 8086-8188 Sentence denotes The CNNCF and CNNRF had unique units, namely the control gate block and regressor block, respectively.
T375 8189-8314 Sentence denotes Both frameworks were implemented using two NVIDIA GTX 1080TI graphics cards and the open-source PyTorch framework.ResBlock-A:
T376 8315-8335 Sentence denotes As discussed in ref.
T377 8336-8477 Sentence denotes 57, the residual block is a CNN-based block that allows the CNN models to reuse features, thus accelerating the training speed of the models.
T378 8478-8638 Sentence denotes In this study, we developed a residual block (ResBlock-A) that utilized a skip-connection for retaining features in different layers in the forward propagation.
T379 8639-8851 Sentence denotes This block (Fig. 6a) consisted of a multiple-input multiple-output structure with two branches (an upper branch and a bottom branch), where input 1 and input 2 have the same size, but the values may be different.
T380 8852-8945 Sentence denotes In contrast, output 1 and output 2 had the same size, but output 1 did not have a ReLu layer.
T381 8946-9073 Sentence denotes The upper branch consisted of a max-pooling layer (Max-Pooling), a convolution layer (Conv 1 × 1), and a batch norm layer (BN).
T382 9074-9308 Sentence denotes The Max-Pooling had a kernel size of 3 × 3 and a stride of 2 to downsample the input 1 for retaining the features and ensuring the same size as the output layer before the element-wise add operation was conducted in the bottom branch.
T383 9309-9487 Sentence denotes The Conv 1 × 1 consisted of multiple 1 × 1 convolution kernels with the same number as that in the second convolution layer in the bottom branch to adjust the number of channels.
T384 9488-9637 Sentence denotes The BN used a regulation function to ensure the input in each layer of the model followed a normal distribution with a mean of 0 and a variance of 1.
T385 9638-9728 Sentence denotes The bottom branch consisted of two convolution layers, two BN layers, and two ReLu layers.
T386 9729-9937 Sentence denotes The first convolution layer in the bottom branch consisted of multiple 3 × 3 convolution kernels with a stride of 2 and a padding of 1 to reduce the size of the feature maps when local features were obtained.
T387 9938-10074 Sentence denotes The second convolution layer in the bottom branch consisted of multiple 3 × 3 convolution kernels with a stride of 1 and a padding of 1.
T388 10075-10194 Sentence denotes The ReLu function was used as the activation function to ensure a non-linear relationship between the different layers.
T389 10195-10329 Sentence denotes The output of the upper branch and the output of the bottom branch after the second BN were fused using an element-wise add operation.
T390 10330-10416 Sentence denotes The fused result was output 1, and the fused result after the ReLu layer was output 2.
T391 10417-10465 Sentence denotes Fig. 6 The four units of the proposed framework.
T392 10466-11146 Sentence denotes a ResBlock-A architecture, containing two convolution layers with 3 × 3 kernels, one convolution layer with a 1 × 1 kernel, three batch normalization layers, two ReLu layers, and one max-pooling layer with a 3 × 3 kernel. b ResBlock-B architecture; the basic unit is the same as the ResBlock-A, except for output 1. c The Control Gate Block has a synaptic-based frontend architecture that controls the direction of the feature map flow and the overall optimization direction of the framework. d The Regressor architecture is a skip-connection architecture containing one convolution layer with 3 × 3 kernels, one batch normalization layer, one ReLu layer, and three linear layers.
T393 11147-11158 Sentence denotes ResBlock-B:
T394 11159-11295 Sentence denotes The ResBlock-B (Fig. 6b) was a multiple-input single-output block that was similar to the ResBlock-A, except that there was no output 1.
T395 11296-11446 Sentence denotes The value of the stride and padding in each layer of the ResBlock-A and ResBlock-B could be adjusted using hyper-parameters based on the requirements.
T396 11447-11466 Sentence denotes Control Gate Block:
T397 11467-11720 Sentence denotes As shown in Fig. 6c, the Control Gate Block was a multiple-input single-output block consisting of a predictor module, a counter module, and a synapses module to control the optimization direction while controlling the information flow in the framework.
