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人工智能处理电子健康记录以识别米兰胡曼itas免疫中心的共性和共病聚类。

Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas.

作者信息

Morandini Pierandrea, Laino Maria Elena, Paoletti Giovanni, Carlucci Alessandro, Tommasini Tobia, Angelotti Giovanni, Pepys Jack, Canonica Giorgio Walter, Heffler Enrico, Savevski Victor, Puggioni Francesca

机构信息

Artificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.

Department of Biomedical Sciences Humanitas University Milan Italy.

出版信息

Clin Transl Allergy. 2022 Jun 8;12(6):e12144. doi: 10.1002/clt2.12144. eCollection 2022 Jun.

Abstract

BACKGROUND

Comorbidities are common in chronic inflammatory conditions, requiring multidisciplinary treatment approach. Understanding the link between a single disease and its comorbidities is important for appropriate treatment and management. We evaluate the ability of an NLP-based process for knowledge discovery to detect information about pathologies, patients' phenotype, doctors' prescriptions and commonalities in electronic medical records, by extracting information from free narrative text written by clinicians during medical visits, resulting in the extraction of valuable information and enriching real world evidence data from a multidisciplinary setting.

METHODS

We collected clinical notes from the Allergy Department of Humanitas Research Hospital written in the last 3 years and used it to look for diseases that cluster together as comorbidities associated to the main pathology of our patients, and for the extent of prescription of systemic corticosteroids, thus evaluating the ability of NLP-based tools for knowledge discovery to extract structured information from free text.

RESULTS

We found that the 3 most frequent comorbidities to appear in our clusters were asthma, rhinitis, and urticaria, and that 991 (of 2057) patients suffered from at least one of these comorbidities. The clusters which co-occur particularly often are oral allergy syndrome and urticaria (131 patients), angioedema and urticaria (105 patients), rhinitis and asthma (227 patients). With regards to systemic corticosteroid prescription volume by our clinicians, we found it was lower when compared to the therapy the patients followed before coming to our attention, with the exception of two diseases: Chronic obstructive pulmonary disease and Angioedema.

CONCLUSIONS

This analysis seems to be valid and is confirmed by the data from the literature. This means that NLP tools could have significant role in many other research fields of medicine, as it may help identify other important, and possibly previously neglected clusters of patients with comorbidities and commonalities. Another potential benefit of this approach lies in its potential ability to foster a multidisciplinary approach, using the same drugs to treat pathologies normally treated by physicians in different branches of medicine, thus saving resources and improving the pharmacological management of patients.

摘要

背景

合并症在慢性炎症性疾病中很常见,需要多学科治疗方法。了解单一疾病与其合并症之间的联系对于适当的治疗和管理很重要。我们评估基于自然语言处理(NLP)的知识发现过程从电子病历中检测有关病理、患者表型、医生处方和共性信息的能力,通过从临床医生在就诊期间撰写的自由叙述文本中提取信息,从而从多学科环境中提取有价值的信息并丰富真实世界证据数据。

方法

我们收集了过去3年里在胡曼itas研究医院过敏科撰写的临床记录,并用于寻找作为与我们患者主要病理相关的合并症而聚集在一起的疾病,以及全身用糖皮质激素的处方范围,从而评估基于NLP的知识发现工具从自由文本中提取结构化信息的能力。

结果

我们发现出现在我们聚类中的3种最常见合并症是哮喘、鼻炎和荨麻疹,并且2057名患者中有991名患有这些合并症中的至少一种。特别经常同时出现的聚类是口腔过敏综合征和荨麻疹(131名患者)、血管性水肿和荨麻疹(105名患者)、鼻炎和哮喘(227名患者)。关于我们临床医生的全身用糖皮质激素处方量,我们发现与患者在引起我们注意之前所遵循的治疗相比,其较低,但慢性阻塞性肺疾病和血管性水肿这两种疾病除外。

结论

该分析似乎是有效的,并得到了文献数据的证实。这意味着NLP工具在医学的许多其他研究领域可能具有重要作用,因为它可能有助于识别其他重要的、可能以前被忽视的合并症和共性患者聚类。这种方法的另一个潜在好处在于其潜在能力,即促进多学科方法,使用相同的药物来治疗通常由不同医学分支的医生治疗的病症,从而节省资源并改善患者的药物管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/204d/9175261/4607e9d84edf/CLT2-12-e12144-g005.jpg

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