Case-based System Research Articles

Occupational health risk assessment of PC production-caused pollution based on damage assessment and cyclic mitigation model

PurposeThe occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.Design/methodology/approachThe DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.FindingsThe results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.Originality/valueBased on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

Construction of Digital Case Base System Based on DB2 Database

DB2 database supports from mainframe to single-user environment, and makes database localization and remote connection transparent, simplifies the operation and maintenance process of database system, and has become the most important platform for data storage and processing of mainframe system. Digital teaching resources simplify the knowledge and teaching difficulties through multimedia devices, making the communication and interaction between teachers and students more convenient. Teaching case base is one of the digital teaching resources, and the construction of digital teaching case base for recitation based on DB2 database meets the needs of teaching reform and development in the information age. The DB2 object architecture and DB2 Viper architecture are analysed, the database design and functional design of digital teaching case base system for recitation are carried out, and the specific application scheme is proposed.

Advancing the frontiers: Revolutionary control and prevention paradigms against Nipah virus.

Nipah Virus (NiV) is a highly virulent pathogen that poses a significant threat to human and animal populations. This review provides a comprehensive overview of the latest control and prevention strategies against NiV, focusing on vaccine development, antiviral drug discovery, early diagnosis, surveillance, and high-level biosecurity measures. Advancements in vaccine research, including live-attenuated vaccines, virus-like particles, and mRNA-based vaccines, hold promise for preventing NiV infections. In addition, antiviral drugs, such as remdesivir, ribavirin, and favipiravir, have the potential to inhibit NiV replication. Early diagnosis through molecular and serological assays, immunohistochemistry, and real-time reverse transcription polymerase chain reaction plays a crucial role in timely detection. Surveillance efforts encompassing cluster-based and case-based systems enhance outbreak identification and provide valuable insights into transmission dynamics. Furthermore, the implementation of high-level biosecurity measures in agriculture, livestock practices, and healthcare settings is essential to minimize transmission risks. Collaboration among researchers, public health agencies, and policymakers is pivotal in refining and implementing these strategies to effectively control and prevent NiV outbreaks and safeguard public health on a global scale.

Methodology for development of an expert system to derive knowledge from existing nature-based solutions experiences

Learning from past experiences is essential for the adoption of Nature-Based Solutions (NBS). There is a growing number of knowledge repositories sharing the experience of NBS projects implemented worldwide. These repositories provide access to a large amount of information, however, acquiring knowledge from them remains a challenge. This paper outlines the technical details of the NBS Case-Based System (NBS-CBS), an expert system that facilitates knowledge acquisition from an NBS case repository. The NBS-CBS is a hybrid system integrating a black-box Artificial Neural Network (ANN) with a white-box Case-Based Reasoning model. The system involves:•a repository that stores the information of past NBS projects, and an input collection component, guiding the collection and encoding of the user's inputs;•a classifier that predicts solutions (i.e., generates a hypothesis), based on user input (target case), drawing on a pre-trained ANN model to guide the case retrieval, and a case retrieval engine that identifies cases similar to the target case;•a case adaption and retainment process in which the user assesses the provided recommendations and retains the solved problem as a new case in the repository.

The Nature-Based Solutions Case-Based System: A hybrid expert system

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a ‘black-box’ artificial neural networks model with a ‘white-box’ case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process.

