Machine Learning-Aided Decision-Making Model for the Discontinuation of Continuous Renal Replacement Therapy.

Introduction: Continuous renal replacement therapy (CRRT) is a primary form of renal support for patients with acute kidney injury in an intensive care unit. Making an accurate decision of discontinuation is crucial for the prognosis of patients. Previous research has mostly focused on the univariate and multivariate analysis of factors in CRRT, without the capacity to capture the complexity of the decision-making process.

A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.

Objective: To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms.

Methods: This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022.

In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

Objective: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo.

Methods: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy.

Multi-organ spatiotemporal information aware model for sepsis mortality prediction.

Background: Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems.

A dynamic spatiotemporal model for fall warning and protection.

Early detection of falls is important for reducing fall injuries. However, existing fall detection strategies mostly focus on reducing impact injuries rather than avoiding falls. This study proposed the concept of identifying "Imbalance Point" to warn the body imbalance, allowing sufficient time to recover balance.

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy.

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.

Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation.

Background And Objective: Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored.

Enhanced assessment of human dynamic stability by eliminating the effect of body height: modeling and experiment study.

Margin of stability (MOS) is one of the essential indices for evaluating dynamic stability. However, there are indications that MOS was affected by body height and its application in identifying factors on dynamic stability other than body height is restricted. An inverted pendulum model was used to simulate human walking and investigate the relevance between MOS and body height.

A model of the pulmonary acinar circulatory system with gas exchange function to explore the influence of alveolar diameter.

Lung diseases such as acute respiratory distress syndrome affect the patient's lung compliance, which in turn affects the ability of gas exchange. Changes in alveolar diameter relate to local lung compliance. How alveolar diameter affects gas exchange, particularly oxygen concentrations in alveolar capillaries, is a topic of concern for researchers, and can be studied using mathematical models.

Visualizing the spatiotemporal pattern of yolk sac membrane vascular network by enhanced local fractal analysis.

Objective: To establish methods for providing a comprehensive and detailed description of the spatial distribution of the vascular networks, and to reveal the spatiotemporal pattern of the yolk sac membrane vascular network during the angiogenic procedure.

Methods: Addressing the limitations in the conventional local fractal analysis, an improved approach, named scanning average local fractal dimension, was proposed. This method was conducted on 6 high-resolution vascular images of the yolk sac membrane for 3 eggs at two stages (E3 and E4) to characterize the spatial distribution of the complexity of the vascular network.

Pulsatility damping in the microcirculation: Basic pattern and modulating factors.

Blood flow pulsatility is an important determinant of macro- and microvascular physiology. Pulsatility is damped largely in the microcirculation, but the characteristics of this damping and the factors that regulate it have not been fully elucidated yet. Applying computational approaches to real microvascular network geometry, we examined the pattern of pulsatility damping and the role of potential damping factors, including pulse frequency, vascular viscous resistance, vascular compliance, viscoelastic behavior of the vessel wall, and wave propagation and reflection.

Development of Novel Fractal Method for Characterizing the Distribution of Blood Flow in Multi-Scale Vascular Tree.

Blood perfusion is an important index for the function of the cardiovascular system and it can be indicated by the blood flow distribution in the vascular tree. As the blood flow in a vascular tree varies in a large range of scales and fractal analysis owns the ability to describe multi-scale properties, it is reasonable to apply fractal analysis to depict the blood flow distribution. The objective of this study is to establish fractal methods for analyzing the blood flow distribution which can be applied to real vascular trees.

Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model.

Background: Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients.

An adaptive fractal model for sublingual microcirculation.

The hemodynamic conditions and partial pressure of oxygen in microcirculation generally indicate the status of tissue perfusion, which provides essential information for the assessment and treatment of critical diseases such as sepsis. The human tongue is known to have abundant microcirculation and is an ideal window to observe the microcirculation. At present, the monitoring of sublingual microcirculation is mostly achieved using handheld vital microscopy (HVM).

Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls.

. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed.

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples.

A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit.

In intensive care unit (ICU), it is essential to predict the mortality of patients and mathematical models aid in improving the prognosis accuracy. Recently, recurrent neural network (RNN), especially long short-term memory (LSTM) network, showed advantages in sequential modeling and was promising for clinical prediction. However, ICU data are highly complex due to the diverse patterns of diseases; therefore, instead of single LSTM model, an ensemble algorithm of LSTM (eLSTM) is proposed, utilizing the superiority of the ensemble framework to handle the diversity of clinical data.

Investigation into the diversity in the fractal dimensions of arterioles and venules in a microvascular network - A quantitative analysis.

Fractal dimension is a robust fractal parameter for estimating the morphology of vascular networks. It reflects the property of vascular networks that may vary and thus, differentiate between individual networks and/or identify physiological and pathological conditions. As such, fractal dimension differs also between arteriolar and venular compartments, yet the underlying reason is so far unclear.

[Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm].

The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications.

[Analysis of the relevance of age and toe out angle of normal adults' gait].

Due to the decline of motor ability and the impact of the diseases, abnormalities in gait is common in the elderly population, which will raise the risk of fall and cause serious injury. This study focuses on the analysis of the gait kinematics parameters of normal adults' gait, aiming to investigate the characteristics of gait parameters in different age groups and to explore the role of gait parameters in motor function assessment and clinical diagnosis. Based on the gait data gained by electronic walkway, the relationship among the toe out angles and their correlation with age and gender etc.

Pulse wave velocity in the microcirculation reflects both vascular compliance and resistance: Insights from computational approaches.

Objective: PWV is the speed of pulse wave propagation through the circulatory system. mPWV emerges as a novel indicator of hypertension, yet it remains unclear how different vascular properties affect mPWV. We aim to identify the biomechanical determinants of mPWV.

Modeling of pulsatile flow-dependent nitric oxide regulation in a realistic microvascular network.

Hemodynamic pulsatility has been reported to regulate microcirculatory function. To quantitatively assess the impact of flow pulsatility on the microvasculature, a mathematical model was first developed to simulate the regulation of NO production by pulsatile flow in the microcirculation. Shear stress and pressure pulsatility were selected as regulators of endothelial NO production and NO-dependent vessel dilation as feedback to control microvascular hemodynamics.

The degree of heart rate asymmetry is crucial for the validity of the deceleration and acceleration capacity indices of heart rate: A model-based study.

The deceleration capacity (DC) and acceleration capacity (AC) of heart rate are a pair of indices used for evaluating the autonomic nervous system (ANS). We assessed the role of heart rate asymmetry (HRA) in defining the relative performance of DC and AC using a mathematical model, which is able to generate a realistic RR interval (RRI) time series with controlled ANS states. The simulation produced a set of RRI series with random sympathetic and vagal activities.

Do the deceleration/acceleration capacities of heart rate reflect cardiac sympathetic or vagal activity? A model study.

Despite increased application of the deceleration capacity (DC) and acceleration capacity (AC) of heart rate indices as indicators of autonomic nervous system (ANS) function, it remains controversial as to whether they reflect cardiac sympathetic or vagal activity. We addressed this problem using a cardiovascular system model that allows analysis of DC and AC under controllable levels of sympathetic and vagal activities. Multi-scale DCs and ACs with various timescales T and wavelet scales s were computed from the simulated RR interval series under randomly fluctuating levels of ANS activity, and the correlations of the indices to ANS functions were assessed.

[Real-time Gait Training System with Embedded Functional Electrical Stimulation].

To solve the problem that mostly gait analysis is independent from the treatment, this work proposes a system that integrates the functions of gait training and assessment for foot drop treatment. The system uses a set of sensors to collect gait parameters and designes multi-mode functional electrical stimulators as actuator. Body area network technology is introduced to coordinate the data communication and execution of the sensors and stimulators, synchronize the gait analysis and foot drop treatment.