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Background The thrombolytic effect for ischemic stroke (IS) is affected by complex factors, such as acute onset of stroke, short therapeutic time window, various individual patient factors, treatment model, types and doses of medicines as well as mode of administration. To identify the influencing factors of thrombolytic effect, most existing studies adopt statistical methods, while rare studies use artificial intelligence (AI) -based algorithms.Objective To establish models using AI-based algorithms for IS patients based on the real-world data including general patient characteristics, medication model and recovery effects, to achieve precise individualized thrombolytic treatment and provide data support for clinical prescription decisions.Methods A retrospective design was used. The clinical information of IS patients (n=55 621) was extracted from the Yidu Cloud scientific research big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to December 31, 2021, among whom 1 855 with complete information were enrolled according to the inclusion criteria. Thrombolysis effect was evaluated by comparing the National Institutes of Health Stroke Scale (NIHSS) score measured at admission and discharge, and those with an improvement in the NIHSS score by ≥4 points and

The economical and safe energy management strategy (EMS) is essential for a battery electric vehicle (BEV). With an improved deep deterministic policy gradient (DDPG)-based EMS proposed for a dual-motor BEV, energy consumption and electric component aging are minimized. Firstly, this study incorporates health awareness of electric components into EMS. Then, this study employs a gated recurrent unit (GRU) as well as a self-attention (SA) mechanism for improving the original DDPG's performance. The GRU enhances the performance of DDPG agents, post-convergence, by furnishing them with additional state information. The SA mechanism empowers agents to distinguish the importance of state information, thus improving its adaptability in new environments. The proposed EMS decreases battery energy consumption to 7.95 kW-hour and enhances battery state of health (SOH) to 96.83 % of the dynamic programming (DP)-based EMS. The two motors of the proposed EMS possess the highest end-value SOH, i.e., 99.99929 % and 99.99978 %, respectively. The proposed EMS demonstrates superior adaptability to other EMSs, regardless of the initial state of charge (SOC). It shows the best adaptability at an initial SOC of 0.6, and achieves 95.87 % and 94.67 % of the energy consumption of DP-based EMS in the mixed and actual test cycles, respectively. • The health awareness of multiple electric components is embedded in EMS. • The GRU is employed to improve the performance of the EMS after convergence. • The Self-attention mechanism is adopted to improve the adaptability of EMS. • The proposed EMS outperforms the other EMSs in both training and testing cycles. [ABSTRACT FROM AUTHOR]

Objective: To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their families, and provide data support for clinical decision-making., Methods: A retrospective study was conducted. The clinical information of stroke patients (n = 53 793) were extracted from the Yidu cloud big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to July 31, 2023. Combined with the results of single factor screening and the opinions of experts with senior professional titles in neurology, the input variable was determined, and the output variable was the National Institutes of Health Stroke Scale (NIHSS) representing the severity of the disease at admission. Python 3.7 was used to build DeepFM algorithm model, and five data mining models including Logistic regression, CART decision tree, C5.0 decision tree, Bayesian network and deep neural network (DNN) were built at the same time. The original data were randomly divided into 80% training set and 20% test set, which were used to train and test the models, adjust the parameters of each model, respectively calculate the accuracy, sensitivity and F-index of the six models, carry out the comprehensive comparison and evaluation of the model. The receiver operator characteristic curve (ROC curve) and calibration curve were drawn, compared the prediction performance of DeepFM model and the other five algorithms. In addition, the data of stroke patients (n = 1 028) were extracted from Dalian Central Hospital for external verification of the model., Results: A total of 14 015 stroke patients with complete information were selected, including 11 212 in the training set and 2 803 in the testing set. After univariate screening, 14 indicators were included to construct the model, including gender, age, recurrence, physical impairment, facial problems, speech disorders, head reactions, disturbance of consciousness, visual disorders, abnormal cough and swallowing, high risk factor, family history, smoking history and drinking history. DeepFM model adopted the two-order crossover feature. The number of hidden layers in DNN layer was 3. Dropout was used to discard the neurons in the neural network. Rule was used as the activation function. Each layer used Dense full connection. The objective function was random gradient descent. The number of iterations was 15. There were 133 922 training parameters in total. Comparing the predictive value of the six models showed that the accuracy of DeepFM model was 0.951, the sensitivity was 0.992, the specificity was 0.814, the F-index was 0.950, and the area under the curve (AUC) was 0.916. The accuracy of the other five data mining models was between 0.771-0.780, the sensitivity was between 0.978-0.987, the F-index was between 0.690-0.707, and the AUC was between 0.568-0.639. The calibration curve of the DeepFM model was more aligned with the ideal curve than the other five data mining models. Suggesting that the prediction performance of DeepFM model was the best. External validation was conducted on the DeepFM model, and its accuracy was 0.891, indicating good generalization performance of the model., Conclusions: The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.