Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Alirocumab therapy in individuals with type 2 diabetes mellitus and atherosclerotic cardiovascular disease:analysis of the ODYSSEY DM-DYSLIPIDEMIA and DM-INSULIN studies

Background Individuals with diabetes often have high levels of atherogenic lipoproteins and cholesterol reflected by elevated low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), apolipoprotein B (ApoB), and LDL particle number (LDL-PN). The presence of atherosclerotic cardiovascular disease (ASCVD) increases the risk of future cardiovascular events. We evaluated the efficacy and safety of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor, alirocumab, among individuals with type 2 diabetes (T2DM), high LDL-C or non-HDL-C, and established ASCVD receiving maximally tolerated statin in ODYSSEY DM-DYSLIPIDEMIA (NCT02642159) and DM-INSULIN (NCT02585778). Methods In DM-DYSLIPIDEMIA, individuals with T2DM and mixed dyslipidemia (non-HDL-C ≥ 100 mg/dL; n = 413) were randomized to open-label alirocumab 75 mg every 2 weeks (Q2W) or usual care (UC) for 24 weeks, with UC options selected before stratified randomization. In DM-INSULIN, insulin-treated individuals with T2DM (LDL-C ≥ 70 mg/dL; n = 441) were randomized in a double-blind fashion to alirocumab 75 mg Q2W or placebo for 24 weeks. Study participants also had a glycated hemoglobin < 9% (DM-DYSLIPIDEMIA) or < 10% (DM-INSULIN). Alirocumab dose was increased to 150 mg Q2W at week 12 if week 8 LDL-C was ≥ 70 mg/dL (DM-INSULIN) or non-HDL-C was ≥ 100 mg/dL (DM-DYSLIPIDEMIA). Lipid reductions and safety were assessed in patients with ASCVD from these studies. Results This analysis included 142 DM-DYSLIPIDEMIA and 177 DM-INSULIN participants with ASCVD, including 95.1% and 86.4% with coronary heart disease, and 32.4% and 49.7% with microvascular diabetes complications, respectively. At week 24, alirocumab significantly reduced LDL-C, non-HDL-C, ApoB, and LDL-PN from baseline versus control. This translated into a greater proportion of individuals achieving non-HDL-C < 100 mg/dL (64.6% alirocumab/23.8% UC [DM-DYSLIPIDEMIA]; 65.4% alirocumab/14.9% placebo [DM-INSULIN]) and ApoB < 80 mg/dL (75.1% alirocumab/35.4% UC and 76.8% alirocumab/24.8% placebo, respectively) versus control at week 24 (all P < 0.0001). In pooling these studies, 66.4% (alirocumab) and 67.0% (control) of individuals reported treatment-emergent adverse events. The adverse event pattern was similar with alirocumab versus controls. Conclusions Among individuals with T2DM and ASCVD who had high non-HDL-C/LDL-C levels despite maximally tolerated statin, alirocumab significantly reduced atherogenic cholesterol and LDL-PN versus control. Alirocumab was generally well tolerated

Real-time estimation of dynamic functional connectivity networks

Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time

The Lifespan Human Connectome Project in Aging: An overview.

The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al., 2018), and the image acquisition protocol common to both studies (Harms et al., 2018)