Evidence map›Paper›PMID 35702725›Full record

ArticleClinical and translational allergy2022

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

Pierandrea Morandini, Maria Elena Laino, Giovanni Paoletti, Alessandro Carlucci, Tobia Tommasini, Giovanni Angelotti, Jack Pepys, Giorgio Walter Canonica, Enrico Heffler, Victor Savevski and 1 more

Open access · goldAbstract read
In one paragraph

Article in Clinical and translational allergy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Pierandrea MorandiniArtificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.
Maria Elena LainoArtificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.ORCID https://orcid.org/0000-0001-7964-8798
Giovanni PaolettiDepartment of Biomedical Sciences Humanitas University Milan Italy.ORCID https://orcid.org/0000-0003-3953-9225
Alessandro CarlucciArtificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.
Tobia TommasiniArtificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.
Giovanni AngelottiArtificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.
Jack PepysDepartment of Biomedical Sciences Humanitas University Milan Italy.
Giorgio Walter CanonicaDepartment of Biomedical Sciences Humanitas University Milan Italy.
Enrico HefflerDepartment of Biomedical Sciences Humanitas University Milan Italy.
Victor SavevskiArtificial Intelligence Center IRCCS Humanitas Research Hospital Milan Italy.
Francesca PuggioniDepartment of Biomedical Sciences Humanitas University Milan Italy.
IRCCS Humanitas Research Hospital · ITHumanitas University · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

allergyartificial intelligenceasthmaclusteringnatural language processingurticaria

Identifiers

PMID35702725
PMCPMC9175261
OpenAlexW4281631774

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.