Asthma Clinical Trial
Official title:
Prediction of Expected Length of Hospital Stay Using Machine Learning
Verified date | April 2021 |
Source | Brigham and Women's Hospital |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict length of stay throughout a patient's admission. This algorithm was then validated in a validation cohort.
Status | Active, not recruiting |
Enrollment | 500 |
Est. completion date | December 1, 2021 |
Est. primary completion date | August 1, 2021 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database. |
Country | Name | City | State |
---|---|---|---|
United States | Brigham and Women's Faulkner Hospital | Boston | Massachusetts |
United States | Brigham and Women's Hospital | Boston | Massachusetts |
Lead Sponsor | Collaborator |
---|---|
Brigham and Women's Hospital | Biofourmis Inc. |
United States,
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Karnuta JM, Churchill JL, Haeberle HS, Nwachukwu BU, Taylor SA, Ricchetti ET, Ramkumar PN. The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. J Shoulder Elbow Surg. 2020 Nov;29(11):2385-2394. doi: 10.1016/j.jse.2020.04.009. Epub 2020 Jun 9. — View Citation
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Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE. Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models. J Arthroplasty. 2019 Apr;34(4):632-637. doi: 10.1016/j.arth.2018.12.030. Epub 2018 Dec 27. — View Citation
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Young AJ, Hare A, Subramanian M, Weaver JL, Kaufman E, Sims C. Using Machine Learning to Make Predictions in Patients Who Fall. J Surg Res. 2021 Jan;257:118-127. doi: 10.1016/j.jss.2020.07.047. Epub 2020 Aug 18. — View Citation
* Note: There are 11 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Length of Stay | The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours | From date of admission to date of discharge (1 to 24 days) |
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