Can case-based payment contain healthcare costs? - A curious case from China

We adopted a difference-in-difference (DID) design to evaluate the impact of a case-based payment pilot in Tianjin, China on hospital admission, utilization of varied therapeutic regimes, and the associated costs. We used claim data of all admissions of angina and acute myocardial infarction during July 2015 to June 2018, 18 months before and after the program. Our analyses were supported by convincing common trends tests and a couple of sensitivity analyses. As intended, for patients who received percutaneous coronary stenting (PCS) and were counted in the case-based payment system, we showed that the program decreased length-of-stay, per-admission spending, and out-of-pocket spending by 20.8%, 14.2%, and 95.5%, respectively, but did not increase readmissions. However, when considering all patients who suffered from the two types of coronary heart diseases, we found that the program otherwise increased per-admission spending by nearly 11%. As a result, the program took a perverse effect in increasing monthly spending for the health insurance scheme and the society by 1005.6 thousand USD (47·5%) and 1095·7 thousand USD (34·7%), respectively. Increases in hospital admissions, and proportion of performing PCS accounted for 66·7% and 39·2% of the rise, respectively. In addition, our analysis provided evidence of health providers’ cream-skimming behaviors, including selecting younger patients with lower CCI in the case-based system, up-coding complications, and keeping higher cost patients in the fee-for-service payment system. We draw lessons that case-based payment may make an unintended impact that increases healthcare costs when incentives are not properly designed.

Automatic Extraction of English-Chinese Translation Templates Based on Deep Learning

Translation templates are an important cause of knowledge in machine translation (MT) systems. Their quality and scale directly influence the performance of MT systems. How to obtain high-quality and efficient translation templates from corpora has become a hot topic in recent study. In this paper, a tree to String alignment template (TAT) based on syntactic structure is proposed. This template describes the alignment between the source language syntax tree and the target language string. The syntactic structure, a large number of construction tags, and variables are introduced into the template, which enables the syntactic model to deal with discontinuous phrases and has the ability of generalization. Templates can be used in syntactic statistics, case-based, and rule-based MT systems according to different decoders. ATTEBSC algorithm is a basic method to learn translation templates by comparing sentence pairs. It demands that sentence pairs be constructed in a precise comparison structure ahead of time, but there are no strict guidelines on how to do it. In this paper, we propose a method to calculate the specific comparison scheme using the longest common subsequence (LCS) and use the normalized LCS distance to screen sentences with high similarity and then use the ATTEBSC algorithm to automatically remove the template. Experiments show that this method is easy and effective, and many expensive templates can be learned.

Multiple Imputation of Missing Race and Ethnicity in CDC COVID-19 Case-Level Surveillance Data.

The COVID-19 pandemic has resulted in a disproportionate burden on racial and ethnic minority groups, but incompleteness in surveillance data limits understanding of disparities. CDC's case-based surveillance system contains case-level information on most COVID-19 cases in the United States. Data analyzed in this paper contain COVID-19 cases with case-level information through September 25, 2020, which represent 70.9% of all COVID-19 cases reported to CDC during the period. Case-level surveillance data are used to investigate COVID-19 disparities by race/ethnicity, sex, and age. However, demographic information on race and ethnicity is missing for a substantial percentage of COVID-19 cases (e.g., 35.8% and 47.2% of cases analyzed were missing race and ethnicity information, respectively). Our goal in this study was to impute missing race and ethnicity to derive more accurate incidence and incidence rate ratio (IRR) estimates for different racial and ethnic groups, and evaluate the results from imputation compared to complete case analysis, which involves removing cases with missing race/ethnicity information from the analysis. Two multiple imputation (MI) models were developed. Model 1 imputes race using six binary race variables, and Model 2 imputes race as a composite multinomial variable. Our evaluation found that compared with complete case analysis, MI reduced biases and improved coverage on incidence and IRR estimates for all race/ethnicity groups, except for the Non-Hispanic Multiple/other group. Our research highlights the importance of supplementing complete case analysis with additional methods of analysis to better describe racial and ethnic disparities. When race and ethnicity data are missing, multiple imputation may provide more accurate incidence and IRR estimates to monitor these disparities in tandem with efforts to improve the collection of race and ethnicity information for pandemic surveillance.

