An ebd-enabled design knowledge acquisition framework

Having enough knowledge and keeping it up to date enables designers to execute the design assignment effectively and gives them a competitive advantage in the design profession. Knowledge elicitation or acquisition is a crucial component of system design, particularly for tasks requiring transdisciplinary or multidisciplinary cooperation. In system design, extracting domain-specific information is exceedingly tricky for designers. This thesis presents three works that attempt to bridge the gap between designers and domain expertise. First, a systematic literature review on data-driven demand elicitation is given using the Environment-based Design (EBD) approach. This review address two research objectives: (i) to investigate the present state of computer-aided requirement knowledge elicitation in the domains of engineering; (ii) to integrate EBD methodology into the conventional literature review framework by providing a well-structured research question generation methodology. The second study describes a data-driven interview transcript analysis strategy that employs EBD environment analysis, unsupervised machine learning, and a range of natural language processing (NLP) approaches to assist designers and qualitative researchers in extracting needs when domain expertise is lacking. The second research proposes a transfer-learning method-based qualitative text analysis framework that aids researchers in extracting valuable knowledge from interview data for healthcare promotion decision-making. The third work is an EBD-enabled design lexical knowledge acquisition framework that automatically constructs a semantic network -- RomNet from an extensive collection of abstracts from engineering publications. Applying RomNet can improve the design information retrieval quality and communication between each party involved in a design project. To conclude, this thesis integrates artificial intelligence techniques, such as Natural Language Processing (NLP) methods, Machine Learning techniques, and rule-based systems to build a knowledge acquisition framework that supports manual, semi-automatic, and automatic extraction of design knowledge from different types of the textual data source

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study

Abstract Background Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. Methods This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision–recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. Results There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90). Conclusions Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.Highlights The incidence rates of surgical site infections following total hip and knee arthroplasty were 0.5 and 0.52 per 100 surgical procedures. The incidence of SSIs varied significantly between care facilities (ranging from 0.53 to 1.71 per 100 procedures). The optimal machine learning model achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90)

Health Data Governance for Research Use in Alberta

Alberta has rich clinical and health services data held under the custodianship of Alberta Health and Alberta Health Services (AHS), which is not only used for clinical and administrative purposes but also disease surveillance and epidemiological research. Alberta is the largest province in Canada with a single payer centralised health system, AHS, and a consolidated data and analytics team supporting researchers across the province. This paper describes Alberta's data custodians, data governance mechanisms, and streamlined processes followed for research data access. AHS has created a centralised data repository from multiple sources, including practitioner claims data, hospital discharge data, and medications dispensed, available for research use through the provincial Data and Research Services (DRS) team. The DRS team is integrated within AHS to support researchers across the province with their data extraction and linkage requests. Furthermore, streamlined processes have been established, including: 1) ethics approval from a research ethics board, 2) any necessary operational approvals from AHS, and 3) a tripartite legal agreement dictating terms and conditions for data use, disclosure, and retention. This allows researchers to gain timely access to data. To meet the evolving and ever-expanding big-data needs, the University of Calgary, in partnership with AHS, has built high-performance computing (HPC) infrastructure to facilitate storage and processing of large datasets. When releasing data to researchers, the analytics team ensures that Alberta's Health Information Act's guiding principles are followed. The principal investigator also ensures data retention and disposition are according to the plan specified in ethics and per the terms set out by funding agencies. Even though there are disparities and variations in the data protection laws across the different provinces in Canada, the streamlined processes for research data access in Alberta are highly efficient

Unveiling 2,000 years of differentiation among Tungusic-speaking populations: a revised phylogeny of the paternal founder lineage C2a-M48-SK1061

Previous studies demonstrated Y chromosome haplogroup C2a-M48-SK1061 is the only founding paternal lineage of all Tungusic-speaking populations. To infer the differentiation history of these populations, we studied more sequences and constructed downstream structure of haplogroup C2a-M48-SK1061 with better resolution. In this study, we generated 100 new sequences and co-analyzed 140 sequences of C2a-M48-SK1061 to reconstruct a highly revised phylogenetic tree with age estimates. We also performed the analysis of the geographical distribution and spatial autocorrelation of sub-branches. Dozens of new sub-branches were discovered, many sub-branches were nearly unique for Ewenki, Evens, Oroqen, Xibe, Manchu, Daur, and Mongolian. The topology of these unique sub-branches is the key evidence for understanding the complex evolutionary relationship between different Tungusic-speaking populations. The revised phylogeny provided a clear pattern for the differentiation history of haplogroup C2a-M48-SK1061 in the past 2,000 years. This study showed that the divergence pattern of founder lineage is essential to understanding the differentiation history of populations

Обоснование методологии аудита операций с основными средствами

The determining factor in the organization of the enterprise's accounting records is the verification of the correctness of the valuation of fixed assets. An integral part of the general audit of the enterprise is the implementation of an audit of fixed assets, since this affects the financial performance of business entities. When conducting an audit of fixed assets, the auditor should be guided in his activity by information sources and methods that enable him to obtain information about the reliability of accounting for transactions with fixed assets in the enterprise.Определяющим фактором организации бухгалтерского учета предприятия определена проверка правильности оценки основных средств. Неотъемлемой частью общего аудита предприятия является осуществление аудиторской проверки основных средств, поскольку это влияет на финансовые результаты деятельности субъектов хозяйствования. При проведении аудита основных средств, аудитор должен руководствоваться в своей деятельности информационными источниками и методами, которые дают ему возможность получить информацию о достоверности отражения в учете и отчетности операций с основными средствами на предприятии

Обоснование методологии аудита операций с основными средствами

The determining factor in the organization of the enterprise's accounting records is the verification of the correctness of the valuation of fixed assets. An integral part of the general audit of the enterprise is the implementation of an audit of fixed assets, since this affects the financial performance of business entities. When conducting an audit of fixed assets, the auditor should be guided in his activity by information sources and methods that enable him to obtain information about the reliability of accounting for transactions with fixed assets in the enterprise.Определяющим фактором организации бухгалтерского учета предприятия определена проверка правильности оценки основных средств. Неотъемлемой частью общего аудита предприятия является осуществление аудиторской проверки основных средств, поскольку это влияет на финансовые результаты деятельности субъектов хозяйствования. При проведении аудита основных средств, аудитор должен руководствоваться в своей деятельности информационными источниками и методами, которые дают ему возможность получить информацию о достоверности отражения в учете и отчетности операций с основными средствами на предприятии