Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based Approach Article Swipe
Musarrat Hussain
,
Jamil Hussain
,
Taqdir Ali
,
Syed Imran Ali
,
Hafiz Syed Muhammad Bilal
,
Sungyoung Lee
,
TaeChoong Chung
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11083296
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11083296
Clinical Practice Guidelines (CPGs) aim to optimize patient care by assisting physicians during the decision-making process. However, guideline adherence is highly affected by its unstructured format and aggregation of background information with disease-specific information. The objective of our study is to extract disease-specific information from CPG for enhancing its adherence ratio. In this research, we propose a semi-automatic mechanism for extracting disease-specific information from CPGs using pattern-matching techniques. We apply supervised and unsupervised machine-learning algorithms on CPG to extract a list of salient terms contributing to distinguishing recommendation sentences (RS) from non-recommendation sentences (NRS). Simultaneously, a group of experts also analyzes the same CPG and extract the initial patterns “Heuristic Patterns” using a group decision-making method, nominal group technique (NGT). We provide the list of salient terms to the experts and ask them to refine their extracted patterns. The experts refine patterns considering the provided salient terms. The extracted heuristic patterns depend on specific terms and suffer from the specialization problem due to synonymy and polysemy. Therefore, we generalize the heuristic patterns to part-of-speech (POS) patterns and unified medical language system (UMLS) patterns, which make the proposed method generalize for all types of CPGs. We evaluated the initial extracted patterns on asthma, rhinosinusitis, and hypertension guidelines with the accuracy of 76.92%, 84.63%, and 89.16%, respectively. The accuracy increased to 78.89%, 85.32%, and 92.07% with refined machine-learning assistive patterns, respectively. Our system assists physicians by locating disease-specific information in the CPGs, which enhances the physicians’ performance and reduces CPG processing time. Additionally, it is beneficial in CPGs content annotation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app11083296
- https://www.mdpi.com/2076-3417/11/8/3296/pdf?version=1617940981
- OA Status
- gold
- Cited By
- 6
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3142749573
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3142749573Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app11083296Digital Object Identifier
- Title
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Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-04-07Full publication date if available
- Authors
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Musarrat Hussain, Jamil Hussain, Taqdir Ali, Syed Imran Ali, Hafiz Syed Muhammad Bilal, Sungyoung Lee, TaeChoong ChungList of authors in order
- Landing page
-
https://doi.org/10.3390/app11083296Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/11/8/3296/pdf?version=1617940981Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/11/8/3296/pdf?version=1617940981Direct OA link when available
- Concepts
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Computer science, Unified Medical Language System, Categorization, Salient, Artificial intelligence, Heuristic, Machine learning, Matching (statistics), Guideline, Natural language processing, Information retrieval, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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