A focus on the future of opioid prescribing: implementation of a virtual opioid and pain management module for medical students

BackgroundThe United States opioid epidemic is a devastating public health crisis fueled in part by physician prescribing. While the next generation of prescribers is crucial to the trajectory of the epidemic, medical school curricula designated to prepare students for opioid prescribing (OP) and pain management is often underdeveloped. In response to this deficit, we aimed to investigate the impact of an online opioid and pain management (OPM) educational intervention on fourth-year medical student knowledge, attitudes, and perceived competence.MethodsGraduating students completing their final year of medical education at Sidney Kimmel Medical College of Thomas Jefferson University were sent an e-mail invitation to complete a virtual OPM module. The module consisted of eight interactive patient cases that introduced topics through a case-based learning system, challenging students to make decisions and answer knowledge questions about the patient care process. An identical pre- and posttest were built into the module to measure general and case-specific learning objectives, with responses subsequently analyzed using the Wilcoxon matched-pairs signed-rank test.ResultsForty-three students (19% response rate) completed the module. All median posttest responses ranked significantly higher than paired median pretest responses (p < 0.05). Comparing the paired overall student baseline score to module completion, median posttest ranks (Mdn = 206, IQR = 25) were significantly higher than median pretest ranks (Mdn = 150, IQR = 24) (p < 0.001). Regarding paired median Perceived Competence Scale metrics specifically, perceived student confidence, capability, and ability in opioid management increased from “disagree” (2) to “agree” (4) (p < 0.001), and student ability to meet the challenge of opioid management increased from “neither agree nor disagree” (3) to “agree” (4) (p < 0.001). Additionally, while 77% of students reported receiving OP training in medical school, 21% reported no history of prior training.ConclusionImplementation of a virtual, interactive module with clinical context is an effective framework for improving the OPM knowledge, attitudes, and perceived competence of fourth-year medical students. This type of intervention may be an important method for standardizing and augmenting the education of future prescribers across multiple institutions.

Progress and challenges of integrated drug efficacy surveillance for uncomplicated malaria in Thailand

BackgroundIntegrated drug efficacy surveillance (iDES) was formally introduced nationally across Thailand in fiscal year 2018 (FY2018), building on a history of drug efficacy monitoring and interventions. According to the National Malaria Elimination Strategy for Thailand 2017–2026, diagnosis is microscopically confirmed, treatment is prescribed, and patients are followed up four times to ensure cure.MethodsRoutine patient data were extracted from the malaria information system for FY2018–FY2020. Treatment failure of first-line therapy was defined as confirmed parasite reappearance within 42 days for Plasmodium falciparum and 28 days for Plasmodium vivax. The primary outcome was the crude drug efficacy rate, estimated using Kaplan–Meier methods, at day 42 for P. falciparum treated with dihydroartemisinin–piperaquine plus primaquine, and day 28 for P. vivax treated with chloroquine plus primaquine; day 60 and day 90 efficacy were secondary outcomes for P. vivax.ResultsThe proportion of patients with outcomes recorded at day 42 for P. falciparum malaria and at day 28 for P. vivax malaria has been increasing, with FY2020 follow-up rates of 61.5% and 57.2%, respectively. For P. falciparum malaria, day 42 efficacy in FY2018 was 92.4% (n = 249), in FY2019 93.3% (n = 379), and in FY2020 98.0% (n = 167). Plasmodium falciparum recurrences occurred disproportionally in Sisaket Province, with day 42 efficacy rates of 75.9% in FY2018 (n = 59) and 49.4% in FY2019 (n = 49), leading to an update in first-line therapy to pyronaridine–artesunate at the provincial level, rolled out in FY2020. For P. vivax malaria, day 28 efficacy (chloroquine efficacy) was 98.5% in FY2018 (n = 2048), 99.1% in FY2019 (n = 2206), and 99.9% in FY2020 (n = 2448), and day 90 efficacy (primaquine efficacy) was 94.8%, 96.3%, and 97.1%, respectively.ConclusionsIn Thailand, iDES provided operationally relevant data on drug efficacy, enabling the rapid amendment of treatment guidelines to improve patient outcomes and reduce the potential for the spread of drug-resistant parasites. A strong case-based surveillance system, integration with other health system processes, supporting biomarker collection and molecular analyses, and cross-border collaboration may maximize the potential of iDES in countries moving towards elimination.

