Cardiovascular diseases (CVDs), including heart failure (HF), are the leading cause of mortality worldwide, with an increasing incidence. Chronic comorbidities often accompany CVDs, such as hypertension, diabetes, and renal disease (RD). We propose an application that could assist with the real-time risk detection of renal dysfunction in hospitalized patients with pre-existing heart failure by developing a recurrent neural network (RNN)-based estimated glomerular filtration rate (eGFR) prediction (PREP) model using sequential deep learning (DL) and machine learning (ML) algorithms. We developed a DL-based sequentially predictive model to improve the early identification of RD in patients with HF through creatinine (Cr), the most frequently performed blood test. Additionally, we employed an ensemble model to compress model and improve its performance by reducing weights. We evaluated our model with several metrics including mean absolute error and accuracy. Finally, we proposed the implementation of the Cr-based PREP in web-browser-based applications. The four modules constituting the PREP received the same 24-hour data as input and developed predictions after 12, 24, 36, and 48 hours. PREP accomplished 5.61 of all features to 3.21 of 10 features for MAE of module 1 in the case of regression (eGFR, unit: mL /min/ 1.73 m^2 ) and 0.82 to 0.89 of Acc for the same module classification. We proposed an ML-based sequentially predictive model and application which are lightweight and practical in assisting with the real-time risk early identification of RD and CVDs during hospitalization. Our findings could be integrated into the information system of hospitals and institutions to improve the management of CVDs. Retrospectively registered. Not applicable.

This paper presents a novel model-free finite-time sliding mode control strategy for robotic manipulators that ensures accurate tracking, rapid error convergence, and smooth control signals while effectively handling system uncertainties and external disturbances. Unlike conventional control techniques requiring detailed dynamic models, the proposed method employs time-delay estimation (TDE) to approximate system dynamics in real-time, enabling model-independent implementation. A new nonsingular sliding surface and finite-time reaching law are introduced to enhance stability, convergence speed, and chattering mitigation. The controller’s performance is validated through simulations on a 3-DOF SAMSUNG FARA AT2 robot arm, demonstrating superior tracking accuracy, robustness, and control smoothness compared to Computed Torque Control (CTC), Sliding Mode Control (SMC), and Nonsingular Terminal SMC (NTSMC).

This paper presents a predefined-time control approach to address slow convergence and instability in the orbit control of remotely operated vehicles (ROVs). The proposed method introduces tunable predefined-time stability (PTS), allowing precise adjustment of the system’s stability time through configurable parameters, thereby enhancing controller adaptability. A control input system ensures PTS is developed, while a fuzzy logic system (FLS) is employed to estimate unstructured uncertainties and disturbances. This integration improves robustness, reduces chattering, and eliminates singularities, making the approach well suited for systems with incomplete or unknown model data. Comprehensive simulations validate the effectiveness of the proposed method, demonstrating superior performance compared to existing control strategies and highlighting its potential for advanced ROV applications.

Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins. The predictive performance of three ML models, including Extreme Gradient Boosting (XGBoost), Random Forest, and Logistic Regression, was evaluated using electronic medical records from the Asan Medical Center in Seoul across six performance metrics. Additionally, all three models achieved an average AUROC of 0.695 despite reducing features by over 43%. SHAP analysis was conducted to identify key features influencing model predictions, aiming insights into patient characteristics associated with achieving LDL-C targets. This study suggests that ML-based approaches may help identify patients likely to benefit from moderate-dose statins, potentially supporting personalized treatment strategies and clinical decision-making for LDL-C management.

This paper presents an approach to predefined-time terminal sliding mode control (PT-TSMC) for Euler-Lagrange systems (ELSs) with actuator control input saturation, leveraging fuzzy compensation techniques. Initially, the paper focuses on tunable predefined-time stability (PTS), providing adjustable parameters to finetune the system's stability time, thereby enhancing the adaptability of the controller design. Based on this approach, a control input system is formulated to ensure predefined-time stability for Euler-Lagrange systems. Additionally, a strategy is proposed to enhance system performance, including robustness improvement, chattering reduction, and singularity elimination, through the design of an adaptive fuzzy logic system (AFLS). The AFLS estimates unstructured model uncertainties and compounded disturbances, seamlessly integrating them into the control system. Notably, this approach effectively addresses the challenge of handling unknown model data. Finally, comprehensive comparative simulations demonstrate the effectiveness of the proposed method, showcasing its commendable control performance.

