Research on health care integration policy evaluation based on grounded theory and PMC index model

Health care integration is an important way to achieve healthy ageing and meet the personalized health care needs of the elderly. Based on 16 policies on health care integration introduced at the national level in China from 2013 to 2022, the open coding process of grounded theory was used to set up variable indicators and construct a PMC index model for quantitative evaluation of the policies. The results of the study show that the mean value of the PMC index of 16 policies is 7.42, including 12 excellent policies and 4 good policies. Through the PMC curve and depression index, it is found that the overall quality of the combined medical care and support policy in China is good, but it still needs further optimization and improvement in terms of policy timeliness, policy tools, policy operability and policy innovation

A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics

Autonomous vehicles and driving technologies have received notable attention in the past decades. In autonomous driving systems, \textcolor{black}{the} information of vehicle dynamics is required in most cases for designing of motion planning and control algorithms. However, it is nontrivial for identifying a global model of vehicle dynamics due to the existence of strong non-linearity and uncertainty. Many efforts have resorted to machine learning techniques for building data-driven models, but it may suffer from interpretability and result in a complex nonlinear representation. In this paper, we propose a deep learning framework relying on an interpretable Koopman operator to build a data-driven predictor of the vehicle dynamics. The main idea is to use the Koopman operator for representing the nonlinear dynamics in a linear lifted feature space. The approach results in a global model that integrates the dynamics in both longitudinal and lateral directions. As the core contribution, we propose a deep learning-based extended dynamic mode decomposition (Deep EDMD) algorithm to learn a finite approximation of the Koopman operator. Different from other machine learning-based approaches, deep neural networks play the role of learning feature representations for EDMD in the framework of the Koopman operator. Simulation results in a high-fidelity CarSim environment are reported, which show the capability of the Deep EDMD approach in multi-step prediction of vehicle dynamics at a wide operating range. Also, the proposed approach outperforms the EDMD method, the multi-layer perception (MLP) method, and the Extreme Learning Machines-based EDMD (ELM-EDMD) method in terms of modeling performance. Finally, we design a linear MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm

Learning-based Predictive Control for Nonlinear Systems with Unknown Dynamics Subject to Safety Constraints

Model predictive control (MPC) has been widely employed as an effective method for model-based constrained control. For systems with unknown dynamics, reinforcement learning (RL) and adaptive dynamic programming (ADP) have received notable attention to solve the adaptive optimal control problems. Recently, works on the use of RL in the framework of MPC have emerged, which can enhance the ability of MPC for data-driven control. However, the safety under state constraints and the closed-loop robustness are difficult to be verified due to approximation errors of RL with function approximation structures. Aiming at the above problem, we propose a data-driven robust MPC solution based on incremental RL, called data-driven robust learning-based predictive control (dr-LPC), for perturbed unknown nonlinear systems subject to safety constraints. A data-driven robust MPC (dr-MPC) is firstly formulated with a learned predictor. The incremental Dual Heuristic Programming (DHP) algorithm using an actor-critic architecture is then utilized to solve the online optimization problem of dr-MPC. In each prediction horizon, the actor and critic learn time-varying laws for approximating the optimal control policy and costate respectively, which is different from classical MPCs. The state and control constraints are enforced in the learning process via building a Hamilton-Jacobi-Bellman (HJB) equation and a regularized actor-critic learning structure using logarithmic barrier functions. The closed-loop robustness and safety of the dr-LPC are proven under function approximation errors. Simulation results on two control examples have been reported, which show that the dr-LPC can outperform the DHP and dr-MPC in terms of state regulation, and its average computational time is much smaller than that with the dr-MPC in both examples.Comment: The paper has been submitted at a IEEE Journal for possible publicatio

Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation

The emergence of multi-modal deep learning models has made significant impacts on clinical applications in the last decade. However, the majority of models are limited to single-tasking, without considering disease diagnosis is indeed a multi-task procedure. Here, we demonstrate a unified transformer model specifically designed for multi-modal clinical tasks by incorporating customized instruction tuning. We first compose a multi-task training dataset comprising 13.4 million instruction and ground-truth pairs (with approximately one million radiographs) for the customized tuning, involving both image- and pixel-level tasks. Thus, we can unify the various vision-intensive tasks in a single training framework with homogeneous model inputs and outputs to increase clinical interpretability in one reading. Finally, we demonstrate the overall superior performance of our model compared to prior arts on various chest X-ray benchmarks across multi-tasks in both direct inference and finetuning settings. Three radiologists further evaluate the generated reports against the recorded ones, which also exhibit the enhanced explainability of our multi-task model

Causal relationships between COVID-19 and osteoporosis: a two-sample Mendelian randomization study in European population

IntroductionThe causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study.MethodsWe conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR–Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran’s Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method.ResultsThe primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses.ConclusionThe results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent

Case report: Treatment of Wilson’s disease by human amniotic fluid administration

BackgroundWilson’s disease (WD) is not an uncommon genetic disease in clinical practice. However, the current WD therapies have limitations. The effectiveness of stem cell therapy in treating WD has yet to be verified, although a few animal studies have shown that stem cell transplantation could partially correct the abnormal metabolic phenotype of WD. In this case report, we present the therapeutic effect of human amniotic fluid containing stem cells in one WD patient.Case presentationA 22-year-old Chinese woman was diagnosed with WD 1 year ago in 2019. The available drugs were not effective in managing the progressive neuropsychiatric symptoms. We treated the patient with pre-cultured human amniotic fluid containing stem cells. Amniotic fluid was collected from pregnant women who underwent induced labor at a gestational age of 19–26 weeks, and then, the fluid was cultured for 2 h to allow stem cell expansion. Cultured amniotic fluid that contained amniotic fluid derived stem cells (AFSC) in the range of approximately 2.8–5.5 × 104/ml was administrated by IV infusion at a rate of 50–70 drops per minute after filtration with a 300-mu nylon mesh. Before the infusion of amniotic fluid, low-molecular-weight heparin and dexamethasone were successively administrated. The patient received a total of 12 applications of amniotic fluid from different pregnant women, and the treatment interval depended on the availability of amniotic fluid. The neuropsychiatric symptoms gradually improved after the stem cell treatment. Dystonia, which included tremor, chorea, dysphagia, dysarthria, and drooling, almost disappeared after 1.5 years of follow-up. The Unified Wilson’s Disease Rating Scale score of the patient decreased from 72 to 10. Brain magnetic resonance imaging (MRI) showed a reduction in the lesion area and alleviation of damage in the central nervous system, along with a partial recovery of the lesion to the normal condition. The serum ceruloplasmin level was elevated from undetectable to 30.8 mg/L, and the 24-h urinary copper excretion decreased from 171 to 37 μg. In addition, amniotic fluid transplantation also alleviates hematopoietic disorders. There were no adverse reactions during or after amniotic fluid administration.ConclusionAmniotic fluid administration, through which stem cells were infused, significantly improves the clinical outcomes in the WD patient, and the finding may provide a novel approach for managing WD effectively

Nonlinear Magneto-Electro-Mechanical Response of Physical Cross-Linked Magneto-Electric Polymer Gel

This work reports on a novel magnetorheological polymer gel with carbon nanotubes and carbonyl iron particles mixed into the physical cross-linked polymer gel matrix. The resulting composites show unusual nonlinear magneto-electro-mechanical responses. Because of the low matrix viscosity, effective conductive paths formed by the CNTs were mobile and high-performance sensing characteristics were observed. In particular, due to the transient and mutable physical cross-linked bonds in the polymer gel, the electromechanical behavior acted in a rate-dependent manner. External stimulus at a high rate significantly enhanced the electrical resistance response during mechanical deformation. Meanwhile, the rheological properties were regulated by the external magnetic field when magnetic particles were added. This dual enhancement mechanism further contributes to the active control of electromechanical performance. These polymer composites could be adopted as electromechanical sensitive sensors to measure impact and vibration under different frequencies. There is great potential for this magnetorheological polymer gel in the application of intelligent vibration controls