Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study

BackgroundShort-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH.MethodsThis study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP).ResultsA total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results.ConclusionsThis study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH

Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior

Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled k-space measurements

Numerical Simulation of High-Performance CsPbI₃/FAPbI₃ Heterojunction Perovskite Solar Cells

To broaden the absorption spectrum of cells, enhance the cell stability, and avoid high costs, a novel perovskite solar cell (PSC) with the structure of fluorine-doped tin oxide (FTO)/ZnO/CsPbI3/FAPbI3/CuSCN/Au is designed using the solar cell capacitance simulator (SCAPS) software. The simulation results indicate that the CsPbI3/FAPbI3 heterojunction PSC has higher quantum efficiency (QE) characteristics than the single-junction CsPbI3-based PSC, and it outputs a higher short-circuit current density (Jsc) and power conversion efficiency (PCE). In order to optimize the device performance, several critical device parameters, including the thickness and defect density of both the CsPbI3 and FAPbI3 layers, the work function of the contact electrodes, and the operating temperature are systematically investigated. Through the optimum analysis, the thicknesses of CsPbI3 and FAPbI3 are optimized to be 100 and 700 nm, respectively, so that the cell could absorb photons more sufficiently without an excessively high recombination rate, and the cell achieved the highest PCE. The defect densities of CsPbI3 and FAPbI3 are set to 1012 cm−3 to effectively avoid the excessive carrier recombination centering on the cell to increase the carrier lifetime. Additionally, we found that when the work function of the metal back electrode is greater than 4.8 eV and FTO with a work function of 4.4 eV is selected as the front electrode, the excessively high Schottky barrier could be avoided and the collection of photogenerated carriers could be promoted. In addition, the operating temperature is proportional to the carrier recombination rate, and an excessively high temperature could inhibit Voc. After implementing the optimized parameters, the cell performance of the studied solar cell was improved. Its PCE reaches 28.75%, which is higher than most of existing solar cells. Moreover, the open circuit voltage (Voc), Jsc, and PCE are increased by 17%, 9.5%, and 25.1%, respectively. The results of this paper provide a methodology and approach for the construction of high-efficiency heterojunction PSCs

Numerical Simulation of High-Performance CsPbI3/FAPbI3 Heterojunction Perovskite Solar Cells

To broaden the absorption spectrum of cells, enhance the cell stability, and avoid high costs, a novel perovskite solar cell (PSC) with the structure of fluorine-doped tin oxide (FTO)/ZnO/CsPbI3/FAPbI3/CuSCN/Au is designed using the solar cell capacitance simulator (SCAPS) software. The simulation results indicate that the CsPbI3/FAPbI3 heterojunction PSC has higher quantum efficiency (QE) characteristics than the single-junction CsPbI3-based PSC, and it outputs a higher short-circuit current density (Jsc) and power conversion efficiency (PCE). In order to optimize the device performance, several critical device parameters, including the thickness and defect density of both the CsPbI3 and FAPbI3 layers, the work function of the contact electrodes, and the operating temperature are systematically investigated. Through the optimum analysis, the thicknesses of CsPbI3 and FAPbI3 are optimized to be 100 and 700 nm, respectively, so that the cell could absorb photons more sufficiently without an excessively high recombination rate, and the cell achieved the highest PCE. The defect densities of CsPbI3 and FAPbI3 are set to 1012 cm−3 to effectively avoid the excessive carrier recombination centering on the cell to increase the carrier lifetime. Additionally, we found that when the work function of the metal back electrode is greater than 4.8 eV and FTO with a work function of 4.4 eV is selected as the front electrode, the excessively high Schottky barrier could be avoided and the collection of photogenerated carriers could be promoted. In addition, the operating temperature is proportional to the carrier recombination rate, and an excessively high temperature could inhibit Voc. After implementing the optimized parameters, the cell performance of the studied solar cell was improved. Its PCE reaches 28.75%, which is higher than most of existing solar cells. Moreover, the open circuit voltage (Voc), Jsc, and PCE are increased by 17%, 9.5%, and 25.1%, respectively. The results of this paper provide a methodology and approach for the construction of high-efficiency heterojunction PSCs