Development of a Design Strategy for Playful Products of Older Adults

To support developing new playful product for older adults, this paper briefly reviewed a range of existing playful products, and describes how they could be strategically deconstructed and refactored from the perspective of the five interaction elements (person, purpose, means, contexts, and actions). A design framework combining case analogies and persona methods was proposed. The formalized design strategy of persona and case-based interaction elements analysis system that mainly considers two parts in the design process which are called ‘inspiration cases’ and ‘typical users’ in this paper. The strategy can be applied to the process of re-designing a specific class of playful products, such as tangible toys or digital games, for particular older adults.

HIV in Canada-surveillance report, 2019.

Human immunodeficiency virus (HIV) is a global public health issue. HIV has been nationally notifiable in Canada since 1985. The Public Health Agency of Canada (PHAC) monitors trends in new HIV diagnoses. The objective of this surveillance report is to provide an overview of the epidemiology of reported HIV cases in 2019 in Canada. The report highlights 10-year trends (2010-2019). Data on HIV diagnosed through Immigration Medical Exams (IME) and trends in perinatal transmission of HIV are also presented. PHAC monitors HIV through the HIV/AIDS Surveillance System, a passive, case-based system that collates non-nominal data submitted voluntarily by all Canadian provinces and territories. Descriptive analyses were conducted on national data. IME data were obtained from Immigration, Refugees and Citizenship Canada (IRCC), and data on HIV-exposed pregnancies were obtained through the Canadian Perinatal HIV Surveillance Program. In 2019, a total of 2,122 HIV diagnoses were reported in Canada (5.6 per 100,000 population). Saskatchewan reported the highest provincial diagnosis rate at 16.9 per 100,000 population. The 30 to 39-year age group had the highest HIV diagnosis rate at 12.7 per 100,000 population. While the rates for both males and females fluctuated in the past decade, since 2010 the rates among males decreased overall, while the rate among females increased slightly. As in previous years, the diagnosis rate for males in 2019 was higher than that for females (7.9 versus 3.4 per 100,000 population, respectively). The highest proportion of all reported adult cases with known exposure were gay, bisexual and other men who have sex with men (gbMSM, 39.7%), followed by cases attributed to heterosexual contact (28.3%) and among people who inject drugs (PWID, 21.5%). The number of migrants who tested positive for HIV during an IME conducted in Canada was 626. The one documented perinatal HIV transmission related to a mother who had not received antepartum or intrapartum antiretroviral therapy prophylaxis. The number and rate of reported HIV cases in Canada has remained relatively stable over the last decade, with minor year-to-year variations. As in previous years, the gbMSM and PWID populations represent a high proportion of HIV diagnoses, although a sizable number of cases were attributed to heterosexual contact. It is important to routinely monitor trends in HIV in light of pan-Canadian commitments to reduce the health impact of sexually transmitted and blood-borne infections by 2030.

Improving Dengue Surveillance System with Administrative Claim Data in Indonesia: Opportunities and Challenges.

Administrative claim data is believed as one of the promising data set to augment the mandatory surveillance system which suffered from under-reporting and delay in reporting. Therefore, this study aims to examine whether the Indonesian National Health Insurance (INHI) sample data could complement dengue case-based surveillance system in a more practical way. Afterwards, this analysis also identified several future opportunities and challenges in improving the dengue surveillance system. We utilized the referral care table linked with capitation and non-capitation-based primary care service table from 2015-2016. Data cleaning, query and visualization were performed using Tableau Public and Microsoft Power BI. Result shows that dengue referral pattern is indicating the opportunity to detect dengue cases in an earlier stage and high utilization of referral care disclose the patient behaviour. Therefore, anonymous INHI sample data set potentially to complement dengue traditional surveillance system. A huge number of health facilities as data providers, bridging and interoperability chance and opportunity of early detection are identified as future opportunities. However, we also determine challenges involving how to provide the mechanism for the quick and interoperable reporting system, how to construct supportive regulation and anticipatory approach regarding the change in dengue diagnosis criteria as the implementation of ICD 11 code. Thus, practical approaches should be prepared to support the utilization of INHI sample data.

