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

Evaluation of a tool for Java structural specification checking

Although a number of tools for evaluating Java code functionality and style exist, little work has been done in a distance learning context on automated marking of Java programs with respect to structural specifications. Such automated checks support human markers in assessing students’ work and evaluating their own marking; online automated marking; students checking code before submitting it for marking; and question setters evaluating the completeness of questions set. This project developed and evaluated a prototype tool that performs an automated check of a Java program’s correctness with respect to a structural specification. Questionnaires and interviews were used to gather feedback on the usefulness of the tool as a marking aid to humans, and on its potential usefulness to students for self-assessment when working on their assignments. Markers were asked to compare the usefulness of structural specification testing as compared to other kinds of support, including syntax error assistance, style checking and functionality testing. Initial results suggest that most markers using the structural specification checking tool found it to be useful, and some reported that it increased their accuracy in marking. Reasons for not using the tool included lack of time and the simplicity of the assignment it was trialled on. Some reservations were expressed about reliance on tools for assessment, both for markers and for students. The need for advice on incorporating tools in marking workflow is suggested

Model-based spacecraft and mission design for the evaluation of technology

In order to meet the future vision of robotic missions, engineers will face intricate mission concepts, new operational approaches, and technologies that have yet to be developed. The concept of smaller, model driven projects helps this transition by including life-cycle cost as part of the decision making process. For example, since planetary exploration missions have cost ceilings and short development periods, heritage flight hardware is utilized. However, conceptual designs that rely solely on heritage technology will result in estimates that may not be truly representative of the actual mission being designed and built. The Laboratory for Spacecraft and Mission Design (LSMD) at the California Institute of Technology is developing integrated concurrent models for mass and cost estimations. The purpose of this project is to quantify the infusion of specific technologies where the data would be useful in guiding technology developments leading up to a mission. This paper introduces the design-to-cost model to determine the implications of various technologies on the spacecraft system in a collaborative engineering environment. In addition, comparisons of the benefits of new or advanced technologies for future deep space missions are examined

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works