Ontology for assessment studies of human–computer-interaction in surgery

Machno, Andrej; Jannin, Pierre; Dameron, Olivier; Korb, Werner; Scheuermann, Gerik; Meixensberger, Jürgen

doi:10.1016/j.artmed.2014.12.011

Cited by 10 publications

(3 citation statements)

References 17 publications

(24 reference statements)

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“…This type of deliberate disruption of built up habits should be considered where execution of habitual selection may lead to irreversible, and potentially dangerous outcomes (Norman, 1983). In safety critical circumstances such as medical surgery (Machno et al, 2015) or transport operation (Walker & Strathie, 2016), performing a habitual, often-practiced response in an interface without considering the consequences could be catastrophic. Disrupting these habits through micro boundaries may be necessary, even if it prevents users from progressing quickly.…”

Section: 15mentioning

confidence: 99%

Quantifying the impact of making and breaking interface habits

Garaialde

Bowers

Pinder

et al. 2020

International Journal of Human-Computer Studies

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Section: 15mentioning

confidence: 99%

Quantifying the impact of making and breaking interface habits

Garaialde

Bowers

Pinder

et al. 2020

International Journal of Human-Computer Studies

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“…The standard method for workflow modeling is based on the results of the interviews with domain experts and the modeler's experience [1]. Ontologies are currently under extensive research for surgical workflow modeling, where they aim at providing the procedural knowledge for evaluating the surgical skills or planning the surgery [2] and in some advanced application such as contextawareness [3], robotics [4] and so on. An ontology consists of the domain concepts, properties, and relations, generally "part-of" and "is-a" relationship, and provides a common set of concepts that could help to solve the heterogeneity and complexity of surgical information.…”

Section: Introductionmentioning

confidence: 99%

Inductive Learning of the Surgical Workflow Model through Video Annotations

Nakawala

Momi

Pescatori

et al. 2017

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

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Surgical workflow modeling is becoming increasingly useful to train surgical residents for complex surgical procedures. Rule-based surgical workflows have shown to be useful to create context-aware systems. However, manually constructing production rules is a time-intensive and laborious task. With the expansion of new technologies, large video archive can be created and annotated exploiting and storing the expert's knowledge. This paper presents a prototypical study of automatic generation of production rules, in the Horn-clause, using the First Order Inductive Learner (FOIL) algorithm applied to annotated surgical videos of Thoracentesis procedure and of its feasibility to use in context-aware system framework. The algorithm was able to learn 18 rules for surgical workflow model with 0.88 precision, and 0.94 F1 score on the standard video annotation data, representing entities of the surgical workflow, which was used to retrieve contextual information on Thoracentesis workflow for its application to surgical training.

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“…The developed strategy for harmonizing imaging data and anatomic metadata using knowledge representation and the resulted web-based service allows radiologists to explore both images and anatomic rela-Efficient Results in Semantic Interoperability for Health Care tions. In the context of surgery and human computer interaction in clinical practice, and to cope with unwanted consequences thanks to an investigation model, Machno et al [10] showed the several advantages of using an ontology as information model instead of a traditional database. Several recent works are related to the extension of existing ontologies.…”

Section: Introductionmentioning

confidence: 99%

Efficient Results in Semantic Interoperability for Health Care

Soualmia

Charlet

2016

Yearb Med Inform

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SummaryObjectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic

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Ontology for assessment studies of human–computer-interaction in surgery

Cited by 10 publications

References 17 publications

Quantifying the impact of making and breaking interface habits

Quantifying the impact of making and breaking interface habits

Inductive Learning of the Surgical Workflow Model through Video Annotations

Efficient Results in Semantic Interoperability for Health Care

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