This paper introduces a novel model-free nonsingular fixed-time sliding mode control (MF-NFxTSMC) strategy for precise trajectory tracking in robot arm systems. Unlike conventional sliding mode control (SMC) approaches that require accurate dynamic models, the proposed method leverages the time delay estimation (TDE) approach to effectively estimate system dynamics and external disturbances in real-time, enabling a fully model-free control solution. This significantly enhances its practicality in real-world scenarios where obtaining precise models is challenging or infeasible. A significant innovation of this work lies in designing a novel fixed-time control framework that achieves faster convergence than traditional fixed-time methods. Building on this, a novel MF-NFxTSMC law is developed, featuring a novel singularity-free fixed-time sliding surface (SF-FxTSS) and a novel fixed-time reaching law (FxTRL). The proposed SF-FxTSS incorporates a dynamic proportional term and an adaptive exponent, ensuring rapid convergence and robust tracking. Notably, its smooth transition between nonlinear and linear dynamics eliminates the singularities often encountered in terminal and fixed-time sliding mode surfaces. Additionally, the designed FxTRL effectively suppresses chattering while guaranteeing fixed-time convergence, leading to smoother control actions and reduced mechanical stress on the robotic hardware. The fixed-time stability of the proposed method is rigorously proven using the Lyapunov theory. Numerical simulations on the SAMSUNG FARA AT2 robotic platform demonstrate the superior performance of the proposed method in terms of tracking accuracy, convergence speed, and control smoothness compared to existing strategies, including conventional SMC, finite-time SMC, approximate fixed-time SMC, and global fixed-time nonsingular terminal SMC (NTSMC). Overall, this approach offers compelling advantages , i.e., model-free implementation, fixed-time convergence, singularity avoidance, and reduced chattering, making it a practical and scalable solution for high-performance control in uncertain robotic systems.

BACKGROUND:The analysis of cardiovascular borders (CVBs) in chest x-rays (CXRs) traditionally relied on subjective assessment and does not have established normal ranges. OBJECTIVES:The authors aimed to develop a deep learning-based method for quantifying CVBs on CXRs and to explore its clinical utility. METHODS:This study used a prevalidated deep learning to analyze CVBs. A total of 96,129 normal CXRs from 4 sites were used to establish age- and sex-specific normal ranges of CVBs. The quantified CVBs were standardized into z-scores for newly inputted CXRs. The clinical utility of the z-score analysis was tested using 44,567 diseased CXRs from 3 sites (9,964 valve disease; 32,900 coronary artery disease; 1,299 congenital heart disease; 294 aortic aneurysm; 110 mediastinal mass). RESULTS:For distinguishing valve disease from normal controls, the area under the receiver operating characteristic curve for the cardiothoracic ratio was 0.80 (95% CI: 0.80-0.80), while the combination of right atrium and left ventricle borders had an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83-0.83). Between mitral and aortic stenosis, z-scores of CVBs were significantly different in the left atrial appendage (1.54 vs 0.33, P < 0.001), carinal angle (1.10 vs 0.67, P < 0.001), and ascending aorta (0.63 vs 1.02, P < 0.001), reflecting disease pathophysiology. Cardiothoracic ratio was independently associated with a 5-year risk of death or myocardial infarction in the coronary artery disease (z-score ≥2, adjusted HR: 3.73 [95% CI: 2.09-6.64], reference z-score <-1). CONCLUSIONS:Deep learning-derived z-score analysis of CXR showed potential in classifying and stratifying the risk of cardiovascular abnormalities.