Trends in measles cases in Bayelsa state, Nigeria: a five-year review of case-based surveillance data (2014\u20132018)

BackgroundMeasles is a vaccine preventable, highly transmissible viral infection that affects mostly children under five years. It has been ear marked for elimination and Nigeria adopted the measles elimination strategies of the World Health Organization (WHO) African region to reduce cases and deaths. This study was done to determine trends in measles cases in Bayelsa state, to describe cases in terms of person and place, identify gaps in the case-based surveillance data collection system and identify risk factors for measles infection.MethodsWe carried out a secondary data analysis of measles case-based surveillance data for the period of January 2014 to December 2018 obtained in Microsoft Excel from the State Ministry of Health. Cases were defined according to WHO standard case definitions. We calculated frequencies, proportions, estimated odds ratios (OR), 95% confidence intervals (CI) and multivariate analysis.ResultsA total of 449 cases of measles were reported. There were 245(54.6%) males and the most affected age group was 1–4 years with 288(64.1%) cases. Of all cases, 289(9.35%) were confirmed and 70 (48.27%) had received at least one dose of measles vaccine. There was an all-year transmission with increased cases in the 4th quarter of the year. Yenegoa local government area had the highest number of cases. Timeliness of specimen reaching the laboratory and the proportion of specimens received at the laboratory with results sent to the national level timely were below WHO recommended 80% respectively. Predictors of measles infection were, age less than 5 years (AOR: 0.57, 95% CI: 0.36–0.91) and residing in an urban area (AOR: 1.55, 95% CI:1.02–2.34).ConclusionsMeasles infection occurred all-year round, with children less than 5 years being more affected. Measles case-based surveillance system showed high levels of case investigation with poor data quality and poor but improving indicators. Being less than 5 years was protective of measles while living in urban areas increased risk for infection. We recommended to the state government to prioritize immunization activities in the urban centers, start campaigns by the 4th quarter and continue to support measles surveillance activities and the federal government to strengthen regional laboratory capacities.

Empowering remote health centre with decision support system for diagnosis and treatment using NLP and nearest neighbors algorithms

The increasing demand for high-quality medical services coupled with the scarcity of medical experts leads to the use of electronic devices, information technology and its infrastructure in delivering health service. In line with that, this work proposes a solution to the problems of scarcity of experts especially in the remote regions of developing countries by developing a system that uses NLP and Nearest Neighbors Algorithms to learn from an online case-based repository built on a network of experts’ hospitals to aid remote health Centers in diagnosis and treatment. The results obtained from the empirical evaluation of the system show the efficiency of the system in assisting non-experts in decision making and could, therefore, improve the quality of service in the remote health center with a very minimal cost.Keywords: Case-Based system, NLP, Diagnosis, Nearest NeighborsVol. 26 No 1, June 2019

Intelligent Case-based System for Decision-making in the Electricity Sector

The energy is important for the economic development of nations; and one of the main challenges today. The Electrical Union develops two systems for the automatization of transmission and distribution process: the Integral System of Network Management (SIGERE) and the Integral Management System of the Electrical Industry Construction Enterprise (SIGECIE). The data collected facilitate and improve the efficiency in the operation, use, analysis, planning and management of the electricity networks. A weakness of the systems proposed is in the dissatisfaction with the queries carried out. Hence, the objective of the research is: to develop intelligent queries in real time, based on existing knowledge that facilitate decision making in the electrical energy transmission and distribution processes. In the research a case-based system is develop, that it’s based on the premise that similar previous problems will have similar solutions. The similarity between two cases is determined by the weighted sum of the distance of their traits and the calculation of the distance between traits was done according to its nature. The conceptual basis of the system is develop with a lightweight ontology which it is incorporated in the cases database as a trait. An intelligent real-time queries system for the Electric Union (SICUNE) was implemented, achieving the generation of automatic queries that allow the system to respond to any type of queries in real time. The experimental study showed the feasibility of the proposal

HIV in Canada-Surveillance Report, 2018.

Human immunodeficiency virus (HIV) is a global public health issue, with an estimated 36.9 million people living with HIV in 2017. HIV has been reportable in Canada since 1985 and the Public Health Agency of Canada (PHAC) continues to monitor trends in new HIV diagnoses. The objective of this surveillance report is to provide an overview of the epidemiology of all reported diagnoses of HIV in Canada since 1985 with a focus on 2018 overall, and by geographic location, age group, sex, and exposure category. PHAC monitors HIV through the national HIV/AIDS Surveillance System, a passive, case-based system that collates nonnominal data that is voluntarily submitted by all Canadian provinces and territories. Descriptive epidemiological analyses were conducted on national data and those relating to specific populations provided by Immigration, Refugees and Citizenship Canada and the Canadian Perinatal HIV Surveillance Program. In 2018, a total of 2,561 HIV diagnoses were reported in Canada, an increase of 8.2% compared with 2017. The national diagnosis rate increased to 6.9 per 100,000 population in 2018 from 6.5 per 100,000 population in 2017. Saskatchewan reported the highest provincial diagnosis rate at 14.9 per 100,000 population. The 30-39 year age group continued to have the highest HIV diagnosis rate at 15.4 per 100,000 population. Overall, the diagnosis rate for males continued to be higher than that of females (9.8 versus 4.0 per 100,000 population, respectively); however, females experienced a larger increase in reported cases and diagnosis rate. The gay, bisexual and other men who have sex with men (gbMSM) exposure category continued to represent the highest proportion of all reported adult cases (41.4%), though the proportion has decreased over time. Five perinatal HIV transmissions were documented, three were related to the mother not receiving perinatal antiretroviral therapy prophylaxis. The number and rate of reported HIV cases in Canada increased in 2018, gbMSM continued to account for the largest exposure category and the number and rate of reported HIV cases among women increased. PHAC will continue to work with its national partners to refine the collection, analysis and publication of national data to better understand the burden of HIV in Canada.

Design of an Intelligent Educational Resource Production System

In the field of learning, we are witnessing more and more the introduction of new environments in order to better meet the specific needs of the main actors of the process. The shift from face-to-face learning to distance learning or e-learning has overcome some of the challenges of availability, location, prerequisites, but has been rapidly impacted by the development of mobile technology. As a result, m-learning appeared and quickly evolved into p-learning.&#x0D; The arrival of the "Open Software" concept has given birth to several "open-something" initiatives, among which are the Open Educational Resource (OER) and the Massive Online Open Course (MOOC). These learning resources have also made progress, although they are fairly recent. Admittedly, this diversity of environments offers a wealth and a multitude of pedagogical resources. However, the question of the capitalization of contents, knowledge and know-how of each of these environments is necessary. How can the exchange and reuse of pedagogical resources be guaranteed between these different learning environ-ments? otherwise-said how to guarantee the interoperability of these resources? In order to contribute to the creation of an pedagogical heritage, we propose to design a case-based system allowing the author, when creating a course in a particular context and environment, to exploit the resources that are already available. The goal is to put in place an intelligent production system based on case-based reasoning. It is based on four phases ranging from indexing to reuse, through the similarity measurement and the evaluation. &#x0D; In the first part, we will detail the evolution of learning environments. In the second part, we will review the existing course production platforms, their prin-ciples and their challenges. In the third part, we will present case-based reasoning systems, and then we will introduce our target system.

HIV in Canada-Surveillance Report, 2017.

Human immunodeficiency virus (HIV) is a global public health issue with an estimated 1.8 million people newly infected in 2017. To provide a descriptive overview of reported cases of HIV in Canada by geographic location, sex, age group, exposure category and race/ethnicity, from 1985-2017, with a focus on the most recent data. The Public Health Agency of Canada (PHAC) monitors HIV through the national HIV/AIDS Surveillance System, which is a passive, case-based system that collates non-nominal data voluntarily submitted and validated by all Canadian provinces and territories. Additional data sources presented here include data on immigration-related medical screening for HIV by Immigration, Refugees and Citizenship Canada and data on infants perinatally-exposed to HIV submitted by the Canadian Perinatal HIV Surveillance Program. Data were collated, tables and figures were prepared and descriptive statistics were applied by PHAC and validated by each province and territory. A total of 2,402 new HIV diagnoses were reported in 2017 in Canada; an increase of 3% compared with 2016 and an increase of 17.1% since 2014. The national diagnosis rate increased slightly, from 6.4 per 100,000 population in 2016 to 6.5 per 100,000 population in 2017. In 2017, while Ontario continued to account for the highest number (n=935) and proportion (38.9%) of reported HIV cases, Saskatchewan reported the highest provincial diagnosis rate (15.5 per 100,000 population). In 2017, the diagnostic rate for males at 9.9 per 100,000 population was higher than for females at 3.2 per 100,000 population. As in 2016, the 30-39 year age group had the highest HIV diagnosis rate at 14.8 per 100,000 population. The "gay, bisexual and other men who have sex with men" exposure category continued to represent almost half (46.4%) of all reported HIV cases in adults. In 2017, the absolute number of HIV-positive migrants entering Canada increased to a total number of 835 migrants. One mother-to-child HIV transmission was confirmed in a mother who did not receive any perinatal antiretroviral therapy and two transmissions were confirmed in mothers who did receive perinatal antiretroviral therapy. Similar to the annual changes that have been reported since 2014, the number and rate of reported HIV cases in Canada in 2017 increased slightly compared with 2016. Additional data and analysis are needed to determine the extent to which these findings reflect an increase in HIV transmission, an increase in HIV testing, changes in reporting practices and an increase in the number of HIV-positive people migrating to Canada.

Smart Adaptive Big Data Analysis with Advanced Deep Learning

Abstract Increasing volumes of data, referred as big data, require massive scale and complex computing. Artificial intelligence, deep learning, internet of things and cloud computing are proposed for heterogeneous datasets in hierarchical analytics to manage with the volume, variety, velocity and value of the big data. These solutions are not sufficient in technical systems where measurements, waveform signals, spectral data, images and sparse performance indicators require specific methods for the feature extraction before interactions can be properly analysed. In practical applications, the data analysis, knowledge-based methodologies and optimization need to be combined. The solutions require compact calculation units which can be adaptively modified. The artificial intelligence is extended with various methodologies of computational intelligence. The advanced deep learning approach proposed in this paper uses generalized norms in feature generation, nonlinear scaling in developing compact indicators and linear interactions in model-based systems. The intelligent temporal analysis is available for all indices, including for stress, condition and quality indicators. The service and automation solutions combine these data-driven solutions with the domain expertise by using fuzzy logic for case-based systems. The applications are developed gradually in connections, conversion, cyber, cognition and configuration layers. The advanced methodology is based on the integration of features, scaling functions and interaction models specified by parameters. All the sub-systems and different combinations of them can be recursively updated and optimized with evolutionary computing. The systems adapt to the changing operating conditions and provide situation awareness for the risk analysis. The approach supports different levels of the smart adaptive systems.