T398 11721-11845 Sentence denotes The pipeline of the predictor module is shown in Supplementary Fig. 19a, where the Input S1 is the output of the ResBlock-B.
T399 11846-11947 Sentence denotes The Input S1 was then flattened to a one-dimensional feature vector as the input of the linear layer.
T400 11948-12054 Sentence denotes The output of the linear layer was converted to a probability of each category using the softmax function.
T401 12055-12203 Sentence denotes A sensitivity calculator used the Vpred and Vtrue as inputs to calculate the TP, TN, FP, and false-negative (FN) rates to calculate the sensitivity.
T402 12204-12303 Sentence denotes The sensitivity calculation was followed by a step function to control the output of the predictor.
T403 12304-12450 Sentence denotes The ths was a threshold value; if the calculated sensitivity was greater or equal to ths, the step function output 1; otherwise, the output was 0.
T404 12451-12532 Sentence denotes The counter module was a conditional counter, as shown in Supplementary Fig. 19b.
T405 12533-12598 Sentence denotes If the input n was zero, the counter was cleared and set to zero.
T406 12599-12637 Sentence denotes Otherwise, the counter increased by 1.
T407 12638-12672 Sentence denotes The output of the counter was num.
T408 12673-12822 Sentence denotes The synapses block mimicked the synaptic structure, and the input variable num was similar to a neurotransmitter, as shown in Supplementary Fig. 19c.
T409 12823-12882 Sentence denotes The input num was the input parameter of the step function.
T410 12883-13011 Sentence denotes The ths was a threshold value; if the input num was greater or equal to ths, the step function output 1; otherwise, it output 0.
T411 13012-13115 Sentence denotes An element-wise multiplication was performed between the input S1 and the output of the synapses block.
T412 13116-13171 Sentence denotes The multiplied result was passed on to a discriminator.
T413 13172-13272 Sentence denotes If the sum of each element in the result was not zero, the Input S1 was passed on to the next layer.
T414 13273-13327 Sentence denotes Otherwise, the input S1 information was not passed on.
T415 13328-13344 Sentence denotes Regressor block:
T416 13345-13473 Sentence denotes The regressor block consisted of multiple linear layers, a convolution layer, a BN layer, and a ReLu layer, as shown in Fig. 6d.
T417 13474-13616 Sentence denotes A skip-connection architecture was adopted to retain the features and increase the ability of the block to represent non-linear relationships.
T418 13617-13747 Sentence denotes The convolution block in the skip-connection structure was a convolution layer with multiple numbers of 1 × 1 convolution kernels.
T419 13748-13903 Sentence denotes The number of the convolution kernels was the same as that of the output size of the second linear layer to ensure the consistency of the vector dimension.
T420 13904-14005 Sentence denotes The input size and output size of each linear layer were adjustable to be applicable to actual cases.
T421 14006-14148 Sentence denotes Based on the four blocks, two frameworks were designed for the classification task and regression task, respectively.Classification framework:
T422 14149-14214 Sentence denotes The CNNCF consisted of stage I and stage II, as shown in Fig. 3a.
T423 14215-14286 Sentence denotes Stage I was duplicated Q times in the framework (in this study, Q = 1).
T424 14287-14409 Sentence denotes It consisted of multiple ResBlock-A with a number of M (in this study, M = 2), one ResBlock-B, and one Control Gate Block.
T425 14410-14513 Sentence denotes Stage II consisted of multiple ResBlock-A with a number of N (in this study, N = 2) and one ResBlock-B.
T426 14514-14660 Sentence denotes The weighted cross-entropy loss function was used and was minimized using the SGD optimizer with a learning rate of a1 (in this study, a1 = 0.01).
T427 14661-14911 Sentence denotes A warm-up strategy58 was used in the initialization of the learning rate for a smooth training start, and a reduction factor of b1 (in this study, b1 = 0.1) was used to reduce the learning rate after every c1 (in this study, c1 = 10) training epochs.
T428 14912-15050 Sentence denotes The model was trained for d1 (in this study, d1 = 40) epochs, and the model parameters saved in the last epoch was used in the test phase.
T429 15051-15072 Sentence denotes Regression framework:
T430 15073-15145 Sentence denotes The CNNRF (Fig. 3b) consisted of two parts (stage II and the regressor).
T431 15146-15390 Sentence denotes The inputs to the regression framework were the images of the lesion areas, and the output was the corresponding vector with five dimensions, representing the five clinical indicators (all clinical indicators were normalized to a range of 0–1).
T432 15391-15495 Sentence denotes The stage II structure was the same as that in the classification framework, except for some parameters.
T433 15496-15639 Sentence denotes The loss function was the MSE loss function, which was minimized using the SGD optimizer with a learning rate of a2 (in this study, a2 = 0.01).
T434 15640-15888 Sentence denotes A warm-up strategy was used in the initialization of the learning rate for a smooth training start, and a reduction factor of b2 (in this study, b2 = 0.1) was used to reduce the learning rate after every c2 (in this study, c2 = 50) training epochs.
T435 15889-16033 Sentence denotes The framework was trained for d2 (in this study, d2 = 200) epochs, and the model parameters saved in the last epoch were used in the test phase.
T436 16034-16079 Sentence denotes The workflow of the classification framework.
T437 16080-16153 Sentence denotes The workflow of the classification framework was demonstrated in Fig. 3c.
T438 16154-16282 Sentence denotes The preprocessed images are sent to the first convolution block to expand the channels and processed as the input for the CNNCF.
T439 16283-16425 Sentence denotes Given the input Fi with a size of M × N × 64, the stage I output feature maps F′i with a size of M/8 × N/8 × 256 in the default configuration.
T440 16426-16565 Sentence denotes As we introduced above, the Control Gate Block controls the optimization direction while controlling the information flow in the framework.
T441 16566-16648 Sentence denotes If the Control Gate Block is open, the feature maps F′i are passed on to stage II.
T442 16649-16954 Sentence denotes Given the input F′i, the stage II output the feature maps F″i with a size of M/64 × N/64 × 512 which is defined as follows:1 Fi′=S1(Fi)Fi″=S2(Fi′)⊗CGB(Fi′),where S1 denotes the stage I block, S2 denotes the stage II block, and CGB is the Control Gate Block. ⊗ is the element-wise multiplication operation.
T443 16955-17111 Sentence denotes Stage II is Followed by a global average pooling layer (GAP) and a fully connect layer (FC layer) with a softmax function to generate the final predictions.
T444 17112-17202 Sentence denotes Given F″i as input, the GAP is adopted to generate a vector Vf with a size of 1 × 1 × 512.
T445 17203-17570 Sentence denotes Given Vf as input, the FC layer with the softmax function outputs a vector Vc with a size of 1 × 1 × C.2 Vf=GAPFi′Vc=SMaxFCVf,where GAP is the global average pooling layer, the FC is the fully connect layer, SMax is the softmax function, Vf is the feature vector generated by the GAP, Vc is the prediction vector, and C is the number of case types used in this study.
T446 17572-17649 Sentence denotes Training strategies and evaluation indicators of the classification framework
T447 17650-17743 Sentence denotes The training strategies and hyper-parameters of the classification framework were as follows.
T448 17744-17922 Sentence denotes We adopted a knowledge distillation method (Fig. 7) to train the CNNCF as a student network with one stage I block and one stage II block, each of which contained two ResBlock-A.
T449 17923-18127 Sentence denotes Four teacher networks (the hyper-parameters are provided in Supplementary Table 21) with the proposed blocks were trained on the train-val part of each sub-data set using a 5-fold cross-validation method.
T450 18128-18197 Sentence denotes All networks were initialized using the Xavier initialization method.
T451 18198-18276 Sentence denotes The initial learning rate was 0.01, and the optimization function was the SGD.
T452 18277-18387 Sentence denotes The CNNCF was trained using the image data and the label, as well as the fused output of the teacher networks.
T453 18388-18510 Sentence denotes The comparison of RT-PCR test results using throat specimen and the CNNCF results were provided in Supplementary Table 22.
T454 18511-18588 Sentence denotes Supplementary Fig. 20 shows the details of the knowledge distillation method.
T455 18589-18705 Sentence denotes The definitions and details of the five evaluation indicators used in this study were given in Supplementary Note 2.
