Automatic detection of procedural knowledge in robotic-assisted surgical texts

Purpose The automatic extraction of knowledge about intervention execution from surgical manuals would be of the utmost importance to develop expert surgical systems and assistants. In this work we assess the feasibility of automatically identifying the sentences of a surgical intervention text containing procedural information, a subtask of the broader goal of extracting intervention workflows from surgical manuals. Methods We frame the problem as a binary classification task. We first introduce a new public dataset of 1958 sentences from robotic surgery texts, manually annotated as procedural or non-procedural. We then apply different classification methods, from classical machine learning algorithms, to more recent neural-network approaches and classification methods exploiting transformers (e.g., BERT, ClinicalBERT). We also analyze the benefits of applying balancing techniques to the dataset. Results The architectures based on neural-networks fed with FastText’s embeddings and the one based on ClinicalBERT outperform all the tested methods, empirically confirming the feasibility of the task. Adopting balancing techniques does not lead to substantial improvements in classification. Conclusion This is the first work experimenting with machine / deep learning algorithms for automatically identifying procedural sentences in surgical texts. It also introduces the first public dataset that can be used for benchmarking different classification methods for the task

Digital support interventions for the self-management of low back pain: a systematic review

Background: Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. Self-management, including components on increased knowledge, monitoring of symptoms, and physical activity, are consistently recommended in clinical guidelines as cost-effective strategies for LBP management and there is increasing interest in the potential role of digital health. Objective: The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes? Methods: Bibliographic databases searched from 2000 to March 2016 included Medline, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER and TRoPHI, Social Science Citation Index, and Science Citation Index. Reference and citation searching was also undertaken. Search strategy combined the following concepts: (1) back pain, (2) digital intervention, and (3) self-management. Only randomized controlled trial (RCT) protocols or completed RCTs involving adults with LBP published in peer-reviewed journals were included. Two reviewers independently screened titles and abstracts, full-text articles, extracted data, and assessed risk of bias using Cochrane risk of bias tool. An independent third reviewer adjudicated on disagreements. Data were synthesized narratively. Results: Of the total 7014 references identified, 11 were included, describing 9 studies: 6 completed RCTs and 3 protocols for future RCTs. The completed RCTs included a total of 2706 participants (range of 114-1343 participants per study) and varied considerably in the nature and delivery of the interventions, the duration/definition of LBP, the outcomes measured, and the effectiveness of the interventions. Participants were generally white, middle aged, and in 5 of 6 RCT reports, the majority were female and most reported educational level as time at college or higher. Only one study reported between-group differences in favor of the digital intervention. There was considerable variation in the extent of reporting the characteristics, components, and theories underpinning each intervention. None of the studies showed evidence of harm. Conclusions: The literature is extremely heterogeneous, making it difficult to understand what might work best, for whom, and in what circumstances. Participants were predominantly female, white, well educated, and middle aged, and thus the wider applicability of digital self-management interventions remains uncertain. No information on cost-effectiveness was reported. The evidence base for interactive digital interventions to support patient self-management of LBP remains weak

Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts

Many of the most commonly explored natural language processing (NLP) information extraction tasks can be thought of as evaluations of declarative knowledge, or fact-based information extraction. Procedural knowledge extraction, i.e., breaking down a described process into a series of steps, has received much less attention, perhaps in part due to the lack of structured datasets that capture the knowledge extraction process from end-to-end. To address this unmet need, we present FlaMB\'e (Flow annotations for Multiverse Biological entities), a collection of expert-curated datasets across a series of complementary tasks that capture procedural knowledge in biomedical texts. This dataset is inspired by the observation that one ubiquitous source of procedural knowledge that is described as unstructured text is within academic papers describing their methodology. The workflows annotated in FlaMB\'e are from texts in the burgeoning field of single cell research, a research area that has become notorious for the number of software tools and complexity of workflows used. Additionally, FlaMB\'e provides, to our knowledge, the largest manually curated named entity recognition (NER) and disambiguation (NED) datasets for tissue/cell type, a fundamental biological entity that is critical for knowledge extraction in the biomedical research domain. Beyond providing a valuable dataset to enable further development of NLP models for procedural knowledge extraction, automating the process of workflow mining also has important implications for advancing reproducibility in biomedical research.Comment: Submitted to NeurIPS 2023 Datasets and Benchmarks Trac

A Semantic Web of Know-How: Linked Data for Community-Centric Tasks

This paper proposes a novel framework for representing community know-how on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.Comment: 6th International Workshop on Web Intelligence & Communities (WIC14), Proceedings of the companion publication of the 23rd International Conference on World Wide Web (WWW 2014

Impact of workplace based assessment on doctors’ education and performance: a systematic review

Objective To investigate the literature for evidence that workplace based assessment affects doctors’ education and performance

Effect of simulation on cognitive load in health care professionals and students : protocol for a systematic review and meta-analysis

Objective: The objective of this review is to assess the effect of simulation activities and their design features on cognitive load in health care professionals and students. Introduction: Simulation activities are now widely implemented in health care professionals’ education. However, the mechanisms by which simulations and their design features lead to health care professionals’ and students’ learning remains unclear. Still, because of their high interactivity and complexity, simulation activities have the potential to impact the cognitive load of learners. Synthesizing evidence regarding this phenomenon could help simulation educators identify the design features that affect learners’ cognitive load, and explain why some simulation activities are more effective than others. Inclusion criteria: This review will consider experimental and quasi-experimental studies in which the effect of a simulation activity on cognitive load in health care professionals or students from any discipline or level of practice is evaluated. All academic and health settings will be included. Methods: Following the guidelines of the JBI methods for systematic reviews of effectiveness, CINAHL, Embase, ERIC, MEDLINE, PsycINFO, and Web of Science will be searched for studies published in English or French, without a date limit. Retrieved studies will be independently screened for inclusion, then critically appraised for methodological quality by two reviewers using standardized JBI tools. Data extraction will be done independently using adapted tools from JBI. Where possible, data will be pooled using meta-analytical methods

Permanent health education in the context of obesity: a scoping review

OBJETIVE: To map the international literature on Permanent Health Education initiatives to care for people with obesity. METHODS: In total, six databases were searched without any language or publication period restriction according to the Joana Briggs Institute manual for evidence synthesis and the Prisma extension for scoping reviews (Prisma-ScR). Articles were independently analyzed by four reviewers and data, by two authors, which were then analyzed and discussed with our research team. RESULTS: After screening 8,780 titles/abstracts and 26 full texts, 10studies met our eligibility criteria. We extracted data on methodologies, themes, definitions of obesity, outcomes, and gaps. Most initiatives came from North American countries without free or universal health systems and lasted a short period of time (70%), had multidisciplinary teams (70%), and addressed sub-themes on obesity approaches (90%). Results included changes in participants’ understanding, attitude, and procedures (80%) and gaps which pointed to the sustainability of these changes (80%). CONCLUSION: This review shows the scarce research in the area and a general design of poorly effective initiatives, with traditional teaching methodologies based on information transmission techniques, the understanding of obesity as a disease and a public health problem, punctual actions, disciplinary fragmentation alien to the daily work centrality, and failure to recognize problems and territory as knowledge triggers and to focus on health care networks, line of care, the integrality of care, and food and body cultures

MLA Research Agenda. Systematic Review Project. Team Updates Presentation. MLA Annual Meeting Supplement. May 17, 2015

Most of the 15 systematic review teams provided one-page summaries of their progress to date in compiling systematic reviews on one of 15 top-ranked important research projects. This builds upon an earlier Delphi study that was reported here: Eldredge JD, Ascher MT, Holmes HN, Harris MR. The new Medical Library Association research agenda: final results from a three-phase Delphi study. J Med Libr Assoc. 2012 Jul;100(3):214-8. doi: 10.3163/1536-5050.100.3.012. PubMed PMID: 22879811; PubMed Central PMCID: PMC3411260