A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines

Fazlic, Lejla Begic; Hallawa, Ahmed; Schmeink, Anke; Peine, Arne; Märtin, Lukas; Dartmann, Guido

doi:10.23919/mipro.2019.8756929

Cited by 16 publications

(5 citation statements)

References 16 publications

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“…Therefore, specifying guideline recommendations from the start in a machine-readable format holds multiple advantages and we therefore consider it a desirable change in the current practice of high quality, evidence-based guideline recommendation development. An alternative approach to using a machine-readable guideline recommendation specification could be the application of recent advances of natural language processing (NLP) methods to allow the computer to "understand" and process human-readable guideline recommendations [44,45]. However, any errors unknowingly introduced by such an approach (e.g., due to imperfect "understanding" of the guideline recommendation by the NLP algorithms) could have severe consequences on patient health and outcomes.…”

Section: Discussionmentioning

confidence: 99%

Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Lichtner

Spies

Jurth

et al. 2022

Preprint

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Background Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gap-less implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content, but also to recognize every situation in which they are applicable. To not miss situations in which guideline recommendations should be applied, computerized clinical decision support could be given through a system that allows an automated monitoring of adherence to clinical guideline recommendation in individual patients. Objectives (1) To derive the requirements for a system that allows to monitor the adherence to evidence-based clinical guideline recommendations in individual patients, and based on these requirements, (2) to implement a software prototype that integrates clinical guideline recommendations with individual patient data and (3) to demonstrate the prototype's utility on a COVID-19 intensive care treatment recommendation. Methods We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system for supporting recommendation adherence monitoring in a consensus-based requirements analysis within loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, software developers). Based on these requirements, we implemented a prototype and demonstrated its functionality by integrating clinical data with a treatment recommendation. Results Based on our conceptual flow chart model of recommendation adherence monitoring in clinical routine, we identified four main requirements of a software system for automated support of recommendation adherence monitoring of in-hospital patients: (i) Ability to interpret guideline recommendations' semantics and logics, (ii) integration of clinical routine data from various underlying data structures, (iii) automatic adoption of new or updated guideline recommendations, and (iv) user interfaces optimized for distinct groups of users. Using a prototype implementation that fulfills these requirements, we demonstrate how such a system could be applied to monitor guideline recommendation adherence over time in clinical patients. Conclusions The four main requirements identified through our model-based analysis represent the most important aspects that need to be considered when developing a clinical decision support system for monitoring the adherence to evidence-based clinical guideline recommendations in individual patients. As each of the requirements corresponds to a different expertise (guideline development, health data engineering, software development, patient treatment), a modularized software architecture separated by area of required expertise seems favorable. Our prototype successfully demonstrates how such a modular architecture can be implemented to allow real-time monitoring of guideline recommendation adherence. This prototype, which we released as open source to invigorate collaboration, could serve as a basis for further development to integrate guideline recommendations with clinical information systems.

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Section: Discussionmentioning

confidence: 99%

Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Lichtner

Spies

Jurth

et al. 2022

Preprint

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show abstract

“…Therefore, specifying guideline recommendations from the start in a machine-readable format has multiple advantages, and we consider it a desirable change in the current practice of high-quality evidence-based guideline recommendation development. An alternative approach to using a machine-readable guideline recommendation specification could be the application of recent advances in natural language processing methods to allow the computer to understand and process human-readable guideline recommendations [53,54]. However, any errors unknowingly introduced by such an approach (eg, because of imperfect understanding of the guidelines by natural language processing algorithms) could have severe consequences for patient health and outcomes.…”

Section: Principal Findingsmentioning

confidence: 99%

Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation Study

Lichtner¹,

Spies²,

Jurth³

et al. 2023

J Med Internet Res

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Background Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. Objective This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype’s utility in treatment recommendations. Methods We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. Results We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources–based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. Conclusions Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

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“…For example, when dealing with unstructured nursing notes and medical guidelines, since such documents will have different records according to different doctor authors, there may be various fuzzy representations. In order to gather the information of these notes, Gangavarapu et al used fuzzy labeling combined with topic clustering to sort out features and multiple deep learning classifiers for text classification, helping subsequent healthcare analysis and disease prediction models [125], Fazlic et al combined fuzzy logic with LSTM models in deep learning to extract semantic features in medical guidelines, and then component-related fuzzy rules assisted classification [126].…”

Section: Medicinementioning

confidence: 99%

Review of Survey Research in Fuzzy Approach for Text Mining

Lai

Chen

2023

IEEE Access

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Text mining has been common research field, with the emergence of Web 2.0 and the development of social software, the amount of text generated every day has increased dramatically. The texts contain a lot of valuable information, how to analysis for the information from text is very important. Therefore, many research have explored related methods and various fields of text mining, such as sentiment analysis, text clustering, text summarization, etc., However, unlike other numerical data that can be directly calculated in terms of character performance, the calculation must be performed after vector conversion, and the words may have polysemy, which provides more challenges for natural language processing. Due to the above challenges, various techniques are used for data preprocessing before analysis. In addition to the common statistical and discrete methods for text data, methods based on fuzzy logic provide another option for effective natural language analysis, Therefore, in recent years, more and more studies have added Fuzzy logic to additionally capture the context semantics of individual words to help more accurate natural language processing. This survey research discusses multiple text mining methods, The survey paper discusses multiple text mining methods, subfields and application fields, covering the literature published between 2010 and 2022, It is organized to the subtasks to be performed, the methods and natural language processing techniques used, and the application scenarios. At the end of this study, the research has provided the key point discussion and relevant suggestions for text mining combined with fuzzy logic.

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A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines

Cited by 16 publications

References 16 publications

Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation Study

Review of Survey Research in Fuzzy Approach for Text Mining

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