Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity Article Swipe
Jan Janosch Schneider
,
Marius Adler
,
Christoph Ammer‐Herrmenau
,
Alexander König
,
Ulrich Sax
,
Jonas Hügel
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.07359
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.07359
Finding similar patients is a common objective in precision medicine, facilitating treatment outcome assessment and clinical decision support. Choosing widely-available patient features and appropriate mathematical methods for similarity calculations is crucial. International Statistical Classification of Diseases and Related Health Problems (ICD) codes are used worldwide to encode diseases and are available for nearly all patients. Aggregated as sets consisting of primary and secondary diagnoses they can display a degree of comorbidity and reveal comorbidity patterns. It is possible to compute the similarity of patients based on their ICD codes by using semantic similarity algorithms. These algorithms have been traditionally evaluated using a single-term expert rated data set. However, real-word patient data often display varying degrees of documented comorbidities that might impair algorithm performance. To account for this, we present a scale term that considers documented comorbidity-variance. In this work, we compared the performance of 80 combinations of established algorithms in terms of semantic similarity based on ICD-code sets. The sets have been extracted from patients with a C25.X (pancreatic cancer) primary diagnosis and provide a variety of different combinations of ICD-codes. Using our scale term we yielded the best results with a combination of level-based information content, Leacock & Chodorow concept similarity and bipartite graph matching for the set similarities reaching a correlation of 0.75 with our expert's ground truth. Our results highlight the importance of accounting for comorbidity variance while demonstrating how well current semantic similarity algorithms perform.
Related Topics
Concepts
Comorbidity
Similarity (geometry)
Semantic similarity
Medical diagnosis
Computer science
Set (abstract data type)
Natural language processing
Data mining
Information retrieval
Diagnosis code
Variance (accounting)
Artificial intelligence
Machine learning
Medicine
Psychiatry
Programming language
Image (mathematics)
Business
Population
Environmental health
Pathology
Accounting
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.07359
- https://arxiv.org/pdf/2308.07359
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385889806
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385889806Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2308.07359Digital Object Identifier
- Title
-
Improving ICD-based semantic similarity by accounting for varying degrees of comorbidityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-14Full publication date if available
- Authors
-
Jan Janosch Schneider, Marius Adler, Christoph Ammer‐Herrmenau, Alexander König, Ulrich Sax, Jonas HügelList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.07359Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.07359Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.07359Direct OA link when available
- Concepts
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Comorbidity, Similarity (geometry), Semantic similarity, Medical diagnosis, Computer science, Set (abstract data type), Natural language processing, Data mining, Information retrieval, Diagnosis code, Variance (accounting), Artificial intelligence, Machine learning, Medicine, Psychiatry, Programming language, Image (mathematics), Business, Population, Environmental health, Pathology, AccountingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sets | 57, 159 |
| abstract_inverted_index.term | 131, 184 |
| abstract_inverted_index.that | 118, 132 |
| abstract_inverted_index.they | 64 |
| abstract_inverted_index.this | 137 |
| abstract_inverted_index.used | 43 |
| abstract_inverted_index.well | 233 |
| abstract_inverted_index.with | 165, 190, 215 |
| abstract_inverted_index.& | 198 |
| abstract_inverted_index.(ICD) | 40 |
| abstract_inverted_index.C25.X | 167 |
| abstract_inverted_index.These | 94 |
| abstract_inverted_index.Using | 181 |
| abstract_inverted_index.based | 84, 154 |
| abstract_inverted_index.codes | 41, 88 |
| abstract_inverted_index.graph | 204 |
| abstract_inverted_index.might | 119 |
| abstract_inverted_index.often | 111 |
| abstract_inverted_index.rated | 104 |
| abstract_inverted_index.scale | 130, 183 |
| abstract_inverted_index.sets. | 157 |
| abstract_inverted_index.terms | 150 |
| abstract_inverted_index.their | 86 |
| abstract_inverted_index.this, | 126 |
| abstract_inverted_index.using | 90, 100 |
| abstract_inverted_index.while | 230 |
