ABSTRACT
The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.
Subject(s)
Hypotension , Humans , Reproducibility of Results , Hypotension/etiology , Renal Dialysis/adverse effects , Renal Dialysis/methodsABSTRACT
Importance: This is the first study to investigate the correlation between intra-operative hemodynamic changes and postoperative physiological status. Design settings and participants: Patients receiving laparoscopic hepatectomy were routinely monitored using FloTract for goal-directed fluid management. The Pringle maneuver was routinely performed during parenchymal dissection and the hemodynamic changes were prospectively recorded. We retrospectively analyzed the continuous hemodynamic data from FloTrac to compare with postoperative physiological outcomes. Exposure: The Pringle maneuver during laparoscopic hepatectomy. Results: Stroke volume variation that did not recover from the relief of the Pringle maneuver during the last application of Pringle maneuver predicted elevated postoperative MELD-Na scores. Conclusions and relevance: The complexity of the hemodynamic data recorded by the FloTrac system during the Pringle Maneuver in laparoscopic hepatectomy can be effectively analyzed using the growth mixture modeling (GMM) method. The results can potentially predict the risk of short-term liver function deterioration.
ABSTRACT
Booming progress has been made in both the molecular design concept and the fundamental electroluminescence (EL) mechanism of thermally activated delayed fluorescence (TADF)-based organic light-emitting diodes (OLEDs) in recent years. One of the requirements for TADF-based OLEDs having high external quantum efficiency (EQE) is the favorable energy level alignment between the host and the guest to promote the energy transfer and improve the carrier balance. However, strategies to optimize the TADF-based OLED performance by selecting suitable host-guest systems in the light-emitting layer are far from enough. In this work, we investigated guest-host systems through the use of two machine-learning approaches (feature-based and similarity-based algorithms) from our recent effort for the optimization of TADF-based OLEDs. The Random Forest (RF) algorithm based on the features of electronic and photo-physical properties can accurately predict the EQE of green TADF-based OLEDs with average correlation coefficients of R2 = 0.85 for the training set and R2 = 0.74 for the testing set. Also, the Support Vector Regression (SVR) algorithm based on similarity metrics between pairs of materials (e.g., host and guest) in terms of electronic parameters can provide reasonable device performance prediction (R2 = 0.72) through the optimization procedure of the parameters. These results show that the predictive capability and model applicability of both machine-learning models can be used to identify suitable host-guest systems and explore complex relationships in green TADF-based OLEDs.
ABSTRACT
The presence of a solution-processed hybrid PEDOT: PSS-MoO3-based hole injection layer (HIL) promotes a good interfacial contact between the indium tin oxide anode and hole-transporting layer for efficient operation of organic light-emitting diodes (OLEDs). This work reveals that the use of the hybrid HIL benefits the performance of phosphorescent OLEDs in two ways: (1) to assist in efficient hole injection, thereby improving power efficiency of OLEDs, and (2) to improve electron-hole current balance and suppression of interfacial defects at the organic/anode interface. The combined effects result in the power efficiency of 89.2 lm/W and external quantum efficiency of 23.9% for phosphorescent green OLEDs. The solution-processed hybrid PEDOT: PSS-MoO3-based HIL is beneficial for application in solution-processed organic electronic devices.
