Identification of Health Conditions in Unstructured Health Records with Deep Learning-Based Natural Language Processing Article Swipe
Jake Lin
,
Tomi Korpi
,
Anna Kuukka
,
Anna Tirkkonen
,
Antti Kariluoto
,
Juho Kaijansinkko
,
Maija Satamo
,
Hanna Pajulammi
,
Markus J. Haapanen
,
Sergei Häyrynen
,
Eetu Pursiainen
,
Daniel Ciovica
,
Mikaela B. von Bonsdorff
,
Juulia Jylhävä
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.08.24315141
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.08.24315141
Importance Many clinically significant health conditions are frequently underreported, underdiagnosed or recorded only in unstructured textual health records, yet they contain critical information for patient assessment, care and prognosis. Objective To determine whether deep learning-based natural language processing employed for named entity recognition can effectively identify health conditions, such as incontinence, falls, mobility limitations and loneliness in unstructured textual electronic health records. The identified conditions were further used to predict all-cause mortality. Design This cohort study utilized electronic health records from public primary, secondary, tertiary, long-term and home care from 2010 to 2022, providing up to 12 years of follow-up. The named entity recognition task to identify incontinence, falls, mobility limitations, and loneliness was implemented using Google’s Bidirectional Encoder Representations from Transformers deep learning model pre-trained for the Finnish language. Diagnostic codes for incontinence and falls were collected for comparisons. Setting Retrospective electronic health records across the Central Finland wellbeing services county. Participants Structured summary data and 10.6 million free-text entries from 102,525 patients aged 50 to 80 years at baseline. Exposure Incontinence, falls, mobility limitations and loneliness were considered as exposures. Main Outcomes and Measures The performance of the named entity recognition models was evaluated by precision, recall and F1 scores benchmarked against human ratings. Cox regression models were used to assess and compare NER- and diagnostic code-identified falls and incontinence onsets in predicting all-cause mortality. Results The deep learning model demonstrated excellent performance with recall, precision and F1 scores of 0.86, 0.88, and 0.87 for falls; 0.84, 0.78, and 0.81 for incontinence; 0.86, 0.84, and 0.85 for mobility limitations and 0.91, 0.84, and 0.87 for loneliness, respectively. Compared to diagnostic codes, named entity recognition identified greater numbers of falls (31987 vs 4090) and incontinence (7059 vs 3873) onsets and yielded greater hazard ratios: 1.31 vs 1.04 for falls and 1.99 vs 0.65 for incontinence. Conclusions and Relevance Deep learning-based named entity recognition models reliably identified incontinence, falls, loneliness and mobility limitations in free-text medical records, presenting new opportunities to use unstructured clinical data to identify vulnerable patients and apply the method in research applications. KEY POINTS Question Can deep learning-based natural language processing (NLP) identify health conditions, such as incontinence, falls, mobility limitations and loneliness in unstructured electronic health records (EHRs)? Findings The results of this cohort study demonstrate that a deep-learning NLP model can effectively identify incontinence, falls, mobility limitations and loneliness in textual EHR data. This approach also results in improved mortality prediction compared to available diagnostic codes. Meaning NLP approaches could be used to identify underreported and underdiagnosed health conditions in textural EHR data, enabling identification of vulnerable and at-risk patients.
Related Topics
Concepts
Recall
Medicine
Artificial intelligence
Named-entity recognition
Text messaging
Health care
Hazard ratio
Natural language processing
Computer science
Machine learning
Gerontology
Psychology
Internal medicine
Engineering
Cognitive psychology
World Wide Web
Economics
Systems engineering
Economic growth
Confidence interval
Task (project management)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.10.08.24315141
- OA Status
- green
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403287422
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403287422Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.10.08.24315141Digital Object Identifier
- Title
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Identification of Health Conditions in Unstructured Health Records with Deep Learning-Based Natural Language ProcessingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-10-10Full publication date if available
- Authors
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Jake Lin, Tomi Korpi, Anna Kuukka, Anna Tirkkonen, Antti Kariluoto, Juho Kaijansinkko, Maija Satamo, Hanna Pajulammi, Markus J. Haapanen, Sergei Häyrynen, Eetu Pursiainen, Daniel Ciovica, Mikaela B. von Bonsdorff, Juulia JylhäväList of authors in order
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https://doi.org/10.1101/2024.10.08.24315141Publisher landing page
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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://doi.org/10.1101/2024.10.08.24315141Direct OA link when available
- Concepts
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Recall, Medicine, Artificial intelligence, Named-entity recognition, Text messaging, Health care, Hazard ratio, Natural language processing, Computer science, Machine learning, Gerontology, Psychology, Internal medicine, Engineering, Cognitive psychology, World Wide Web, Economics, Systems engineering, Economic growth, Confidence interval, Task (project management)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.precision | 238 |
| abstract_inverted_index.providing | 94 |
| abstract_inverted_index.tertiary, | 85 |
| abstract_inverted_index.wellbeing | 150 |
| abstract_inverted_index.Diagnostic | 131 |
| abstract_inverted_index.Google’s | 117 |
| abstract_inverted_index.Importance | 1 |
| abstract_inverted_index.Structured | 154 |
| abstract_inverted_index.approaches | 415 |
| abstract_inverted_index.clinically | 3 |
| abstract_inverted_index.conditions | 6, 65, 425 |
| abstract_inverted_index.considered | 180 |
| abstract_inverted_index.diagnostic | 218, 272, 411 |
| abstract_inverted_index.electronic | 60, 78, 143, 370 |
| abstract_inverted_index.exposures. | 182 |
| abstract_inverted_index.follow-up. | 100 |
| abstract_inverted_index.frequently | 8 |
| abstract_inverted_index.identified | 64, 277, 317 |
| abstract_inverted_index.loneliness | 56, 113, 178, 320, 367, 395 |
| abstract_inverted_index.mortality. | 72, 227 |
| abstract_inverted_index.precision, | 198 |
| abstract_inverted_index.predicting | 225 |
| abstract_inverted_index.prediction | 407 |
| abstract_inverted_index.presenting | 328 |
| abstract_inverted_index.processing | 38, 355 |
| abstract_inverted_index.prognosis. | 29 |
| abstract_inverted_index.regression | 208 |
| abstract_inverted_index.secondary, | 84 |
| abstract_inverted_index.vulnerable | 338, 433 |
| abstract_inverted_index.Conclusions | 307 |
| abstract_inverted_index.assessment, | 26 |
| abstract_inverted_index.benchmarked | 203 |
| abstract_inverted_index.conditions, | 48, 359 |
| abstract_inverted_index.demonstrate | 381 |
| abstract_inverted_index.effectively | 45, 388 |
| abstract_inverted_index.implemented | 115 |
| abstract_inverted_index.information | 23 |
| abstract_inverted_index.limitations | 54, 176, 261, 323, 365, 393 |
| abstract_inverted_index.loneliness, | 268 |
| abstract_inverted_index.performance | 188, 235 |
| abstract_inverted_index.pre-trained | 126 |
| abstract_inverted_index.recognition | 43, 104, 193, 276, 314 |
| abstract_inverted_index.significant | 4 |
| abstract_inverted_index.Participants | 153 |
| abstract_inverted_index.Transformers | 122 |
| abstract_inverted_index.comparisons. | 140 |
| abstract_inverted_index.demonstrated | 233 |
| abstract_inverted_index.incontinence | 134, 222, 286 |
| abstract_inverted_index.limitations, | 111 |
| abstract_inverted_index.unstructured | 15, 58, 333, 369 |
| abstract_inverted_index.Bidirectional | 118 |
| abstract_inverted_index.Incontinence, | 173 |
| abstract_inverted_index.Retrospective | 142 |
| abstract_inverted_index.applications. | 346 |
| abstract_inverted_index.deep-learning | 384 |
| abstract_inverted_index.incontinence, | 51, 108, 318, 362, 390 |
| abstract_inverted_index.incontinence. | 306 |
| abstract_inverted_index.incontinence; | 254 |
| abstract_inverted_index.opportunities | 330 |
| abstract_inverted_index.respectively. | 269 |
| abstract_inverted_index.underreported | 421 |
| abstract_inverted_index.identification | 431 |
| abstract_inverted_index.learning-based | 35, 311, 352 |
| abstract_inverted_index.underdiagnosed | 10, 423 |
| abstract_inverted_index.underreported, | 9 |
| abstract_inverted_index.Representations | 120 |
| abstract_inverted_index.code-identified | 219 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5062085920 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I166825849, https://openalex.org/I28166907 |
| citation_normalized_percentile.value | 0.18903413 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |