Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency

Computational intelligence is often used in smart environment applications in order to determine a user’s context. Many computational intelligence algorithms are complex and resource-consuming which can be problematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. These types of devices are, however, highly useful in pervasive and mobile computing due to their small size, energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classifier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers. CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing. The classifier was evaluated on eight different datasets of various types. Our results show that CORPSE, despite its simplistic design, has comparable performance to some common machine learning algorithms. This makes the classifier a viable choice for use in pervasive systems that have limited resources, requires energy-efficiency, or have the need for fast real-time responses.publishedVersionnivå

United States Acculturation and Cancer Patients' End-of-Life Care

Background: Culture shapes how people understand illness and death, but few studies examine whether acculturation influences patients' end-of-life treatment preferences and medical care. Methods and Findings: In this multi-site, prospective, longitudinal cohort study of terminally-ill cancer patients and their caregivers (n = 171 dyads), trained interviewers administered the United States Acculturation Scale (USAS). The USAS is a 19-item scale developed to assess the degree of "Americanization" in first generation or non-US born caregivers of terminally-ill cancer patients. We evaluated the internal consistency, concurrent, criterion, and content validity of the USAS. We also examined whether caregivers' USAS scores predicted patients' communication, treatment preferences, and end-of-life medical care in multivariable models that corrected for significant confounding influences (e.g. education, country of origin, English proficiency). The USAS measure was internally consistent (Cronbach α = 0.98); and significantly associated with US birthplace (r = 0.66, P<0.0001). USAS scores were predictive of patients' preferences for prognostic information (AOR = 1.31, 95% CI:1.00-1.72), but not comfort asking physicians' questions about care (AOR 1.23, 95% CI:0.87-1.73). They predicted patients' preferences for feeding tubes (AOR = 0.68, 95% CI:0.49-0.99) and wish to avoid dying in an intensive care unit (AOR = 1.36, 95% CI:1.05-1.76). Scores indicating greater acculturation were also associated with increased odds of patient participation in clinical trials (AOR = 2.20, 95% CI:1.28-3.78), compared with lower USAS scores, and greater odds of patients receiving chemotherapy (AOR = 1.59, 95% CI:1.20-2.12). Conclusion: The USAS is a reliable and valid measure of "Americanization" associated with advanced cancer patients' end-of-life preferences and care. USAS scores indicating greater caregiver acculturation were associated with increased odds of patient participation in cancer treatment (chemotherapy, clinical trials) compared with lower scores. Future studies should examine the effects of acculturation on end-of-life care to identify patient and provider factors that explain these effects and targets for future interventions to improve care (e.g., by designing more culturally-competent health education materials). © 2013 Wright et al

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Decentralized Location-aware Orchestration of Containerized Microservice Applications : Enabling Distributed Intelligence at the Edge

Services that operate on public, private, or hybrid clouds, should always be available and reachable to their end-users or clients. However, a shift in the demand for current and future services has led to new requirements on network infrastructure, service orchestration, and Quality-of-Service (QoS). Services related to, for example, online-gaming, video-streaming, smart cities, smart homes, connected cars, or other Internet-of-Things (IoT) powered use cases are data-intensive and often have real-time and locality requirements. These have pushed for a new computing paradigm, Edge computing, based on moving some intelligence from the cloud to the edge of the network to minimize latency and data transfer. This situation has set new challenges for cloud providers, telecommunications operators, and content providers. This thesis addresses two issues in this problem area that call for distinct approaches and solutions. Both issues share the common objectives of improving energy-efficiency and mitigating network congestion by minimizing data transfer to boost service performance, particularly concerning latency, a prevalent QoS metric. The first issue is related to the demand for a highly scalable orchestrator that can manage a geographically distributed infrastructure to deploy services efficiently at clouds, edges, or a combination of these. We present an orchestrator using process containers as the virtualization technology for efficient infrastructure deployment in the cloud and at the edge. The work focuses on a Proof-of-Concept design and analysis of a scalable and resilient decentralized orchestrator for containerized applications, and a scalable monitoring solution for containerized processes. The proposed orchestrator deals with the complexity of managing a geographically dispersed and heterogeneous infrastructure to efficiently deploy and manage applications that operate across different geographical locations — thus facilitating the pursuit of bringing some of the intelligence from the cloud to the edge, in a way that is transparent to the applications. The results show this orchestrator’s ability to scale to 20 000 nodes and to deploy 30 000 applications in parallel. The resource search algorithm employed and the impact of location awareness on the orchestrator’s deployment capabilities were also analyzed and deemed favorable. The second issue is related to enabling fast real-time predictions and minimizing data transfer for data-intensive scenarios by deploying machine learning models at devices to decrease the need for the processing of data by upper tiers and to decrease prediction latency. Many IoT or edge devices are typically resource-scarce, such as FPGAs, ASICs, or low-level microcontrollers. Limited devices make running well-known machine learning algorithms that are either too complex or too resource-consuming unfeasible. Consequently, we explore developing innovative supervised machine learning algorithms to efficiently run in settings demanding low power and resource consumption, and realtime responses. The classifiers proposed are computationally inexpensive, suitable for parallel processing, and have a small memory footprint. Therefore, they are a viable choice for pervasive systems with one or a combination of these limitations, as they facilitate increasing battery life and achieving reduced predictive latency. An implementation of one of the developed classifiers deployed to an off-the-shelf FPGA resulted in a predictive throughput of 57.1 million classifications per second, or one classification every 17.485 ns

