Crohn Disease Clinical Trial
— INTUITION-CPFOfficial title:
Use of Natural Language Processing (NLP) and Machine Learning (ML) for the Identification of Patients With Crohn's Disease (CD) and Complex Perianal Fistulas (CPF) and Their Characterization in Terms of Clinical and Demographic Characteristics. A Multicentre, Retrospective, NLP Based Study
Verified date | May 2024 |
Source | Takeda |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
Natural Language Processing and machine learning are examples of artificial intelligence tools. This study will check if these tools correctly identify people with Crohn's disease with complex perianal fistulas from their medical records.
Status | Completed |
Enrollment | 32 |
Est. completion date | April 29, 2024 |
Est. primary completion date | February 27, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: 1. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021. Exclusion Criteria: Not applicable. |
Country | Name | City | State |
---|---|---|---|
Spain | Hospital del Mar | Barcelona | Cataluna |
Spain | Hospital Universitario Fundacion Alcorcon | Madrid | Comunidad De Madrid |
Spain | Hospital Universitario Son Espases | Palma | Islas Baleares |
Lead Sponsor | Collaborator |
---|---|
Takeda |
Spain,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Percentage of Participants With CD and CPF Accurately Identified With the use of NLP and Medical Language (MEL) | Percentage of participants will be measured in terms of accuracy and precision (sensitivity and specificity) of the "algorithm" used to identify participants with CPF associated with CD. Data obtained through the artificial intelligence (AI) technology will be compared with data obtained through traditional electronic data capture (EDC) and source data verification methods. | Up to Month 36 | |
Secondary | Number of Participants With CD and CPF Characterized Using NLP and Machine Learning Techniques | The following information at the moment of CPF diagnosis will be extracted from the electronical medical records (EMRs): age, gender, date of diagnosis of CPF, smoking status, date of diagnosis of CD, luminal disease characteristics (localization, behaviour and activity) at diagnosis, treatments (medical and surgical) established for luminal disease in the study period, treatments (medical and surgical) established for CPF since first occurrence, fistula characteristics at diagnosis: type of fistula (following American Gastroenterological Association [AGA] classification) number of fistula internal and external openings, fistula activity. | Up to Month 36 |
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