Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes
Abstract Background Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-10-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-020-01282-z |
| _version_ | 1818408872757428224 |
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| author | Xiaojun Li Guangjian Liu Wenxiong Chen Zhisheng Bi Huiying Liang |
| author_facet | Xiaojun Li Guangjian Liu Wenxiong Chen Zhisheng Bi Huiying Liang |
| author_sort | Xiaojun Li |
| collection | DOAJ |
| description | Abstract Background Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism. Methods We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children’s Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search. Results We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported. Conclusion We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism. |
| first_indexed | 2024-12-14T09:50:38Z |
| format | Article |
| id | doaj.art-c301715bba934fafa7468200d7a98161 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-14T09:50:38Z |
| publishDate | 2020-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-c301715bba934fafa7468200d7a981612022-12-21T23:07:32ZengBMCBMC Medical Informatics and Decision Making1472-69472020-10-0120111110.1186/s12911-020-01282-zNetwork analysis of autistic disease comorbidities in Chinese children based on ICD-10 codesXiaojun Li0Guangjian Liu1Wenxiong Chen2Zhisheng Bi3Huiying Liang4Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical UniversityInstitute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical UniversityDepartment of Neurology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical UniversitySchool of Basic Medical Sciences, Guangzhou Medical UniversityInstitute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical UniversityAbstract Background Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism. Methods We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children’s Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search. Results We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported. Conclusion We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.http://link.springer.com/article/10.1186/s12911-020-01282-zAutismComorbidityDisease network |
| spellingShingle | Xiaojun Li Guangjian Liu Wenxiong Chen Zhisheng Bi Huiying Liang Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes BMC Medical Informatics and Decision Making Autism Comorbidity Disease network |
| title | Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes |
| title_full | Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes |
| title_fullStr | Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes |
| title_full_unstemmed | Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes |
| title_short | Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes |
| title_sort | network analysis of autistic disease comorbidities in chinese children based on icd 10 codes |
| topic | Autism Comorbidity Disease network |
| url | http://link.springer.com/article/10.1186/s12911-020-01282-z |
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