Analysis of End-User programming platforms

End-user programming platforms allow end-users with and without programming experienceto build applications using a user-friendly graphical environment. This study reviews dif-ferent types of end-user platforms focusing on the features obtained from previous end-usersoftware engineering studies: What You See Is What You Get(WYSIWYG), What You TestIs What You Get (WTISWYG), how the learning by examples methodology is implementedand how the performance of end-user programmers is increased through reusable code. Thestudy also establishes the dierence between end-user programming platforms and tradi-tional programming platforms based on the programmer's interaction. In this report, a newin-between category is dened as End-User Professional Programming Platform, which rep-resents the end-user programming platforms that require the end-user programmer to havea certain programming knowledge. Finally, the research discusses current trends and de-nes new features for the future of end-user platforms, in particular the denition of a newconcept, which is What You SAy Is What You Get(WYSAIWYG).Godkänd; 2015; 20150520 (andbra

Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency

Computational intelligence is often used in smart environment applications in order to determine a user’scontext. Many computational intelligence algorithms are complex and resource-consuming which can beproblematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. Thesetypes of devices are, however, highly useful in pervasive and mobile computing due to their small size,energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classi-fier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers.CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing.The classifier was evaluated on eight different datasets of various types. Our results show thatCORPSE, despite its simplistic design, has comparable performance to some common machine learningalgorithms. This makes the classifier a viable choice for use in pervasive systems that have limitedresources, requires energy-efficiency, or have the need for fast real-time responses.Konferensartikel i tidskrift</p

Clinical Trial Participation among Ethnic/Racial Minority and Majority Patients with Advanced Cancer: What Factors Most Influence Enrollment?

BACKGROUND: Studies using administrative data report that racial/ethnic minority patients enroll in clinical trials less frequently than white patients. We studied a cohort of terminally ill cancer patients to determine a) if racial/ethnic minority patients have lower rates of drug trial enrollment than white patients once socioeconomic characteristics are accounted for and b) what factors most influence drug trial enrollment among patients with advanced canceroverall. METHODS: : Coping with Cancer (CwC) is a National Cancer Institute/National Institute of Mental Health (NCI/NIMH)-funded multisite, prospective, longitudinal study of patients with advanced cancer. Baseline interviews assessed drug trial enrollment as well as socioeconomic characteristics. Logistic regression models estimated associations between drug trial enrollment and baseline characteristics. Stepwise, backward, and subset model selection was applied to select the final model where characteristics significant at α=0.05 remained in the model. RESULTS: At a median of 4.4 months prior to death, 35 of 358 patients (9.8%) were enrolled in a drug trial. In unadjusted analyses, race/ethnicity, health insurance, performance status, recruitment site, cancer type, preference for life-extending care, and lack of end-of-life care planning were associated (p<0.05) with enrollment. In multivariable analysis, patient race/ethnicity was not significantly associated with enrollment. Patients who reported not having an end-of-life discussion (adjusted odds ratio [AOR], 0.18; 95% confidence interval [CI] 0.04–0.83) and those not wanting to discuss life expectancy (AOR, 0.31; 95%CI 0.12–0.79) were more likely to be trial enrollees. CONCLUSION: Patient race/ethnicity was not associated with clinical trial enrollment after adjustment for socioeconomic covariates. Patients with advanced cancer endorsing less engagement in end-of-life planning were more likely to be enrolled in a clinical trial

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc