Assisted morbidity coding: the SISCO.web use case for identifying the main diagnosis in Hospital Discharge Records
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915185855299584 |
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| author | Cardillo, Elena Frattura, Lucilla |
| author_facet | Cardillo, Elena Frattura, Lucilla |
| contents | Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and physicians assign codes to patient episodes based on their interpretation of case notes or electronic patient records. Therefore, accurate coding relies on the legibility of case notes and the coders' understanding of medical terminology. During the last ten years, many studies have shown poor reproducibility of clinical coding, even recently, with the application of Artificial Intelligence-based models. Given this context, the paper aims to present the SISCO.web approach designed to support physicians in filling in Hospital Discharge Records with proper diagnoses and procedures codes using the International Classification of Diseases (9th and 10th), and, above all, in identifying the main pathological condition. The web service leverages NLP algorithms, specific coding rules, as well as ad hoc decision trees to identify the main condition, showing promising results in providing accurate ICD coding suggestions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09651 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assisted morbidity coding: the SISCO.web use case for identifying the main diagnosis in Hospital Discharge Records Cardillo, Elena Frattura, Lucilla Other Computer Science Artificial Intelligence H.4.2; H.3.3; H.3.5; H.3.1; I.2.1; I.2.4; J.3 Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and physicians assign codes to patient episodes based on their interpretation of case notes or electronic patient records. Therefore, accurate coding relies on the legibility of case notes and the coders' understanding of medical terminology. During the last ten years, many studies have shown poor reproducibility of clinical coding, even recently, with the application of Artificial Intelligence-based models. Given this context, the paper aims to present the SISCO.web approach designed to support physicians in filling in Hospital Discharge Records with proper diagnoses and procedures codes using the International Classification of Diseases (9th and 10th), and, above all, in identifying the main pathological condition. The web service leverages NLP algorithms, specific coding rules, as well as ad hoc decision trees to identify the main condition, showing promising results in providing accurate ICD coding suggestions. |
| title | Assisted morbidity coding: the SISCO.web use case for identifying the main diagnosis in Hospital Discharge Records |
| topic | Other Computer Science Artificial Intelligence H.4.2; H.3.3; H.3.5; H.3.1; I.2.1; I.2.4; J.3 |
| url | https://arxiv.org/abs/2412.09651 |