AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice Article Swipe
Musarrat Hussain
,
Jamil Hussain
,
Taqdir Ali
,
Fahad Ahmed Satti
,
Sungyoung Lee
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.05489
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.05489
Background and Objectives: Clinical Practice Guidelines (CPGs) represent the foremost methodology for sharing state-of-the-art research findings in the healthcare domain with medical practitioners to limit practice variations, reduce clinical cost, improve the quality of care, and provide evidence based treatment. However, extracting relevant knowledge from the plethora of CPGs is not feasible for already burdened healthcare professionals, leading to large gaps between clinical findings and real practices. It is therefore imperative that state-of-the-art Computing research, especially machine learning is used to provide artificial intelligence based solution for extracting the knowledge from CPGs and reducing the gap between healthcare research/guidelines and practice. Methods: This research presents a novel methodology for knowledge extraction from CPGs to reduce the gap and turn the latest research findings into clinical practice. First, our system classifies the CPG sentences into four classes such as condition-action, condition-consequences, action, and not-applicable based on the information presented in a sentence. We use deep learning with state-of-the-art word embedding, improved word vectors technique in classification process. Second, it identifies qualifier terms in the classified sentences, which assist in recognizing the condition and action phrases in a sentence. Finally, the condition and action phrase are processed and transformed into plain rule If Condition(s) Then Action format. Results: We evaluate the methodology on three different domains guidelines including Hypertension, Rhinosinusitis, and Asthma. The deep learning model classifies the CPG sentences with an accuracy of 95%. While rule extraction was validated by user-centric approach, which achieved a Jaccard coefficient of 0.6, 0.7, and 0.4 with three human experts extracted rules, respectively.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2012.05489
- https://arxiv.org/pdf/2012.05489
- OA Status
- green
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3112559567Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2012.05489Digital Object Identifier
- Title
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AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into PracticeWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-10Full publication date if available
- Authors
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Musarrat Hussain, Jamil Hussain, Taqdir Ali, Fahad Ahmed Satti, Sungyoung LeeList of authors in order
- Landing page
-
https://arxiv.org/abs/2012.05489Publisher landing page
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https://arxiv.org/pdf/2012.05489Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2012.05489Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Health care, Sentence, Natural language processing, Knowledge management, Machine learning, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 1, 2021: 1Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.This | 102 |
| abstract_inverted_index.deep | 153, 221 |
| abstract_inverted_index.four | 134 |
| abstract_inverted_index.from | 44, 90, 111 |
| abstract_inverted_index.gaps | 60 |
| abstract_inverted_index.into | 123, 133, 197 |
| abstract_inverted_index.real | 65 |
| abstract_inverted_index.rule | 199, 234 |
| abstract_inverted_index.such | 136 |
| abstract_inverted_index.that | 71 |
| abstract_inverted_index.turn | 118 |
| abstract_inverted_index.used | 79 |
| abstract_inverted_index.with | 20, 155, 228, 251 |
| abstract_inverted_index.word | 157, 160 |
| abstract_inverted_index.While | 233 |
| abstract_inverted_index.based | 38, 84, 143 |
| abstract_inverted_index.care, | 34 |
| abstract_inverted_index.cost, | 29 |
| abstract_inverted_index.human | 253 |
| abstract_inverted_index.large | 59 |
| abstract_inverted_index.limit | 24 |
| abstract_inverted_index.model | 223 |
| abstract_inverted_index.novel | 106 |
| abstract_inverted_index.plain | 198 |
| abstract_inverted_index.terms | 170 |
| abstract_inverted_index.three | 211, 252 |
| abstract_inverted_index.which | 175, 241 |
| abstract_inverted_index.(CPGs) | 6 |
| abstract_inverted_index.Action | 203 |
| abstract_inverted_index.First, | 126 |
| abstract_inverted_index.action | 182, 191 |
| abstract_inverted_index.assist | 176 |
| abstract_inverted_index.domain | 19 |
| abstract_inverted_index.latest | 120 |
| abstract_inverted_index.phrase | 192 |
| abstract_inverted_index.reduce | 27, 114 |
| abstract_inverted_index.rules, | 256 |
| abstract_inverted_index.system | 128 |
| abstract_inverted_index.Asthma. | 219 |
| abstract_inverted_index.Jaccard | 244 |
| abstract_inverted_index.Second, | 166 |
| abstract_inverted_index.action, | 140 |
| abstract_inverted_index.already | 53 |
| abstract_inverted_index.between | 61, 96 |
| abstract_inverted_index.classes | 135 |
| abstract_inverted_index.domains | 213 |
| abstract_inverted_index.experts | 254 |
| abstract_inverted_index.format. | 204 |
| abstract_inverted_index.improve | 30 |
| abstract_inverted_index.leading | 57 |
| abstract_inverted_index.machine | 76 |
| abstract_inverted_index.medical | 21 |
| abstract_inverted_index.phrases | 183 |
| abstract_inverted_index.provide | 36, 81 |
| abstract_inverted_index.quality | 32 |
| abstract_inverted_index.sharing | 12 |
| abstract_inverted_index.vectors | 161 |
| abstract_inverted_index.Clinical | 3 |
| abstract_inverted_index.Finally, | 187 |
| abstract_inverted_index.However, | 40 |
| abstract_inverted_index.Methods: | 101 |
| abstract_inverted_index.Practice | 4 |
| abstract_inverted_index.Results: | 205 |
| abstract_inverted_index.accuracy | 230 |
| abstract_inverted_index.achieved | 242 |
| abstract_inverted_index.burdened | 54 |
| abstract_inverted_index.clinical | 28, 62, 124 |
| abstract_inverted_index.evaluate | 207 |
| abstract_inverted_index.evidence | 37 |
| abstract_inverted_index.feasible | 51 |
| abstract_inverted_index.findings | 15, 63, 122 |
| abstract_inverted_index.foremost | 9 |
| abstract_inverted_index.improved | 159 |
| abstract_inverted_index.learning | 77, 154, 222 |
| abstract_inverted_index.plethora | 46 |
| abstract_inverted_index.practice | 25 |
| abstract_inverted_index.presents | 104 |
| abstract_inverted_index.process. | 165 |
| abstract_inverted_index.reducing | 93 |
| abstract_inverted_index.relevant | 42 |
| abstract_inverted_index.research | 14, 103, 121 |
| abstract_inverted_index.solution | 85 |
| abstract_inverted_index.Computing | 73 |
| abstract_inverted_index.approach, | 240 |
| abstract_inverted_index.condition | 180, 189 |
| abstract_inverted_index.different | 212 |
| abstract_inverted_index.extracted | 255 |
| abstract_inverted_index.including | 215 |
| abstract_inverted_index.knowledge | 43, 89, 109 |
| abstract_inverted_index.practice. | 100, 125 |
| abstract_inverted_index.presented | 147 |
| abstract_inverted_index.processed | 194 |
| abstract_inverted_index.qualifier | 169 |
| abstract_inverted_index.represent | 7 |
| abstract_inverted_index.research, | 74 |
| abstract_inverted_index.sentence. | 150, 186 |
| abstract_inverted_index.sentences | 132, 227 |
| abstract_inverted_index.technique | 162 |
| abstract_inverted_index.therefore | 69 |
| abstract_inverted_index.validated | 237 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Guidelines | 5 |
| abstract_inverted_index.artificial | 82 |
| abstract_inverted_index.classified | 173 |
| abstract_inverted_index.classifies | 129, 224 |
| abstract_inverted_index.embedding, | 158 |
| abstract_inverted_index.especially | 75 |
| abstract_inverted_index.extracting | 41, 87 |
| abstract_inverted_index.extraction | 110, 235 |
| abstract_inverted_index.guidelines | 214 |
| abstract_inverted_index.healthcare | 18, 55, 97 |
| abstract_inverted_index.identifies | 168 |
| abstract_inverted_index.imperative | 70 |
| abstract_inverted_index.practices. | 66 |
| abstract_inverted_index.sentences, | 174 |
| abstract_inverted_index.treatment. | 39 |
| abstract_inverted_index.Objectives: | 2 |
| abstract_inverted_index.coefficient | 245 |
| abstract_inverted_index.information | 146 |
| abstract_inverted_index.methodology | 10, 107, 209 |
| abstract_inverted_index.recognizing | 178 |
| abstract_inverted_index.transformed | 196 |
| abstract_inverted_index.variations, | 26 |
| abstract_inverted_index.Condition(s) | 201 |
| abstract_inverted_index.intelligence | 83 |
| abstract_inverted_index.user-centric | 239 |
| abstract_inverted_index.Hypertension, | 216 |
| abstract_inverted_index.practitioners | 22 |
| abstract_inverted_index.respectively. | 257 |
| abstract_inverted_index.classification | 164 |
| abstract_inverted_index.not-applicable | 142 |
| abstract_inverted_index.professionals, | 56 |
| abstract_inverted_index.Rhinosinusitis, | 217 |
| abstract_inverted_index.state-of-the-art | 13, 72, 156 |
| abstract_inverted_index.condition-action, | 138 |
| abstract_inverted_index.research/guidelines | 98 |
| abstract_inverted_index.condition-consequences, | 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |