Temporal Drift in the Semantic Meaning of Pediatric Anxiety Terms in Electronic Healthcare Records Article Swipe
Jordan Tschida
,
Mayanka Chandrashekar
,
Heidi A. Hanson
,
Ian Goethert
,
Surbhi Bhatnagar
,
Daniel Santel
,
John Pestian
,
Jeffery R Stawn
,
Tracy A. Glauser
,
Anuj J. Kapadia
,
Greeshma Agasthya
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.09.25323626
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.09.25323626
Objective To identify and measure semantic drift (i.e., the change in semantic meaning over time) in expert-provided anxiety-related (AR) terminology and compare it to other common electronic health record (EHR) vocabulary in longitudinal clinical notes. Methods Computational methods were used to investigate semantic drift in a pediatric clinical note corpus from 2009 to 2022. First, we measured the semantic drift of a word using the similarity of temporal word embeddings. Second, we analyzed how a word’s contextual meaning evolved over successive years by examining its nearest neighbors. Third, we investigated the Laws of Semantic Change to measure frequency and polysemy. Words were categorized as AR or common EHR vocabulary. Results 98% of the AR terminology maintained a cosine similarity score of 0.00 – 0.50; at least 90% of common EHR vocabulary maintained a cosine similarity score of 0.00 – 0.25. Laws of Semantic Change indicated that frequently occurring vocabulary words remained contextually stable (Frequency Coefficient = 0.04); however, words with multiple meanings, such as abbreviations, did not show the same stability (Polysemy Coefficient = 0.630). The semantic change over time within the AR terminology was slower on average than the semantic change within the common EHR vocabulary (Type Coefficient = -0.179); this was further validated by interacting the year and Type (Coef = -0.09 – -0.523). Conclusions The semantic meaning of anxiety terms remains stable within our dataset, indicating slower overall semantic drift compared to common EHR vocabulary. However, failure to capture nuanced changes may impact the accuracy and reliability of clinical decision support systems over time.
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Metadata
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- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.09.25323626
- OA Status
- green
- References
- 21
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408403838Canonical identifier for this work in OpenAlex
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https://doi.org/10.1101/2025.03.09.25323626Digital Object Identifier
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Temporal Drift in the Semantic Meaning of Pediatric Anxiety Terms in Electronic Healthcare RecordsWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-12Full publication date if available
- Authors
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Jordan Tschida, Mayanka Chandrashekar, Heidi A. Hanson, Ian Goethert, Surbhi Bhatnagar, Daniel Santel, John Pestian, Jeffery R Stawn, Tracy A. Glauser, Anuj J. Kapadia, Greeshma AgasthyaList of authors in order
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https://doi.org/10.1101/2025.03.09.25323626Publisher landing page
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greenOpen access status per OpenAlex
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https://doi.org/10.1101/2025.03.09.25323626Direct OA link when available
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Meaning (existential), Anxiety, Psychology, Health care, Electronic health record, Health records, Cognitive psychology, Psychiatry, Political science, Psychotherapist, LawTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.Methods | 36 |
| abstract_inverted_index.Results | 110 |
| abstract_inverted_index.Second, | 71 |
| abstract_inverted_index.anxiety | 222 |
| abstract_inverted_index.average | 188 |
| abstract_inverted_index.capture | 242 |
| abstract_inverted_index.changes | 244 |
| abstract_inverted_index.compare | 22 |
| abstract_inverted_index.evolved | 79 |
| abstract_inverted_index.failure | 240 |
| abstract_inverted_index.further | 204 |
| abstract_inverted_index.meaning | 13, 78, 220 |
| abstract_inverted_index.measure | 5, 97 |
| abstract_inverted_index.methods | 38 |
| abstract_inverted_index.nearest | 86 |
| abstract_inverted_index.nuanced | 243 |
| abstract_inverted_index.overall | 231 |
| abstract_inverted_index.remains | 224 |
| abstract_inverted_index.support | 254 |
| abstract_inverted_index.systems | 255 |
| abstract_inverted_index.-0.179); | 201 |
| abstract_inverted_index.-0.523). | 216 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 239 |
| abstract_inverted_index.Semantic | 94, 143 |
| abstract_inverted_index.accuracy | 248 |
| abstract_inverted_index.analyzed | 73 |
| abstract_inverted_index.clinical | 34, 48, 252 |
| abstract_inverted_index.compared | 234 |
| abstract_inverted_index.dataset, | 228 |
| abstract_inverted_index.decision | 253 |
| abstract_inverted_index.however, | 158 |
| abstract_inverted_index.identify | 3 |
| abstract_inverted_index.measured | 57 |
| abstract_inverted_index.multiple | 161 |
| abstract_inverted_index.remained | 151 |
| abstract_inverted_index.semantic | 6, 12, 43, 59, 177, 191, 219, 232 |
| abstract_inverted_index.temporal | 68 |
| abstract_inverted_index.word’s | 76 |
| abstract_inverted_index.(Polysemy | 172 |
| abstract_inverted_index.Objective | 1 |
| abstract_inverted_index.examining | 84 |
| abstract_inverted_index.frequency | 98 |
| abstract_inverted_index.indicated | 145 |
| abstract_inverted_index.meanings, | 162 |
| abstract_inverted_index.occurring | 148 |
| abstract_inverted_index.pediatric | 47 |
| abstract_inverted_index.polysemy. | 100 |
| abstract_inverted_index.stability | 171 |
| abstract_inverted_index.validated | 205 |
| abstract_inverted_index.(Frequency | 154 |
| abstract_inverted_index.contextual | 77 |
| abstract_inverted_index.electronic | 27 |
| abstract_inverted_index.frequently | 147 |
| abstract_inverted_index.indicating | 229 |
| abstract_inverted_index.maintained | 116, 132 |
| abstract_inverted_index.neighbors. | 87 |
| abstract_inverted_index.similarity | 66, 119, 135 |
| abstract_inverted_index.successive | 81 |
| abstract_inverted_index.vocabulary | 31, 131, 149, 197 |
| abstract_inverted_index.Coefficient | 155, 173, 199 |
| abstract_inverted_index.Conclusions | 217 |
| abstract_inverted_index.categorized | 103 |
| abstract_inverted_index.embeddings. | 70 |
| abstract_inverted_index.interacting | 207 |
| abstract_inverted_index.investigate | 42 |
| abstract_inverted_index.reliability | 250 |
| abstract_inverted_index.terminology | 20, 115, 184 |
| abstract_inverted_index.vocabulary. | 109, 238 |
| abstract_inverted_index.contextually | 152 |
| abstract_inverted_index.investigated | 90 |
| abstract_inverted_index.longitudinal | 33 |
| abstract_inverted_index.Computational | 37 |
| abstract_inverted_index.abbreviations, | 165 |
| abstract_inverted_index.anxiety-related | 18 |
| abstract_inverted_index.expert-provided | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.08789175 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |