Notes

Muddling Over the Health System: Encounters of Three Groups of Dark Females in Three Regions.
To propose a coupled convolutional and graph convolutional network (CCGCN) model for diagnosis of Alzheimer's disease (AD) and its prodromal stage.

The disease-related brain regions generated by group-wise comparison were used as the input. The convolutional neural networks (CNNs) were used to extract disease-related features from different locations on brain magnetic resonance (MR) images. The generated features via the graph convolutional network (GCN) were processed, and graph pooling was performed to analyze the inherent relationship between the brain topology and the diagnosis task adaptively. Through ADNI dataset, we acquired the accuracy, sensitivity and specificity of the diagnosis tasks for AD and its prodromal stages, followed by an ablation study on the model structure.

The CCGCN model outperformed the current state-of-the-art methods and showed a classification accuracy of 92.5% for AD with a sensitivity of 88.1% and a specificity of 96.0%.

Based on the structural and topological features of the brain MR images, the proposed CCGCN model shows excellent performance in AD diagnosis and is expected to provide important assistance to physicians in disease diagnosis.
Based on the structural and topological features of the brain MR images, the proposed CCGCN model shows excellent performance in AD diagnosis and is expected to provide important assistance to physicians in disease diagnosis.
To evaluate the diagnostic efficacy of Kaiser score for breast lesions presenting as non-mass enhancement.

We collected data from patients with breast lesions presenting as non-mass enhancement on preoperative DCE-MRI between January, 2014 and June, 2019. All the cases were confirmed by surgical pathology or puncture biopsy. this website With pathology results as the gold standard, we evaluated the diagnostic efficacy of Kaiser score and MRI BI-RADS classification and the consistency between the diagnostic results by the two methods and the pathological results.

A total of 90 lesions were detected in 88 patients, including 28 benign lesions (31.1%) and 62 malignant lesions (68.9%). For diagnosis of the lesions, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of Kaiser Score were 100%, 75%, 89.9%, 100% and 92%, as compared with 93.5%, 46.4%, 79.5%, 76.5% and 78.9% of MRI BI-RADS, respectively. The diagnostic specificity of Kaiser score was significantly higher than that of BI-RADS classification (
=0.021).

The Kaiser score system provides a diagnostic strategy for BI-RADS classification of breast lesions with non-mass enhancement and has a better diagnostic efficacy than BI-RADS classification alone. The use of Kaiser score can significantly improve the diagnostic specificity of such breast lesions for inexperienced radiologists.
The Kaiser score system provides a diagnostic strategy for BI-RADS classification of breast lesions with non-mass enhancement and has a better diagnostic efficacy than BI-RADS classification alone. The use of Kaiser score can significantly improve the diagnostic specificity of such breast lesions for inexperienced radiologists.
To evaluate the value of Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score Ⅱ (SAPS-Ⅱ), Oxford Acute Severity of Illness Score (OASIS) and Logistic Organ Dysfunction System (LODS) scoring systems for predicting ICU mortality in patients with sepsis.

We collected the data of a total of 2470 cases of sepsis recorded in the MIMIC-III database from 2001 to 2012 and retrieved the scores of SOFA, SAPS-Ⅱ, OASIS and LODS of the patients within the first day of ICU admission. We compared with the score between the survivors and the non-survivors and analyzed the differences in the area under the ROC curve (AUC) of the 4 scoring systems. Binomial logistic regression was performed to compare the predictive value of the 4 scoring systems for ICU mortality of the patients.

In the 2470 patients with sepsis, 1966 (79.6%) survived and 504 (20.4%) died in the ICU. Compared with the survivors, the non-survivors had a significantly older mean age, higher proportion of patients receiving mechanic.768 (0.745-0.791) and 0.762 (0.738-0.785), respectively, were significantly higher than those of the other two scoring systems. Binomial logistic regression showed the corrected SOFA, SAPS-Ⅱ, and OASIS scores, but not LODS scores, were significantly correlated with ICU mortality in patients with sepsis, and their ORs were 1.08 (95% CI 1.03-1.14,
=0.001), 1.04 (95% CI 1.02-1.05,
< 0.001), 1.04 (95% CI 1.01-1.06,
=0.001), 0.96 (95% CI 0.89-1.04,
=0.350), respectively.

The scores of SOFA, SAPS-Ⅱ, OASIS, and LODS can predict ICU mortality in patients with sepsis, but SAPS-Ⅱ and OASIS scores have better predictive value than SOFA and LODS scores.
The scores of SOFA, SAPS-Ⅱ, OASIS, and LODS can predict ICU mortality in patients with sepsis, but SAPS-Ⅱ and OASIS scores have better predictive value than SOFA and LODS scores.
To investigate the role of pharmacist-led anticoagulation monitoring service for warfarin anticoagulation therapy in patients during hospitalization.

We retrospectively analyzed the data of 421 patients receiving warfarin anticoagulation therapy during hospitalization between April, 2016 and December, 2017. Of these patients, 316 received daily pharmacist-led anticoagulation monitoring service including checking the patients' International Normalized Ratio (INR) and other pertinent laboratory test results and reviewing medication changes and the patients' clinical status (monitoring group); the other 105 patients receiving warfarin anticoagulation therapy without pharmaceutical care served as the control group. The data including compliance rate of anticoagulant indicators, incidence and rate of prompt management of INR alert, thrombosis and bleeding events during hospitalization were analyzed among these patients.

Compared with the control patients, the patients in the monitoring group showed a signifipitalization.
To establish an algorithm based on 3D convolution neural network to segment the organs at risk (OARs) in the head and neck on CT images.

We propose an automatic segmentation algorithm of head and neck OARs based on V-Net. To enhance the feature expression ability of the 3D neural network, we combined the squeeze and exception (SE) module with the residual convolution module in V-Net to increase the weight of the features that has greater contributions to the segmentation task. Using a multi-scale strategy, we completed organ segmentation using two cascade models for location and fine segmentation, and the input image was resampled to different resolutions during preprocessing to allow the two models to focus on the extraction of global location information and local detail features respectively.

Our experiments on segmentation of 22 OARs in the head and neck indicated that compared with the existing methods, the proposed method achieved better segmentation accuracy and efficiency, and the average segmentation accuracy was improved by 9%.
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