دورية أكاديمية
Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.
| العنوان: | Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre. |
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| المؤلفون: | Hunter, B, Reis, S, Campbell, D, Matharu, S, Ratnakumar, P, Mercuri, L, Hindocha, S, Kalsi, H, Mayer, E, Glampson, B, Robinson, EJ, Al-Lazikani, B, Scerri, L, Bloch, S, Lee, R |
| المساهمون: | Al-Lazikani, Bissan |
| بيانات النشر: | FRONTIERS MEDIA SA |
| سنة النشر: | 2022 |
| المجموعة: | The Institute of Cancer Research (ICR): Publications Repository |
| مصطلحات موضوعية: | informatics, lung nodule, machine learning, natural language processing (NLP), structured query language (SQL) |
| جغرافية الموضوع: | Switzerland |
| الوصف: | Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. Objective: To automate lung nodule identification in a tertiary cancer centre. Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients. Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy. Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | Electronic-eCollection; application/pdf |
| اللغة: | English |
| تدمد: | 2296-858X |
| العلاقة: | ARTN 748168; Frontiers in Medicine, 2021, 8 pp. 748168 -; https://repository.icr.ac.uk/handle/internal/5383Test |
| DOI: | 10.3389/fmed.2021.748168 |
| الإتاحة: | https://doi.org/10.3389/fmed.2021.748168Test https://repository.icr.ac.uk/handle/internal/5383Test |
| حقوق: | http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: | edsbas.68B0ABD4 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 2296858X |
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| DOI: | 10.3389/fmed.2021.748168 |