Moving towards a new paradigm of creation, dissemination, and application of computer-interpretable medical knowledge

Nováis, Paulo; Oliveira, Tiago; Neves, José

doi:10.1007/s13748-016-0084-2

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…In this way, it will be possible for users to dynamically feed new cases to the prediction system and make it change in order to provide better survival predictions. This type of model could also prove to be very useful when integrated in computer-interpretable guideline systems, such as the one described in (Carneiro et al, 2008;Costa et al, 2011;Lima et al, 2011;Oliveira et al, 2013;Oliveira et al, 2014;Novais et al, 2016), as a way to provide dynamic knowledge to rule-based decision support. Future work also includes the development of conditional survivability models that allow the user to get a prediction knowing that the patient has already survived a number of years after diagnosis and treatment.…”

Section: Discussionmentioning

confidence: 99%

Treating Colon Cancer Survivability Prediction as a Classification Problem

Silva

Oliveira²,

Neves

et al. 2016

ADCAIJ

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Colon cancer; prediction; machine learningThis work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the six-feature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance.

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Section: Discussionmentioning

confidence: 99%

Treating Colon Cancer Survivability Prediction as a Classification Problem

Silva

Oliveira²,

Neves

et al. 2016

ADCAIJ

Self Cite

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“…The CIGs encoded in the CompGuide Editor, using the CompGuide ontology, are made available in a Git Repository. The knowledge stored there is used in the CompGuide CIG Framework, as described in Novais, Oliveira, and Neves , where a server application contains methods to process and run these CIGs against patient data. The functionalities of the server are made available through a set of web services, thus conveying the idea of guideline as a service .…”

Section: Compguide Editor For the Management Of Guidelinesmentioning

confidence: 99%

OWL‐based acquisition and editing of computer‐interpretable guidelines with the CompGuide editor

et al. 2018

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Computer‐Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence‐based nature of their source material. Therefore, these machine‐readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine‐readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.

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“…When developing a CIG model and corresponding execution engine, these are aspects that must be taken into account. They establish the foundations for the development of higher level functions in CIG execution engines [24]. One of such high level functions is the management of uncertainty, which is a pervasive problem in health care.…”

Section: Introductionmentioning

confidence: 99%

The Role of Ontologies and Decision Frameworks in Computer-Interpretable Guideline Execution

Nováis

Oliveira

Satoh

et al. 2017

Advances in Intelligent Systems and Computing

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Computer-Interpretable Guidelines (CIGs) are machine readable representations of Clinical Practice Guidelines (CPGs) that serve as the knowledge base in many knowledge-based systems oriented towards clinical decision support. Herein we disclose a comprehensive CIG representation model based on Web Ontology Language (OWL) along with its main components. Additionally, we present results revealing the expressiveness of the model regarding a selected set of CPGs. The CIG model then serves as the basis of an architecture for an execution system that is able to manage incomplete information regarding the state of a patient through Speculative Computation. The architecture allows for the generation of clinical scenarios when there is missing information for clinical parameters.

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Moving towards a new paradigm of creation, dissemination, and application of computer-interpretable medical knowledge

Cited by 5 publications

References 26 publications

Treating Colon Cancer Survivability Prediction as a Classification Problem

Treating Colon Cancer Survivability Prediction as a Classification Problem

OWL‐based acquisition and editing of computer‐interpretable guidelines with the CompGuide editor

The Role of Ontologies and Decision Frameworks in Computer-Interpretable Guideline Execution

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