A sentence classification framework to identify geometric errors in radiation therapy from relevant literature

The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors

Closing the Affordable Housing Gap: Identifying the Barriers Hindering the Sustainable Design and Construction of Affordable Homes

Despite the commitment of the United Nations (UN) to provide everyone with equal access to basic services, the construction sector still fails to reach the production capacity and quality standards which are needed to meet the fast-growing demand for affordable homes. Whilst innovation measures are urgently needed to address the existing inefficiencies, the identification and development of the most appropriate solutions require a comprehensive understanding of the barriers obstructing the design and construction phase of affordable housing. To identify such barriers, an exploratory data mining analysis was conducted in which agglomerative hierarchical clustering made it possible to gather latent knowledge from 3566 text-based research outputs sourced from the Web of Science and Scopus. The analysis captured 83 supply-side barriers which impact the efficiency of the value chain for affordable housing provision. Of these barriers, 18 affected the design and construction phase, and after grouping them by thematic area, seven key matters of concern were identified: (1) design (not) for all, (2) homogeneity of provision, (3) unhealthy living environment, (4) inadequate construction project management, (5) environmental unsustainability, (6) placemaking, and (7) inadequate technical knowledge and skillsets. The insights which resulted from the analysis were seen to support evidence-informed decision making across the affordable housing sector. The findings suggest that fixing the inefficiencies of the affordable housing provision system will require UN Member States to accelerate the transition towards a fully sustainable design and construction process. This transition should prioritize a more inclusive and socially sensitive approach to the design and construction of affordable homes, capitalizing on the benefits of greater user involvement. In addition, transformative actions which seek to deliver more resource-efficient and environmentally friendly homes should be promoted, as well as new investments in the training and upskilling of construction professionals