Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity
Abstract Background Many clinical concepts are standardized under a categorical and hierarchical taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into semantic meaning and similarity among clinical concepts and have been applied to patient similarity measures. How...
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| Format: | Article |
| Language: | English |
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BMC
2019-04-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | http://link.springer.com/article/10.1186/s12911-019-0807-y |
| _version_ | 1819168769361051648 |
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| author | Zheng Jia Xudong Lu Huilong Duan Haomin Li |
| author_facet | Zheng Jia Xudong Lu Huilong Duan Haomin Li |
| author_sort | Zheng Jia |
| collection | DOAJ |
| description | Abstract Background Many clinical concepts are standardized under a categorical and hierarchical taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into semantic meaning and similarity among clinical concepts and have been applied to patient similarity measures. However, the effects of diverse set sizes of taxonomic clinical concepts contributing to similarity at the patient level have not been well studied. Methods In this paper the most widely used taxonomic clinical concepts system, ICD-10, was studied as a representative taxonomy. The distance between ICD-10-coded diagnosis sets is an integrated estimation of the information content of each concept, the similarity between each pairwise concepts and the similarity between the sets of concepts. We proposed a novel method at the set-level similarity to calculate the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity. A real-world clinical dataset with ICD-10 coded diagnoses and hospital length of stay (HLOS) information was used to evaluate the performance of various algorithms and their combinations in predicting whether a patient need long-term hospitalization or not. Four subpopulation prototypes that were defined based on age and HLOS with different diagnoses set sizes were used as the target for similarity analysis. The F-score was used to evaluate the performance of different algorithms by controlling other factors. We also evaluated the effect of prototype set size on prediction precision. Results The results identified the strengths and weaknesses of different algorithms to compute information content, code-level similarity and set-level similarity under different contexts, such as set size and concept set background. The minimum weighted bipartite matching approach, which has not been fully recognized previously showed unique advantages in measuring the concepts-based patient similarity. Conclusions This study provides a systematic benchmark evaluation of previous algorithms and novel algorithms used in taxonomic concepts-based patient similarity, and it provides the basis for selecting appropriate methods under different clinical scenarios. |
| first_indexed | 2024-12-22T19:08:52Z |
| format | Article |
| id | doaj.art-ed1a95217fec42c7a07de8ce1201100e |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-22T19:08:52Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-ed1a95217fec42c7a07de8ce1201100e2022-12-21T18:15:44ZengBMCBMC Medical Informatics and Decision Making1472-69472019-04-0119111110.1186/s12911-019-0807-yUsing the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarityZheng Jia0Xudong Lu1Huilong Duan2Haomin Li3College of Biomedical Engineering and Instrument Science, Zhejiang UniversityCollege of Biomedical Engineering and Instrument Science, Zhejiang UniversityCollege of Biomedical Engineering and Instrument Science, Zhejiang UniversityThe Children’s Hospital, Zhejiang University School of MedicineAbstract Background Many clinical concepts are standardized under a categorical and hierarchical taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into semantic meaning and similarity among clinical concepts and have been applied to patient similarity measures. However, the effects of diverse set sizes of taxonomic clinical concepts contributing to similarity at the patient level have not been well studied. Methods In this paper the most widely used taxonomic clinical concepts system, ICD-10, was studied as a representative taxonomy. The distance between ICD-10-coded diagnosis sets is an integrated estimation of the information content of each concept, the similarity between each pairwise concepts and the similarity between the sets of concepts. We proposed a novel method at the set-level similarity to calculate the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity. A real-world clinical dataset with ICD-10 coded diagnoses and hospital length of stay (HLOS) information was used to evaluate the performance of various algorithms and their combinations in predicting whether a patient need long-term hospitalization or not. Four subpopulation prototypes that were defined based on age and HLOS with different diagnoses set sizes were used as the target for similarity analysis. The F-score was used to evaluate the performance of different algorithms by controlling other factors. We also evaluated the effect of prototype set size on prediction precision. Results The results identified the strengths and weaknesses of different algorithms to compute information content, code-level similarity and set-level similarity under different contexts, such as set size and concept set background. The minimum weighted bipartite matching approach, which has not been fully recognized previously showed unique advantages in measuring the concepts-based patient similarity. Conclusions This study provides a systematic benchmark evaluation of previous algorithms and novel algorithms used in taxonomic concepts-based patient similarity, and it provides the basis for selecting appropriate methods under different clinical scenarios.http://link.springer.com/article/10.1186/s12911-019-0807-yTaxonomic conceptPatient similarityConcept similarityPredictive modelICD-10Data visualization |
| spellingShingle | Zheng Jia Xudong Lu Huilong Duan Haomin Li Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity BMC Medical Informatics and Decision Making Taxonomic concept Patient similarity Concept similarity Predictive model ICD-10 Data visualization |
| title | Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity |
| title_full | Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity |
| title_fullStr | Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity |
| title_full_unstemmed | Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity |
| title_short | Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity |
| title_sort | using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity |
| topic | Taxonomic concept Patient similarity Concept similarity Predictive model ICD-10 Data visualization |
| url | http://link.springer.com/article/10.1186/s12911-019-0807-y |
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