Linked Data approach for selection process automation in Systematic Reviews

Background: a systematic review identifies, evaluates and synthesizes the available literature on a given topic using scientific and repeatable methodologies. The significant workload required and the subjectivity bias could affect results. Aim: semi-automate the selection process to reduce the amount of manual work needed and the consequent subjectivity bias. Method: extend and enrich the selection of primary studies using the existing technologies in the field of Linked Data and text mining. We define formally the selection process and we also develop a prototype that implements it. Finally, we conduct a case study that simulates the selection process of a systematic literature published in literature. Results: the process presented in this paper could reduce the work load of 20% with respect to the work load needed in the fully manually selection, with a recall of 100%. Conclusions: the extraction of knowledge from scientific studies through Linked Data and text mining techniques could be used in the selection phase of the systematic review process to reduce the work load and subjectivity bia

Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers

BACKGROUND: Citation screening is time consuming and inefficient. We sought to evaluate the performance of Abstrackr, a semi-automated online tool for predictive title and abstract screening. METHODS: Four systematic reviews (aHUS, dietary fibre, ECHO, rituximab) were used to evaluate Abstrackr. Citations from electronic searches of biomedical databases were imported into Abstrackr, and titles and abstracts were screened and included or excluded according to the entry criteria. This process was continued until Abstrackr predicted and classified the remaining unscreened citations as relevant or irrelevant. These classification predictions were checked for accuracy against the original review decisions. Sensitivity analyses were performed to assess the effects of including case reports in the aHUS dataset whilst screening and the effects of using larger imbalanced datasets with the ECHO dataset. The performance of Abstrackr was calculated according to the number of relevant studies missed, the workload saving, the false negative rate, and the precision of the algorithm to correctly predict relevant studies for inclusion, i.e. further full text inspection. RESULTS: Of the unscreened citations, Abstrackr’s prediction algorithm correctly identified all relevant citations for the rituximab and dietary fibre reviews. However, one relevant citation in both the aHUS and ECHO reviews was incorrectly predicted as not relevant. The workload saving achieved with Abstrackr varied depending on the complexity and size of the reviews (9 % rituximab, 40 % dietary fibre, 67 % aHUS, and 57 % ECHO). The proportion of citations predicted as relevant, and therefore, warranting further full text inspection (i.e. the precision of the prediction) ranged from 16 % (aHUS) to 45 % (rituximab) and was affected by the complexity of the reviews. The false negative rate ranged from 2.4 to 21.7 %. Sensitivity analysis performed on the aHUS dataset increased the precision from 16 to 25 % and increased the workload saving by 10 % but increased the number of relevant studies missed. Sensitivity analysis performed with the larger ECHO dataset increased the workload saving (80 %) but reduced the precision (6.8 %) and increased the number of missed citations. CONCLUSIONS: Semi-automated title and abstract screening with Abstrackr has the potential to save time and reduce research waste

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

Towards a user oriented analytical approach to learning design

The London Pedagogy Planner (LPP) is a prototype for a collaborative online planning and design tool that supports lecturers in developing, analysing and sharing learning designs. The tool is based on a developing model of the components involved in learning design and the critical relationships between them. As a decision tool it makes the pedagogical design explicit as an output from the process, capturing it for testing, redesign, reuse and adaptation by the originator, or by others. The aim is to test the extent to which we can engage lecturers in reflecting on learning design, and make them part of the educational community that discovers how best to use technology‐enhanced learning. This paper describes the development of LPP, presents pedagogical benefits of visual representations of learning designs and proposes an analytical approach to learning design based on these visual representations. The analytical approach is illustrated based on an initial evaluation with a small group of lecturers from two partner institutions

Safety-Critical Systems and Agile Development: A Mapping Study

In the last decades, agile methods had a huge impact on how software is developed. In many cases, this has led to significant benefits, such as quality and speed of software deliveries to customers. However, safety-critical systems have widely been dismissed from benefiting from agile methods. Products that include safety critical aspects are therefore faced with a situation in which the development of safety-critical parts can significantly limit the potential speed-up through agile methods, for the full product, but also in the non-safety critical parts. For such products, the ability to develop safety-critical software in an agile way will generate a competitive advantage. In order to enable future research in this important area, we present in this paper a mapping of the current state of practice based on {a mixed method approach}. Starting from a workshop with experts from six large Swedish product development companies we develop a lens for our analysis. We then present a systematic mapping study on safety-critical systems and agile development through this lens in order to map potential benefits, challenges, and solution candidates for guiding future research.Comment: Accepted at Euromicro Conf. on Software Engineering and Advanced Applications 2018, Prague, Czech Republi

The relationship between GPs and hospital consultants and the implications for patient care : a qualitative study

Acknowledgements Clinicians assisting development of topic guide, all based in NHS Highland; Mr Angus Cain (Consultant Ear, Nose & Throat), Professor Steve Leslie (Consultant Cardiologist), Professor Ronald Macvicar (Postgraduate Dean, North of Scotland Region of NHS Education for Scotland (NES)), Dr Jerry O’Rourke (General Practice Principal), Professor Ken Walker (Consultant Colorectal surgeon). Clinicians involved in pilot of the semi-structured questionnaire; Dr Beth Macfarlane (General Practice Principal), and Dr Russell Drummond (Consultant Endocrinologist). Gillian Heron, Cairn Medical Practice who transcribed interview recordings. Funding The research was funded by both the local NHS Highland Research & Development Committee, and the “RCGP Allen & Margaret Wilson Memorial Fund.” The Chief Investigator (Dr Rod Sampson) received no personal payment for the study. No drug company is involved in this research.Peer reviewedPublisher PD

Toward a user-oriented analytical approach to learning design

The London Pedagogy Planner (LPP) is a prototype for a collaborative online planning and design tool that supports lecturers in developing, analysing and sharing learning designs. The tool is based on a developing model of the components involved in learning design, and the critical relationships between them. As a decision tool, it makes the pedagogical design explicit as an output from the process, capturing it for testing, redesign, reuse and adaptation by the originator, or by others. The aim is to test the extent to which we can engage lecturers in reflecting on learning design, and make them part of the educational community that discovers how best to use Technology Enhanced Learning (TEL). This paper describes the development of LPP, presents pedagogical benefits of visual representations of learning designs, and proposes an analytical approach to learning design based on these visual representations. The analytical approach is illustrated based on an initial evaluation with the lecturers

Qualitative evaluation of the Safety and Improvement in Primary Care (SIPC) pilot collaborative in Scotland: perceptions and experiences of participating care teams

Objectives: To explore general practitioner (GP) team perceptions and experiences of participating in a large-scale safety and improvement pilot programme to develop and test a range of interventions that were largely new to this setting. Design: Qualitative study using semistructured interviews. Data were analysed thematically. Subjects and setting: Purposive sample of multiprofessional study participants from 11 GP teams based in 3 Scottish National Health Service (NHS) Boards. Results: 27 participants were interviewed. 3 themes were generated: (1) programme experiences and benefits, for example, a majority of participants referred to gaining new theoretical and experiential safety knowledge (such as how unreliable evidence-based care can be) and skills (such as how to search electronic records for undetected risks) related to the programme interventions; (2) improvements to patient care systems, for example, improvements in care systems reliability using care bundles were reported by many, but this was an evolving process strongly dependent on closer working arrangements between clinical and administrative staff; (3) the utility of the programme improvement interventions, for example, mixed views and experiences of participating in the safety climate survey and meeting to reflect on the feedback report provided were apparent. Initial theories on the utilisation and potential impact of some interventions were refined based on evidence. Conclusions: The pilot was positively received with many practices reporting improvements in safety systems, team working and communications with colleagues and patients. Barriers and facilitators were identified related to how interventions were used as the programme evolved, while other challenges around spreading implementation beyond this pilot were highlighted