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- Fazel-Najafabadi, Azam, et al.
(författare)
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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments
- 2024
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Ingår i: Tsinghua Science and Technology. - : Tsinghua University Press. - 1007-0214 .- 1878-7606. ; 29:4, s. 1118-1137
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Tidskriftsartikel (refereegranskat)abstract
- The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.
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- Fazel, Seena, et al.
(författare)
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Risk of death by suicide following self-harm presentations to healthcare : development and validation of a multivariable clinical prediction rule (OxSATS)
- 2023
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Ingår i: BMJ Mental Health. - : BMJ Publishing Group Ltd. - 2755-9734. ; 26:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes. OBJECTIVE: We developed and validated a predictive model for suicide following self-harm.METHODS: We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work.FINDINGS: An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS).CONCLUSIONS: OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility.CLINICAL IMPLICATIONS: Using a clinical prediction score may assist clinical decision-making and resource allocation.
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- Lagerberg, T, et al.
(författare)
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Selective serotonin reuptake inhibitors and suicidal behaviour: a population-based cohort study
- 2022
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Ingår i: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. - : Springer Science and Business Media LLC. - 1740-634X. ; 47:4, s. 817-823
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Tidskriftsartikel (refereegranskat)abstract
- There is concern that selective serotonin reuptake inhibitor (SSRI) treatment may increase the risk of suicide attempts or deaths, particularly among children and adolescents. However, debate remains regarding the nature of the relationship. Using nationwide Swedish registers, we identified all individuals aged 6–59 years with an incident SSRI dispensation (N = 538,577) from 2006 to 2013. To account for selection into treatment, we used a within-individual design to compare the risk of suicide attempts or deaths (suicidal behaviour) in time periods before and after SSRI-treatment initiation. Within-individual incidence rate ratios (IRRs) of suicidal behaviour were estimated. The 30 days before SSRI-treatment initiation was associated with the highest risk of suicidal behaviour compared with the 30 days 1 year before SSRI initiation (IRR = 7.35, 95% CI 6.60–8.18). Compared with the 30 days before SSRI initiation, treatment periods after initiation had a reduced risk—the IRR in the 30 days after initiation was 0.62 (95% CI 0.58–0.65). The risk then declined over treatment time. These patterns were similar across age strata, and when stratifying on history of suicide attempts. Initiation with escitalopram was associated with the greatest risk reduction, though CIs for the IRRs of the different SSRI types were overlapping. The results do not suggest that SSRI-treatment increases the risk for suicidal behaviour in either youths or adults; rather, it may reduce the risk. Further research with different study designs and in different populations is warranted.
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- Lagerberg, T, et al.
(författare)
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Use of central nervous system drugs in combination with selective serotonin reuptake inhibitor treatment: A Bayesian screening study for risk of suicidal behavior
- 2022
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Ingår i: Frontiers in psychiatry. - : Frontiers Media SA. - 1664-0640. ; 13, s. 1012650-
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Tidskriftsartikel (refereegranskat)abstract
- Using other central nervous system (CNS) medications in combination with selective serotonin reuptake inhibitor (SSRI) treatment is common. Despite this, there is limited evidence on the impact on suicidal behavior of combining specific medications. We aim to provide evidence on signals for suicidal behavior risk when initiating CNS drugs during and outside of SSRI treatment.Materials and methodsUsing a linkage of Swedish national registers, we identified a national cohort of SSRI users aged 6–59 years residing in Sweden 2006–2013. We used a two-stage Bayesian Poisson model to estimate the incidence rate ratio (IRR) of suicidal behavior in periods up to 90 days before and after a CNS drug initiation during SSRI treatment, while accounting for multiple testing. For comparison, and to assess whether there were interactions between SSRIs and other CNS drugs, we also estimated the IRR of initiating the CNS drug without SSRI treatment.ResultsWe identified 53 common CNS drugs initiated during SSRI treatment, dispensed to 262,721 individuals. We found 20 CNS drugs with statistically significant IRRs. Of these, two showed a greater risk of suicidal behavior after versus before initiating the CNS drug (alprazolam, IRR = 1.39; flunitrazepam, IRR = 1.83). We found several novel signals of drugs that were statistically significantly associated with a reduction in the suicidal behavior risk. We did not find evidence of harmful interactions between SSRIs and the selected CNS drugs.ConclusionSeveral of the detected signals for reduced risk correspond to drugs where there is previous evidence of benefit for antidepressant augmentation (e.g., olanzapine, quetiapine, lithium, buspirone, and mirtazapine). Novel signals of reduced suicidal behavior risk, including for lamotrigine, valproic acid, risperidone, and melatonin, warrant further investigation.
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