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| Name | Class |
|---|---|
| Biofourmis Inc. | INDUSTRY |
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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 the likelihood of 30-day readmission throughout a patient's admission. This algorithm was then validated in a validation cohort.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training | A subset of patients that are used to train the machine learning algorithm. | ||
| Validation | A subset of patients that are "held back" and used to validate the algorithm's accuracy. |
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| Measure | Description | Time Frame |
|---|---|---|
| 30-Day Readmission [ yes / no ] | Unplanned hospital admission within 30 days of having been discharged | From date of admission to 30-days post-discharge (31 to 54 days) |
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Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
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Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and residence within 5 mile requirements and are enrolled in home hospital.
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| Name | Affiliation | Role |
|---|---|---|
| David Levine, MD MPH MA | Associate Physician | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Brigham and Women's Hospital | Boston | Massachusetts | 02115 | United States | ||
| Brigham and Women's Faulkner Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30803914 | Background | Merrill RK, Ferrandino RM, Hoffman R, Shaffer GW, Ndu A. Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures. J Foot Ankle Surg. 2019 May;58(3):410-416. doi: 10.1053/j.jfas.2018.09.004. Epub 2019 Feb 23. | |
| 33032774 | Background | Li Q, Yao X, Echevin D. How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data. Value Health. 2020 Oct;23(10):1307-1315. doi: 10.1016/j.jval.2020.06.009. Epub 2020 Sep 7. |
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| ID | Term |
|---|---|
| D007239 | Infections |
| D006333 | Heart Failure |
| D029424 | Pulmonary Disease, Chronic Obstructive |
| D001249 | Asthma |
| D051436 | Renal Insufficiency, Chronic |
| D000096003 | Hypertensive Crisis |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
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| Boston |
| Massachusetts |
| 02130 |
| United States |
| 30213670 | Background | Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019 Apr;95:27-37. doi: 10.1016/j.artmed.2018.08.004. Epub 2018 Sep 10. |
| 32353752 | Background | Morel D, Yu KC, Liu-Ferrara A, Caceres-Suriel AJ, Kurtz SG, Tabak YP. Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach. Int J Med Inform. 2020 Jul;139:104136. doi: 10.1016/j.ijmedinf.2020.104136. Epub 2020 Apr 18. |
| 32174313 | Background | Loreto M, Lisboa T, Moreira VP. Early prediction of ICU readmissions using classification algorithms. Comput Biol Med. 2020 Mar;118:103636. doi: 10.1016/j.compbiomed.2020.103636. Epub 2020 Feb 1. |
| 32711985 | Background | Bolourani S, Tayebi MA, Diao L, Wang P, Patel V, Manetta F, Lee PC. Using machine learning to predict early readmission following esophagectomy. J Thorac Cardiovasc Surg. 2021 Jun;161(6):1926-1939.e8. doi: 10.1016/j.jtcvs.2020.04.172. Epub 2020 May 29. |
| 32868011 | Background | Arvind V, London DA, Cirino C, Keswani A, Cagle PJ. Comparison of machine learning techniques to predict unplanned readmission following total shoulder arthroplasty. J Shoulder Elbow Surg. 2021 Feb;30(2):e50-e59. doi: 10.1016/j.jse.2020.05.013. Epub 2020 Jun 9. |
| D012140 |
| Respiratory Tract Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D001982 | Bronchial Diseases |
| D012130 | Respiratory Hypersensitivity |
| D006969 | Hypersensitivity, Immediate |
| D006967 | Hypersensitivity |
| D007154 | Immune System Diseases |
| D051437 | Renal Insufficiency |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D006973 | Hypertension |
| D014652 | Vascular Diseases |