AIM: To investigate whether illness severity has an impact on gastric residual volume (GRV) in medical critically ill patients. METHODS: Medical intensive care unit (ICU) patients requiring nasogastric feeding were en...AIM: To investigate whether illness severity has an impact on gastric residual volume (GRV) in medical critically ill patients. METHODS: Medical intensive care unit (ICU) patients requiring nasogastric feeding were enrolled. Sequential Organ Failure Assessment (SOFA) score was assessed immediately preceding the start of the study. Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ scores were recorded on the first, fourth, seventh, and fourteenth day of the study period. GRV was measured every 4 h during enteral feeding. The relationship be-tween mean daily GRV and SOFA scores and the correlation between mean daily GRV and mean APACHE Ⅱ score of all patients were evaluated and compared. RESULTS: Of the 61 patients, 43 patients were survivors and 18 patients were non-survivors. The mean daily GRV increased as SOFA scores increased (P < 0.001, analysis of variance). Mean APACHE Ⅱ scores of all patients correlated with mean daily GRV (P = 0.011, Pearson correlation) during the study period. Patients with decreasing GRV in the first 2 d had better survival than patients without decreasing GRV (P = 0.017, log rank test). CONCLUSION: GRV is higher in more severely ill medical ICU patients. Patients with decreasing GRV had lower ICU mortality than patients without decreasing GRV.展开更多
Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper.Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden ...Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper.Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors,simplify the diagnosis and treatment process,and improve the quality of diagnosis.Methods Firstly,data enhancement,image resizings,and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34)is constructed to extract the characteristics of psoriasis.Finally,we used the Adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model,and obtained an optimized ResNet-34 model for psoriasis diagnosis.Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate,F1-score and ROC curve.Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis,and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.展开更多
基金Supported by Kaohsiung Veterans General Hospital, Grant No.VGHKS 94-082
文摘AIM: To investigate whether illness severity has an impact on gastric residual volume (GRV) in medical critically ill patients. METHODS: Medical intensive care unit (ICU) patients requiring nasogastric feeding were enrolled. Sequential Organ Failure Assessment (SOFA) score was assessed immediately preceding the start of the study. Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ scores were recorded on the first, fourth, seventh, and fourteenth day of the study period. GRV was measured every 4 h during enteral feeding. The relationship be-tween mean daily GRV and SOFA scores and the correlation between mean daily GRV and mean APACHE Ⅱ score of all patients were evaluated and compared. RESULTS: Of the 61 patients, 43 patients were survivors and 18 patients were non-survivors. The mean daily GRV increased as SOFA scores increased (P < 0.001, analysis of variance). Mean APACHE Ⅱ scores of all patients correlated with mean daily GRV (P = 0.011, Pearson correlation) during the study period. Patients with decreasing GRV in the first 2 d had better survival than patients without decreasing GRV (P = 0.017, log rank test). CONCLUSION: GRV is higher in more severely ill medical ICU patients. Patients with decreasing GRV had lower ICU mortality than patients without decreasing GRV.
基金We thank for the funding support from the Key Research and Development Plan of China(No.2017YFC1703306)Youth Project of Natural Science Foundation of Hunan Province(No.2019JJ50453)+2 种基金Project of Hunan Health Commission(No.202112072217)Open Fund Project of Hunan University of Traditional Chinese Medicine(No.2018JK02)General Project of Education Department of Hunan Province(No.19C1318).
文摘Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper.Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors,simplify the diagnosis and treatment process,and improve the quality of diagnosis.Methods Firstly,data enhancement,image resizings,and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34)is constructed to extract the characteristics of psoriasis.Finally,we used the Adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model,and obtained an optimized ResNet-34 model for psoriasis diagnosis.Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate,F1-score and ROC curve.Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis,and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.