This paper introduces a novel prescribed performance model-free controller tailored for industrial robot arms, seamlessly integrating adaptive sliding mode control (ASMC) and time-delay estimation (TDE). Leveraging TDE, our controller adeptly estimates both the inherent dynamics of the robot and unstructured uncertainties such as disturbances and parameter variations. However, TDE, which relies on past angular acceleration and input torque, inevitably introduces errors. To mitigate these, our approach compensates for current TDE errors using past error information. Additionally, we introduce a fixed-time sliding mode surface from prescribed performance control and an auxiliary system to improve performance under input saturation. Moreover, we propose an adaptive law to ensure the positivity of the adaptive parameter by considering the current adaptive parameter value and the sampling period. Through extensive simulated studies conducted on industrial robot arms, we demonstrate the effectiveness of our control approach, showcasing robustness, reduced chattering, and high accuracy across diverse scenarios.

Manipulator systems are increasingly deployed across various industries to perform complex, repetitive, and hazardous tasks, necessitating high-precision control for optimal performance. However, the design of effective control algorithms is challenged by nonlinearities, uncertain dynamics, disturbances, and varying real-world conditions. To address these issues, this paper proposes an advanced orbit-tracking control approach for manipulators, leveraging advancements in Time-Delay Estimation (TDE) and Fixed-Time Sliding Mode Control techniques. The TDE approximates the robot’s unknown dynamics and uncertainties, while a novel nonsingular fast terminal sliding mode (NFTSM) surface and novel fixed-time reaching control law (FTRCL) are introduced to ensure faster convergence within a fixed time and improved accuracy without a singularity issue. Additionally, an innovative auxiliary system is designed to address input saturation effects, ensuring that system states converge to zero within a fixed time even when saturation occurs. The Lyapunov-based theory is employed to prove the fixed-time convergence of the overall system. The effectiveness of the proposed controller is validated through simulations on a 3-DOF SAMSUNG FARA AT2 robot manipulator. Comparative analyses against NTSMC, NFTSMC, and GNTSMC methods demonstrate superior performance, characterized by faster convergence, reduced chattering, higher tracking accuracy, and a model-free design. These results underscore the potential of the proposed control strategy to significantly enhance the robustness, precision, and applicability of robotic systems in industrial environments.

The integration of electronic medical records (EMRs) with artificial intelligence (AI) is enhancing medical research, particularly in real-world evidence (RWE) studies. Extracting insights from coded medical data, such as ICD-10 codes, is essential for patient characterization. Traditional techniques, such as one-hot encoding (OHE), face limitations, particularly in managing high-dimensional data. In this study, a Bidirectional Encoder Representations from Transformers (BERT) approach is introduced to encode ICD-10 diagnostic codes, significantly improving model performance and reducing dimensionality. Data from 495,269 patients who visited the Cardiology Department at Asan Medical Center between 2000 and 2020 were used. The performance of models trained with OHE and ClinicalBERT embeddings was compared. For predicting major adverse cardiovascular events within one year following percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), the ClinicalBERT (code-embedded) model outperformed OHE. It achieved an AUC of 0.746 compared to 0.719, while also significantly reducing the dimensionality from 2,492 to 128. This method, which integrates diagnostic and medication data, provides valuable insights into patient care, enhancing the precision of predictions and supporting healthcare professionals in making more informed decisions.

This paper presents a novel adaptive fixed-time sliding mode control (AFxTSMC) framework for industrial manipulators. The proposed adaptive reaching law (ARL) enables rapid and stable gain reduction by leveraging the current parameter values to maintain positivity and prevent sign reversals, thereby reducing chattering. Additionally, the ARL guarantees fixed-time convergence. A singularity-free fixed-time sliding function (SF-FxTSF) ensures fast, robust, and singularity-free convergence. To enhance robustness, a modified third-order sliding mode observer (TOSMO) is integrated into the control framework. This observer estimates both internal uncertainties and external disturbances with improved estimation speed, enabling effective compensation while maintaining convergence performance. A Lyapunov-based analysis rigorously confirms the stability of the proposed method. Simulations of the SAMSUNG FARA AT2 manipulator indicate superior tracking accuracy, faster convergence, and smoother control performance compared to the three state-of-the-art methods. These results underscore the proposed method’s advantages as a robust, scalable, and high-performance control solution for industrial robotic systems.

Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs. Weight measurements were used as the target to estimate fluid loss during diuretic therapy. We designed the TSFD-LSTM, a bi-directional LSTM model with an attention mechanism, to forecast weight change 48 h after heart failure patients were injected with loop diuretics. The model utilized 65 variables, including disease conditions, concurrent medications, laboratory results, vital signs, and physical measurements from EMRs. The framework processed four sequences simultaneously as inputs. An ablation study on attention mechanisms and a comparison with the transformer model as a baseline were conducted. The TSFD-LSTM outperformed the other models, achieving 85% predictive accuracy with MAE and MSE values of 0.56 and 1.45, respectively. Thus, the TSFD-LSTM model can aid in personalized loop diuretic treatment and prevent adverse drug events, contributing to improved healthcare efficacy for heart failure patients.

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

This paper introduces a new control strategy for robot manipulators, specifically designed to tackle the challenges associated with traditional model-based sliding mode (SM) controller design. These challenges include the need for accurately computed system models, knowledge of disturbance upper bounds, fixed-time convergence, prescribed performance, and the generation of chattering. To overcome these obstacles, we propose the incorporation of a neural network (NN) that effectively addresses these issues by removing the constraint of a precise system model. Additionally, we introduce a novel fixed-time prescribed performance control (PPC) to enhance response performance and position-tracking accuracy, while effectively limiting overshoot and maintaining steady-state error within the predefined range. To expedite the convergence of the SM surface to its equilibrium point, we introduce a faster terminal sliding mode (TSM) surface and a novel fixed-time reaching control algorithm (RCA) with adaptable factors. By integrating these approaches, we develop a novel control strategy that successfully achieves the desired goals for robot manipulators. The effectiveness and stability of the proposed approach are validated through extensive simulations on a 3-DOF SAMSUNG FARA-AT2 robot manipulator, utilizing both Lyapunov criteria and performance evaluations. The results demonstrate improved convergence rate and tracking accuracy, reduced chattering, and enhanced controller robustness.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

This paper proposes a novel control strategy for uncertain magnetic levitation (UML) systems. Our method is fundamentally based on terminal sliding mode (TSM) and prescribed performance (PP) control theory. In contrast to conventional TSM and PP methods, which heavily rely on a precise model of the dynamics, our approach breaks free from the constraint of requiring such precision. It employs a neural network (NN) to approximate unknown functions and the components that may cause singularities in the control input. Additionally, the dual PP functions designed in this novel approach not only effectively manage maximum overshoot but also ensure symmetric steady-state tracking error (SSTE) boundaries, rapidly minimizing errors to zero within a fixed time. Moreover, this approach reduces chattering. On the other hand, to mitigate the adverse impact of input saturation, a fixed-time auxiliary system (FTAS) is introduced into the control design. As a result of these proposals, the approach enables the attainment of multiple desired performance indicators within a predetermined domain and a fixed time, particularly under conditions of uncertainties and input saturation. These indicators include convergence, SSTE, and maximum overshoot. The stability of the proposed scheme has been rigorously established by applying the stable principles of Lyapunov theory and fixed-time control (FTC) theory. Experiments serve to validate the exceptional performance of the proposed strategy under a range of diverse operating conditions.

This paper addresses some of the key challenges associated with sliding mode control (SMC)-based trajectory tracking for robotic manipulators. The primary objective is to enhance the accuracy of the follow-up while minimizing chattering behavior and ensuring robustness against uncertainty components and disturbances. Furthermore, this paper aims to eliminate the reliance on complete or partial knowledge of system dynamics, especially in the context of model-based SMC methods. Finally, the paper focuses on achieving fixed-time global convergence rates for the system. To address these issues, we first design a fixed-time terminal sliding mode control (TSMC) method and analyze its limitations. We then propose a model-free fixed-time TSMC strategy to overcome these limitations, removing the necessity for a dynamic model of the robotic system, thus enhancing practical implementation. The proposed approach ensures the tracking error converges to zero within a fixed time, and its fixed-time global stability is rigorously demonstrated using Lyapunov theory. Extensive simulations on the SAMSUNG FARA AT2 robotic arm were conducted to validate the proposed strategy. The results showed improved tracking performance and a significant reduction in chattering in the control input.

Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection by the calculated importance using Xgboost. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group with high scores.

Background and Objective:Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods:We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results:The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions:Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.