CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning by Shaofu Lin, Shiwei Zhou, Han Jiao, Mengzhen Wang, Haokang Yan, Peng Dou, Jianhui Chen

“…In order to solve the problem of data imbalance, a dual experience replay strategy is realized to help the model select representative data samples and accelerate model convergence on the imbalanced EHR data. …”
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Predicting Screening Efficiency of Probability Screens Using KPCA-GRNN with WP-EE Feature Reconstruction by Qingtang Chen, Yijian Huang

“…Following the extraction of two core principal components, model parameters when KPCA’s σ2 = 0.85, the optimal parameter of GRNN model Spread = 0.051, and the optimal number of training samples N = 19, the average prediction error is 1.434%, the minimum prediction error reaching 0.708%, the minimum root mean square error reaching 0.836% and Pearson correlation coefficient marking the closest to 1, these result all representing the optimum achievable values. The budget model selects the optimal parameter combination scheme for the system.…”
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Effectiveness of pre-employment card policy on employment transition during covid-19: evidence from Indonesian dual labor market by Beni Teguh Gunawan, El Bram Apriyanto, Hennigusnia Hennigusnia, Ivan Lilin Suryono, Ardhian Kurniawati

“…To analyze employment transitions, this research employs a multinomial logit model, selected for its ability to estimate the probability of multiple, categorical employment outcomes, making it especially suitable for evaluating the diverse pathways individuals might take from unemployment to formal or informal employment, and from informal to formal sectors. …”
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Generalization of stochastic mortality models to improve mortality prediction in life insurance and pension funds by Shirin Shoaee, Mohammad Mehdi Gholi Keshmarzi

“…The purpose of this study is to generalize static stochastic mortality models to dynamic stochastic mortality models and to predict mortality rates based on the generalization of stochastic mortality models by the Cox-Ingersoll-Ross (CIR) process and to compare the results with each other.Methodology: In this research, two suggestions are presented: the first idea is to provide a dynamic correction method to increase the prediction accuracy using the CIR process and the second idea is to examine the out-of-sample validation method.Findings: In this study, using the out-of-sample validation method, the force of mortality from the best models selected from the two famous mortality model families (Lee-Carter and Cairns, Blake and Dowd (CBD)) is compared with the results of the generalized model. …”
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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Results Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. The model-selected features included gallbladder wall thickness, polyp size, polyp echo, and pedicle. …”
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A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system. by Ellen T Heyman, Awais Ashfaq, Ulf Ekelund, Mattias Ohlsson, Jonas Björk, Ardavan M Khoshnood, Markus Lingman

“…For each unique patient visit, the model selected the most important variables, analysed them and presented them to the clinician interpretably by taking event time and clinical context into account. …”
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In silico analysis of the effect of HCV genotype-specific polymorphisms in Core, NS3, NS5A, and NS5B proteins on T-cell epitope processing and presentation by Samina Baig, Samina Baig, Assel Berikkara, Ramsha Khalid, Syed A. Subhan, Tanveer Abbas, Syed Hani Abidi

“…PEP-FOLD was used to model selected epitopes, followed by peptide-HLA docking using HPEPDOCK. …”
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Treatment Planning Strategies for Interstitial Ultrasound Ablation of Prostate Cancer by Pragya Gupta, Tamas Heffter, Muhammad Zubair, I-Chow Hsu, E. Clif Burdette, Chris J. Diederich

“…Results: For generic prostate tissue, 360 treatment schemes were simulated based on the number of transducers (1-4), applied power (8-20 W/cm2), heating time (5, 7.5, 10 min), and blood perfusion (0, 2.5, 5 kg/m3/s) using forward treatment modelling. Selectable ablation zones ranged from 0.8-3.0 cm and 0.8-5.3 cm in radial and axial directions, respectively. 3D patient-specific thermal treatment modeling for 12 Cases of T2/T3 prostate disease demonstrate applicability of workflow and technique for focal, quadrant and hemi-gland ablation. …”
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