Strategies and solutions for housing sustainability : building information files and performance certificates

The overall aim of this research project was to provide a broader range of value propositions (beyond upfront traditional construction costs) that could transform both the demand side and supply side of the housing industry. The project involved gathering information about how building information is created, used and communicated and classifying building information, leading to the formation of an Information Flow Chart and Stakeholder Relationship Map. These were then tested via broad housing industry focus groups and surveys. The project revealed four key relationships that appear to operate in isolation to the whole housing sector and may have significant impact on the sustainability outcomes and life cycle costs of dwellings over their life cycle. It also found that although a lot of information about individual dwellings does already exist, this information is not coordinated or inventoried in any systematic manner and that national building information files of building passports would present value to a wide range of stakeholders.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Automatic ICD-10 classification of cancers from free-text death certificates

Objective Death certificates provide an invaluable source for cancer mortality statistics; however, this value can only be realised if accurate, quantitative data can be extracted from certificates – an aim hampered by both the volume and variable nature of certificates written in natural language. This paper proposes an automatic classification system for identifying cancer related causes of death from death certificates. Methods Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates. These features were used to train Support Vector Machine classifiers (one classifier for each cancer type). The classifiers were deployed in a cascaded architecture: the first level identified the presence of cancer (i.e., binary cancer/nocancer) and the second level identified the type of cancer (according to the ICD-10 classification system). A held-out test set was used to evaluate the effectiveness of the classifiers according to precision, recall and F-measure. In addition, detailed feature analysis was performed to reveal the characteristics of a successful cancer classification model. Results The system was highly effective at identifying cancer as the underlying cause of death (F-measure 0.94). The system was also effective at determining the type of cancer for common cancers (F-measure 0.7). Rare cancers, for which there was little training data, were difficult to classify accurately (F-measure 0.12). Factors influencing performance were the amount of training data and certain ambiguous cancers (e.g., those in the stomach region). The feature analysis revealed a combination of features were important for cancer type classification, with SNOMED CT concept and oncology specific morphology features proving the most valuable. Conclusion The system proposed in this study provides automatic identification and characterisation of cancers from large collections of free-text death certificates. This allows organisations such as Cancer Registries to monitor and report on cancer mortality in a timely and accurate manner. In addition, the methods and findings are generally applicable beyond cancer classification and to other sources of medical text besides death certificates.

Relationship between quality certification and financial and non-financial performance of organizations

At present, many countries have either embraced ISO9001 or used it as the basis of their national quality certification systems. However, few studies have been conducted to examine the benefits companies’ gain from achieving and implementing ISO9001 standards (Chikuku et al. 2012; Psomas et al. 2013; Sampaio et al. 2011a,b). Analysis has brought much more confused and uneven results across the countries. Turning to the experience of Malaysia, this country has witnessed a spectacular growth at an average rate of 9.89% per annum of ISO certificates issued to companies operating within its borders (ISO Survey 2012). While many companies rush to be ISO 9001 certified whether this brings about better benefits (both the financial and the non-financial) is still an open question. In this study, the research problems were first formulated from the literature and then a questionnaire survey was conducted to test the hypotheses. A survey was administered to chief executives officers and managers across manufacturing and service organizations in Malaysia. Multivariate analysis and SPSS macro developed by Preacher and Hayes were used as statistical techniques to the financial and non-financial benefits of ISO9001 certification. The survey instrument was a two-page questionnaire comprising three sections. The first section of the questionnaire covered the company’s profile. The second section consisted of 25 items on internal benefits and third section consisted of 7 items on external benefits measured on 1–5 Likert scale to assess the benefits of ISO9001 certification. Total 201 valid responses were received. Results of the study indicate that there was no significant direct relationship between ISO9001 certification and organizational financial performance, while strong statistical evidence was found to support the direct relationship between ISO9001 certification and non-financial performance. The findings of the study discovered that financial performance is actually directly related to two non-financial measures, namely quality performance and local and international business performance, which are directly and significantly influenced by ISO9001 certification. Therefore non-financial performance measures are involved in the mediational process. The findings will assist practitioners in taking right courses of action that make the implementation of this standard more effective. For example, the study findings study suggests that companies should put emphasize on nonfinancial factors to improve their financial performance.