NLP to Aid in the Evaluation and Diagnosis of FGIDs
Study Details
Study Description
Brief Summary
The study has two arms, where the same natural language processing (NLP) and probabilistic graphical modeling technology will be utilized on patients' report of symptoms in both arms. The clinical arm is focused on patients presenting for consultation with a gastroenterologist. The endoscopy arm is focused generally on patients presenting for a diagnostic endoscopy, with the goal of capturing Functional Gastrointestinal Disorder (FGID) patients prior to diagnosis.
Condition or Disease | Intervention/Treatment | Phase |
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Study Design
Arms and Interventions
Arm | Intervention/Treatment |
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Patients scheduled for consult with gastroenterologist
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Patients scheduled for diagnostic endoscopy
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Outcome Measures
Primary Outcome Measures
- Latent themes present in patient descriptions of FGIDs symptoms [08/09/2018-08/09/2023]
Latent themes present in patient descriptions of FGIDs symptoms as generated by machine learning as well as quantitative comparisons to traditional metrics of patient descriptions including Rome IV criteria and patient descriptions of severity.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Scheduled for consult in the Motility Clinic in the MGH Gastroenterology Unit or for diagnostic endoscopy in the MGH Gastroenterology Unit
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Patient must agree to have their interactions audio-recorded
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Informed consent form signed by the subjects
Exclusion Criteria:
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Non-native English speaker
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Patients unable to communicate their own symptoms
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | Massachusetts General Hospital | Boston | Massachusetts | United States | 02114 |
Sponsors and Collaborators
- Massachusetts General Hospital
- Massachusetts Institute of Technology
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2018P001542