Machine learning models for prediction of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus: a singled centered retrospective study.

OBJECTIVE: This study aimed to develop and validate a machine learning algorithm-based model for predicting invasive Klebsiella pneumoniae liver abscess syndrome(IKPLAS) in diabetes mellitus and compare the performance of different models. METHODS: The clinical signs and data on the admission of 213 diabetic patients with Klebsiella pneumoniae liver abscesses were collected as variables. The optimal feature variables were screened out, and then Artificial Neural Network, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor, Decision Tree, and XGBoost models were established. Finally, the model's prediction performance was evaluated by the ROC curve, sensitivity (recall), specificity, accuracy, precision, F1-score, Average Precision, calibration curve, and DCA curve. RESULTS: Four features of hemoglobin, platelet, D-dimer, and SOFA score were screened by the recursive elimination method, and seven prediction models were established based on these variables. The AUC (0.969), F1-Score(0.737), Sensitivity(0.875) and AP(0.890) of the SVM model were the highest among the seven models. The KNN model showed the highest specificity (1.000). Except that the XGB and DT models over-estimates the occurrence of IKPLAS risk, the other models' calibration curves are a good fit with the actual observed results. Decision Curve Analysis showed that when the risk threshold was between 0.4 and 0.8, the net rate of intervention of the SVM model was significantly higher than that of other models. In the feature importance ranking, the SOFA score impacted the model significantly. CONCLUSION: An effective prediction model of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus could be established by a machine learning algorithm, which had potential application value.

Tbps wide-field parallel optical wireless communications based on a metasurface beam splitter.

Optical wireless communication (OWC) stands out as one of the most promising technologies in the sixth-generation (6G) mobile networks. The establishment of high-quality optical links between transmitters and receivers plays a crucial role in OWC performances. Here, by a compact beam splitter composed of a metasurface and a fiber array, we proposed a wide-angle (~120°) OWC optical link scheme that can parallelly support up to 144 communication users. Utilizing high-speed optical module sources and wavelength division multiplexing technique, we demonstrated each user can achieve a communication speed of 200 Gbps which enables the entire system to support ultra-high communication capacity exceeding 28 Tbps. Furthermore, utilizing the metasurface polarization multiplexing, we implemented a full range wide-angle OWC without blind area nor crosstalk among users. Our OWC scheme simultaneously possesses the advantages of high-speed, wide communication area and multi-user parallel communications, paving the way for revolutionary high-performance OWC in the future.

Evaluation of manual and electronic healthcare-associated infections surveillance: a multi-center study with 21 tertiary general hospitals in China.

BACKGROUND: Healthcare-associated infections (HAIs) are still a major health threats worldwide. Traditional surveillance methods involving manual surveillance by infection control practitioners (ICPs) for data collection processes are laborious, inefficient, and generate data of variable quality. In this study, we sought to evaluate the impact of surveillance and interaction platform system (SIPS) for HAIs surveillance compared to manual survey in tertiary general hospitals. METHODS: A large multi-center study including 21 tertiary general hospitals and 63 wards were performed to evaluate the impact of electronic SIPS for HAIs. RESULTS: We collected 4,098 consecutive patients and found that the hospitals installed with SIPS significantly increased work efficiency of ICPs achieving satisfactory diagnostic performance of HAIs with 0.73 for sensitivity, 0.81 for specificity and 0.81 area under the curve (AUC). However, there were significant heterogeneity own to regions, time of SIPS installation, departments and sample size. CONCLUSIONS: SIPS significantly improved ICPs efficiency and HAIs monitoring effectiveness, but there were shortcomings such as untimely maintenance and high cost.