| abstract_inverted_index.work, | 138 |
| abstract_inverted_index.Health | 38 |
| abstract_inverted_index.common | 5 |
| abstract_inverted_index.degree | 68 |
| abstract_inverted_index.encode | 46 |
| abstract_inverted_index.expert | 103 |
| abstract_inverted_index.ground | 218 |
| abstract_inverted_index.impair | 120 |
| abstract_inverted_index.nearly | 52 |
| abstract_inverted_index.reveal | 72 |
| abstract_inverted_index.truth. | 219 |
| abstract_inverted_index.Finding | 0 |
| abstract_inverted_index.Leacock | 197 |
| abstract_inverted_index.Related | 37 |
| abstract_inverted_index.account | 124 |
| abstract_inverted_index.cancer) | 169 |
| abstract_inverted_index.compute | 79 |
| abstract_inverted_index.concept | 200 |
| abstract_inverted_index.current | 234 |
| abstract_inverted_index.degrees | 114 |
| abstract_inverted_index.display | 66, 112 |
| abstract_inverted_index.methods | 25 |
| abstract_inverted_index.outcome | 12 |
| abstract_inverted_index.patient | 20, 109 |
| abstract_inverted_index.present | 128 |
| abstract_inverted_index.primary | 60, 170 |
| abstract_inverted_index.provide | 173 |
| abstract_inverted_index.results | 189, 221 |
| abstract_inverted_index.similar | 1 |
| abstract_inverted_index.variety | 175 |
| abstract_inverted_index.varying | 113 |
| abstract_inverted_index.yielded | 186 |
| abstract_inverted_index.Chodorow | 199 |
| abstract_inverted_index.Choosing | 18 |
| abstract_inverted_index.Diseases | 35 |
| abstract_inverted_index.However, | 107 |
| abstract_inverted_index.ICD-code | 156 |
| abstract_inverted_index.Problems | 39 |
| abstract_inverted_index.clinical | 15 |
| abstract_inverted_index.compared | 140 |
| abstract_inverted_index.content, | 196 |
| abstract_inverted_index.crucial. | 30 |
| abstract_inverted_index.decision | 16 |
| abstract_inverted_index.diseases | 47 |
| abstract_inverted_index.expert's | 217 |
| abstract_inverted_index.features | 21 |
| abstract_inverted_index.matching | 205 |
| abstract_inverted_index.patients | 2, 83, 164 |
| abstract_inverted_index.perform. | 238 |
| abstract_inverted_index.possible | 77 |
| abstract_inverted_index.reaching | 210 |
| abstract_inverted_index.semantic | 91, 152, 235 |
| abstract_inverted_index.support. | 17 |
| abstract_inverted_index.variance | 229 |
| abstract_inverted_index.algorithm | 121 |
| abstract_inverted_index.available | 50 |
| abstract_inverted_index.bipartite | 203 |
| abstract_inverted_index.considers | 133 |
| abstract_inverted_index.diagnoses | 63 |
| abstract_inverted_index.diagnosis | 171 |
| abstract_inverted_index.different | 177 |
| abstract_inverted_index.evaluated | 99 |
| abstract_inverted_index.extracted | 162 |
| abstract_inverted_index.highlight | 222 |
| abstract_inverted_index.medicine, | 9 |
| abstract_inverted_index.objective | 6 |
| abstract_inverted_index.patients. | 54 |
| abstract_inverted_index.patterns. | 74 |
| abstract_inverted_index.precision | 8 |
| abstract_inverted_index.real-word | 108 |
| abstract_inverted_index.secondary | 62 |
| abstract_inverted_index.treatment | 11 |
| abstract_inverted_index.worldwide | 44 |
| abstract_inverted_index.Aggregated | 55 |
| abstract_inverted_index.ICD-codes. | 180 |
| abstract_inverted_index.accounting | 226 |
| abstract_inverted_index.algorithms | 95, 148, 237 |
| abstract_inverted_index.assessment | 13 |
| abstract_inverted_index.consisting | 58 |
| abstract_inverted_index.documented | 116, 134 |
| abstract_inverted_index.importance | 224 |
| abstract_inverted_index.similarity | 27, 81, 92, 153, 201, 236 |
| abstract_inverted_index.(pancreatic | 168 |
| abstract_inverted_index.Statistical | 32 |
| abstract_inverted_index.algorithms. | 93 |
| abstract_inverted_index.appropriate | 23 |
| abstract_inverted_index.combination | 192 |
| abstract_inverted_index.comorbidity | 70, 73, 228 |
| abstract_inverted_index.correlation | 212 |
| abstract_inverted_index.established | 147 |
| abstract_inverted_index.information | 195 |
| abstract_inverted_index.level-based | 194 |
| abstract_inverted_index.performance | 142 |
| abstract_inverted_index.single-term | 102 |
| abstract_inverted_index.calculations | 28 |
| abstract_inverted_index.combinations | 145, 178 |
| abstract_inverted_index.facilitating | 10 |
| abstract_inverted_index.mathematical | 24 |
| abstract_inverted_index.performance. | 122 |
| abstract_inverted_index.similarities | 209 |
| abstract_inverted_index.International | 31 |
| abstract_inverted_index.comorbidities | 117 |
| abstract_inverted_index.demonstrating | 231 |
| abstract_inverted_index.traditionally | 98 |
| abstract_inverted_index.Classification | 33 |
| abstract_inverted_index.widely-available | 19 |
| abstract_inverted_index.comorbidity-variance. | 135 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |