The VERICLIG Project: Extraction of Computer Interpretable Guidelines via Syntactic and Semantic Annotation (original) (raw)
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How can information extraction ease formalizing treatment processes in clinical practice guidelines?
Artificial Intelligence in Medicine, 2007
Objective. Formalizing clinical practice guidelines for a subsequent computer-supported processing is a challenging, but burdensome and time-consuming task. Existing methods and tools to support this task demand detailed medical knowledge, knowledge about the formal representations, and a manual modeling. Furthermore, formalized guideline documents mostly fall far short in terms of readability and understandability for the human domain modeler. Methods and Material. We propose a new multi-step approach using information extraction methods to support the human modeler by both automating parts of the modeling process and making the modeling process traceable and comprehensible. This paper addresses the first steps to obtain a representation containing processes which is independent of the final guideline representation language. Results. We have developed and evaluated several heuristics without the need to apply Natural Language Understanding and implemented them in a framework to apply them to several guidelines from the medical subject of otolaryngology. Findings in the evaluation indicate that using semi-automatic, step-wise information extraction methods are a valuable Preprint submitted to Elsevier Science instrument to formalize CPGs. Conclusions. Our evaluation shows that a heuristic-based approach can achieve good results, especially for guidelines with a major portion of semi-structured text. It can be applied to guidelines irrespective to the final guideline representation format.
Supporting the Abstraction of Clinical Practice Guidelines Using Information Extraction
Lecture Notes in Computer Science, 2010
Modelling clinical practice guidelines in a computer-interpretable format is a challenging and complex task. The modelling process involves both medical experts and computer scientists, who have to interact and communicate together. In order to support both modeller groups we propose to provide them with helpful information automatically generated using NLP methods. We identify this information using rules based on both syntactic and semantic information. The majority of the defined information extraction rules are based on semantic relationships derived from the UMLS Semantic Network. Findings in the evaluation indicate that using rules based on semantic and syntactic information provide valuable and helpful results.
Representation of Medical Guidelines with a Computer Interpretable Model
International Journal on Artificial Intelligence Tools, 2014
Nowadays medical software is tightly coupled with medical devices that perform patient state monitoring and lately even some basic treatment procedures. Medical guidelines (GLs) can be seen as specification of a medical system which requires their computerinterpretable representation of medical GLs. Until now most of the medical GLs are often represented in a textual format and therefore often suffer from such structural problems as incompleteness, inconsistencies, ambiguity and redundancy, which makes the translation process to the machine-interpretable language more complicated. Computer-based interpretation of GLs can improve the quality of protocols as well as the quality of medical service. Several GLs formal representation methods have been presented recently. Only some of them enable automatic formal verification by introducing an additional translation path to the existing model checking environments. However, if a verified property fails it is difficult to trace back the result needed to change the model. Moreover, these formalisms provide the notion of time mostly in terms of actions order. In this paper we preset the application of a well-know formal behaviour representation approach of embedded systems design domain to medical GLs interpretation. We use Timed Automata extended with Tasks (TAT) and TIMES toolbox to represent medical GLs as a system behaviour in a computer interpretable form. We discuss the verification issues with the help of the anticancer drug imatinib case study.
International Journal of …, 2002
Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation and evaluation. We studied eleven types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow.
Gaining Process Information from Clinical Practice Guidelines Using Information Extraction
Lecture Notes in Computer Science, 2005
Formalizing Clinical Practice Guidelines for a subsequent computer-supported processing is a cumbersome, challenging, and time-consuming task. But currently available tools and methods do not satisfactorily support this task. We propose a new multi-step approach using Information Extraction and Transformation. This paper addresses the Information Extraction task. We have developed several heuristics, which do not take Natural Language Understanding into account. We implemented our heuristics in a framework to apply them to several guidelines from the specialty of otolaryngology. Our evaluation shows that a heuristic-based approach can achieve good results, especially for guidelines with a major portion of semi-structured text.
A modular approach for representing and executing clinical guidelines
Artificial Intelligence in Medicine, 2001
In this paper, we propose an approach for managing clinical guidelines. We outline a modular architecture, allowing us to separate two conceptually distinct aspects: the representation (and acquisition) of clinical guidelines and their execution. We propose an expressive formalism, which allows one to deal with the context-dependent character of clinical guidelines and also takes into account different temporal aspects. We also describe our tool for acquiring clinical guidelines, which provides a user-friendly interface to physicians, and automatically detects many forms of syntactic and semantic inconsistencies in the guidelines being acquired. In the second part of the paper, we describe a¯exible engine for executing clinical guidelines (e.g. for clinical decision support applications, for medical education, or for integrating guidelines into the clinical practice), focusing our attention on temporal issues.
International Journal of Medical Informatics, 2002
Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation and evaluation. We studied eleven types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow.
Studies in health technology and informatics, 2004
Guideline and protocol representation languages have reached a level of complexity where auxiliary methods are needed to support the authoring of protocols in the particular language. Several approaches and methods exist that claim high knowledge about both, the medical context and the formal requirements. Therefore, we need knowledge-based methods to facilitate the human plan designer and create the protocols of the particular language as automated as possible. We present a three-step wrapper method, called TimeWrap, to extract information, in particular temporal issues, out of semistructured data and integrate it in a formal representation. We illustrate our approach using the guideline-representation language Asbru and examples from guidelines to treat conjunctivitis.
Artificial Intelligence in Medicine, 2009
Objective-Clinical practice guidelines (CPGs) are means to provide evidence-based medical knowledge. In order to make up-to-date "best" scientific evidence available these documents need to be updated on an ongoing basis. An effective method to accomplish this aim is offered by the so-called "living guidelines": Living guidelines are documents presenting up-to-date and state-ofthe-art knowledge to practitioners. To have guidelines implemented by computer-support they have to be formalized in a computer-interpretable form in a first step. Due to the complexity of such formats the formalization process is burdensome and time-consuming. Automating parts of the modeling process and, consequently, modeling updates of these guideline documents are demanded. Methods and material-The LASSIE methodology supports this task by formalizing guidelines in several steps from the textual form to the guideline representation language Asbru using a document-centric approach. LASSIE uses information extraction techniques to semiautomatically accomplish these steps. We apply LASSIE to support the implementation of living guidelines. Results-Based on a living guideline published by the Scottish Intercollegiate Guidelines Network (SIGN) we show that adaptations of previously formalized guidelines can be accomplished easily and fast. Thereby, the different versions of guideline documents are compared and updates are identified. Due to the traceable formalization method of linking text parts and their corresponding formal models, we are able to inherit unchanged models from previously formalized versions. Thus, we only need to formalize updated text parts using the semiautomatic formalization method LASSIE. Conclusion-We propose a simple, time-saving, but effective method called LASSIE to formalize new guideline versions of previously formalized CPGs. Furthermore, models that have been added or modified by knowledge engineers in previous versions can also be transferred easily. This will result in a faster implementation of new guideline versions also known as living guidelines to provide up-to-date knowledge necessary for accomplishing the daily work of health care professionals. ☆ This is an extended and revised version of K. Kaiser and S. Miksch. Formalizing 'Living Guidelines' using LASSIE: A Multi-step Information Extraction Method,