Prognostic performance of two expert systems based on Bayesian belief networks

A decision support system for the prognosis at 24 h of head-injured patients of the intensive care unit (ICU), based on Bayesian belief networks, is constructed by model selection methods applied to a database (637 cases) of seven clinical and laboratory variables. Its performance is compared to other systems, including a simpler belief network that assumes conditional independence among the findings, and a human expert. Results indicate that its performance is not significantly different than that of the neurosurgeon expert and better than the performance of the independence model. Thus, the prognostic judgment of non-neurosurgeon ICU clinicians can be aided by the use of this system.     

一种高效的指令缓存单元架构及其性能分析

为了提高高速DSP或通用处理器的程序执行速度,描述了一种指令缓存单元的有效架构,特别是实现细节和性能分析.因所提出的指令缓存单元是为一种高性能VLIW结构的DSP核而设计,使用了并行的标签比较逻辑和寄存器堆的结构,芯片面积、关键路径延迟、功耗都大大减小.该指令缓存单元使用高层次的RTL(使用Verilog)编码,并由Synopsys的Design Compiler综合,使用不同的StarCoreTM基准程序测试比较,并进行性能分析.比较结果表明,所提出的结构是有效的,适合用于任何高速的处理器核.     

基于情感需求的ICU病房医护产品设计分析

从以人的情感需求作为ICU病房医护产品设计分析的切入点,从患者的认知方式、审美心理、病理情况等角度,对ICU病房医护产品的设计要素进行深层次的分析.以及ICU医护产品在使用过程中的缺陷对病患心理上的影响提出见解,目的在于能最大程度的提高ICU病房医护产品使用的效率.     

重症监护室＜ICU＞建设规划的探讨

阐述重症监护室(ICU)建筑面积与床位关系,污染走廊、辅助用房、卫生间的合理规划。     

Tight glycemic control in critical care--the leading role of insulin sensitivity and patient variability: a review and model-based analysis

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts.This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols.A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S_I). Variation in S_I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S_I) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.     

Improved blood glucose control for critically ill subjects

For patients in intensive care units (ICUs), control of blood glucose level is an important factor in reducing serious complications and mortality. Standard protocols for glucose control in ICUs have been based on infrequent glucose measurements, look-up tables to determine the appropriate insulin infusion rates, and bedside administration of the insulin infusion by ICU staff. In this paper a new automatic control strategy is proposed based on frequent glucose measurements and a self-tuning control technique. During a short initial time period when manual glucose control is performed using a standard protocol, a simple dynamic model of the glucose-insulin system is identified in real time using recursive least squares. Then an adaptive PID controller is tuned, based on the model parameters, and the controller is turned on. A simulation study based on detailed physiological models of the glucose-insulin dynamics demonstrates that the proposed control strategy performs better than standard protocols for insulin infusion.     

Intensive insulin therapy for critically ill subjects based on direct data-driven model predictive control

Patients in the intensive care units (ICU) can suffer from stress-induced hyperglycemia, which can result in negative outcomes and even death. Recent studies show that, regulation of blood glucose (BG) brings in improved outcomes. In this study, a novel direct data-driven model predictive control (MPC) strategy is developed to tightly regulate BG concentration in the ICU. The effectiveness of the proposed direct data-driven MPC strategy is validated on 30 virtual ICU patients, and the in silico results demonstrate the proposed method's excellent robustness with respect to intersubject variability and measurement noises. In addition, the mean percentage values in A-zone of the control variability grid analysis (CVGA) plots are 14% under the Yale protocol, 67% under the combination of particle swarm optimization (PSO) and MPC method (for short, termed as PSO–MPC method), and 90% under the proposed method. In summary, as a good candidate for full closed-loop glycemic control algorithm, the proposed method has superior performance to the nurse-driven Yale protocol and the closed-loop PSO–MPC method.     

引擎设计与跨平台应用研究*

ICU复杂文本布局引擎与其他应用较为广泛的复杂文本布局引擎相比的一大长处是它的开放源代码。通过分析ICU源码,并以此为基础设计新的复杂文本的布局引擎。首先简要介绍了复杂文本布局引擎的一般原理,然后以复杂文字语言蒙古文为例,详细探讨了基于ICU的复杂文本布局引擎的设计思路和方法,最后对复杂文本布局引擎的跨平台应用进行了深入研究。     

A case of hypothermia on CRRT

A 64‐year‐old Asian man, with past medical history of hypertension, hypothyroidism, and hyperlipidemia, presented with 3 days history of fever associated with cough and worsening shortness of breath. Subsequent clinical course was complicated by acute lung injury leading to acute respiratory distress syndrome requiring positive pressure ventilation, septic shock requiring inotropic support, and acute kidney injury requiring continuous renal replacement therapy (CRRT). On day 3 of CRRT, the patient developed significant hypothermia (temporal temperature 27.5°C), which was successfully managed. Continuous renal replacement therapy was subsequently discontinued as renal function recovered and the patient was discharged home after a prolonged hospital stay. He currently remains off dialysis and is being followed as an outpatient for chronic kidney disease. In this article, we examine various aspects of pathophysiology and management of hypothermia on CRRT and review relevant literature in this field.     

Induced L2-norm minimization of glucose-insulin system for Type I diabetic patients

Using induced L₂-norm minimization, a robust controller was developed for insulin delivery in Type I diabetic patients. The high-complexity nonlinear diabetic patient Sorensen-model was considered and Linear Parameter Varying methodology was used to develop open-loop model and robust H_∞ controller. Considering the normoglycaemic set point (81.1 mg/dL), a polytopic set was created over the physiologic boundaries of the glucose-insulin interaction of the Sorensen-model. In this way, Linear Parameter Varying model formalism was defined. The robust control was developed considering input and output multiplicative uncertainties with two additional uncertainties from those used in the literature: sensor noise and worst-case design for meal disturbance (60 g carbohydrate). Simulation scenario on large meal absorption illustrates the applicability of the robust LPV control technique, while patient variability is tested with real data taken from the SPRINT clinical protocol on ICU patients.