Classification of Paediatric Inflammatory Bowel Disease using Machine Learning.
Classification of Paediatric Inflammatory Bowel Disease using Machine Learning.
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DOI：
10.1038/s41598-017-02606-2
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发表时间：
2017-05-25
影响因子：
4.6
通讯作者：
Ennis S
中科院分区：
文献类型：
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作者：
Mossotto E;Ashton JJ;Coelho T;Beattie RM;MacArthur BD;Ennis S
小儿炎症性肠病（PIBD）包括克罗恩病（CD）、溃疡性结肠炎（UC）和未分类的炎症性肠病（IBDU），是一种复杂的多因素疾病，发病率不断增加。PIBD的准确诊断对于及时有效的治疗是必要的。本研究利用机器学习（ML），使用287名诊断患有PIBD的儿童的内窥镜和组织学数据对疾病进行分类。数据用于开发、训练、测试和验证ML模型，以分类疾病亚型。无监督模型显示CD/UC与广泛聚类重叠，但没有明确的亚型划分，而分层聚类确定了4个新的亚组，其特征在于不同的结肠受累。仅利用内窥镜数据、仅利用组织学数据和结合内窥镜/组织学数据开发了三种监督ML模型，分类准确率分别为71.0%、76.9%和82.7%。在来自同一诊所的48名PIBD患者的统计学独立队列中测试了最佳组合模型，准确分类了83.3%的患者。本研究采用内窥镜和组织学数据的数学建模来帮助诊断准确性。虽然无监督建模将患者分为四个亚组，但监督方法证实了需要内窥镜和组织学证据才能进行准确诊断。总的来说，本文提供了ML与临床数据结合使用的蓝图。
Paediatric inflammatory bowel disease (PIBD), comprising Crohn’s disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PIBD is necessary for a prompt and effective treatment. This study utilises machine learning (ML) to classify disease using endoscopic and histological data for 287 children diagnosed with PIBD. Data were used to develop, train, test and validate a ML model to classify disease subtype. Unsupervised models revealed overlap of CD/UC with broad clustering but no clear subtype delineation, whereas hierarchical clustering identified four novel subgroups characterised by differing colonic involvement. Three supervised ML models were developed utilising endoscopic data only, histological only and combined endoscopic/histological data yielding classification accuracy of 71.0%, 76.9% and 82.7% respectively. The optimal combined model was tested on a statistically independent cohort of 48 PIBD patients from the same clinic, accurately classifying 83.3% of patients. This study employs mathematical modelling of endoscopic and histological data to aid diagnostic accuracy. While unsupervised modelling categorises patients into four subgroups, supervised approaches confirm the need of both endoscopic and histological evidence for an accurate diagnosis. Overall, this paper provides a blueprint for ML use with clinical data.