T456 18706-18805 Sentence denotes Fig. 7 Knowledge distillation consisting of multiple teacher networks and a target student network.
T457 18806-18906 Sentence denotes The knowledge is transferred from the teacher networks to the student network using a loss function.
T458 18908-18947 Sentence denotes Gradient-weighted class activation maps
T459 18948-19091 Sentence denotes Grad-CAM59 in the Pytorch framework was used to visualize the salient features that contributed the most to the prediction output of the model.
T460 19092-19338 Sentence denotes Given a target category, the Grad-CAM performed back-propagation to obtain the final CNN feature maps and the gradient of the feature maps; only pixels with positive contributions to the specified category were retained through the ReLU function.
T461 19339-19538 Sentence denotes The Grad-CAM method was used for all test data set (X-data and CT-data) in the CNNCF without changing the framework structure to obtain a visual output of the framework’s high discriminatory ability.
T462 19540-19570 Sentence denotes Statistics and reproducibility
T463 19571-19689 Sentence denotes We used multiple statistical indices and empirical distributions to assess the performance of the proposed frameworks.
T464 19690-19849 Sentence denotes The equations of the statistical indices are shown in Supplementary Fig. 21 and all the abbreviations used in this study are defined in Supplementary Table 23.
T465 19850-19978 Sentence denotes All the data used in this study followed the criteria: (1) sign informed consent prior to enrollment. (2) At least 18 years old.
T466 19979-20110 Sentence denotes This study was conducted following the declaration of Helsinki and was approved by the Capital Medical University Ethics Committee.
T467 20111-20312 Sentence denotes The following statistical analyses of the data were conducted for both evaluating the classification framework and the regression framework.Statistical indices to evaluate the classification framework.
T468 20313-20540 Sentence denotes Multiple evaluation indicators (PRC, ROC, AUPRC, AUROC, sensitivity, specificity, precision, kappa index, and F1 with a fixed threshold) were computed for a comprehensive and accurate assessment of the classification framework.
T469 20541-20657 Sentence denotes Multiple threshold values were in the range from 0 to 1 with a step value of 0.005 to obtain the ROC and PRC curves.
T470 20658-20824 Sentence denotes The PRC showed the relationship between the precision and the sensitivity (or recall), and the ROC indicated the relationship between the sensitivity and specificity.
T471 20825-20912 Sentence denotes The two curves reflected the comprehensive performance of the classification framework.
T472 20913-21017 Sentence denotes The kappa index is a statistical method for assessing the degree of agreement between different methods.
T473 21018-21097 Sentence denotes In our use case, the indicator was used to measure the stability of the method.
T474 21098-21190 Sentence denotes The F1 score is a harmonic average of precision and sensitivity and considers the FP and FN.
T475 21191-21283 Sentence denotes The bootstrapping method was used to calculate the empirical distribution of each indicator.
T476 21284-21477 Sentence denotes The detailed calculation process was as follows: we conducted random sampling with replacement to generate 1000 new test data sets with the same number of samples as the original test data set.
T477 21478-21551 Sentence denotes The evaluation indicators were calculated to determine the distributions.
T478 21552-21625 Sentence denotes The results were displayed in boxplots (Fig. 5 and Supplementary Fig. 2).
T479 21626-21683 Sentence denotes Statistical indices to evaluate the regression framework.
T480 21684-21831 Sentence denotes Multiple evaluation indicators (MSE, RMSE, MAE, R2, and PCC) were computed for a comprehensive and accurate assessment of the regression framework.
T481 21832-21914 Sentence denotes The MSE was used to calculate the deviation between the predicted and true values.
T482 21915-21962 Sentence denotes The RMSE was the square root of the MSE result.
T483 21963-22024 Sentence denotes The two indicators show the accuracy of the model prediction.
T484 22025-22099 Sentence denotes The R2 was used to assess the goodness-of-fit of the regression framework.
T485 22100-22191 Sentence denotes The r was used to assess the correlation between two variables in the regression framework.
T486 22192-22286 Sentence denotes The indicators were calculated using the open-source tools scikit-learn and the scipy library.