Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Insufficient resources.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Biofourmis Inc. | INDUSTRY |
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| 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. |
Not provided
| Measure | Description | Time Frame |
|---|---|---|
| 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) |
Not provided
Not provided
Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
Not provided
Not provided
Not provided
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.
Not provided
| 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 |
|---|---|---|---|
| 32179154 | Background | Lubelski D, Ehresman J, Feghali J, Tanenbaum J, Bydon A, Theodore N, Witham T, Sciubba DM. Prediction calculator for nonroutine discharge and length of stay after spine surgery. Spine J. 2020 Jul;20(7):1154-1158. doi: 10.1016/j.spinee.2020.02.022. Epub 2020 Mar 13. | |
| 32713541 | Background | 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. |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Boston |
| Massachusetts |
| 02130 |
| United States |
| 30665831 | Background | 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. |
| 31765937 | Background | Ma X, Si Y, Wang Z, Wang Y. Length of stay prediction for ICU patients using individualized single classification algorithm. Comput Methods Programs Biomed. 2020 Apr;186:105224. doi: 10.1016/j.cmpb.2019.105224. Epub 2019 Nov 20. |
| 30685103 | Background | Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. Int J Cardiol. 2019 Aug 1;288:140-147. doi: 10.1016/j.ijcard.2019.01.046. Epub 2019 Jan 19. |
| 33070874 | Background | Bacchi S, Oakden-Rayner L, Menon DK, Jannes J, Kleinig T, Koblar S. Stroke prognostication for discharge planning with machine learning: A derivation study. J Clin Neurosci. 2020 Sep;79:100-103. doi: 10.1016/j.jocn.2020.07.046. Epub 2020 Aug 5. |
| 30243882 | Background | Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM, Ramkumar PN. Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model. J Arthroplasty. 2018 Dec;33(12):3617-3623. doi: 10.1016/j.arth.2018.08.028. Epub 2018 Sep 5. |
| 32823009 | Background | 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. |
| 32376173 | Background | Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. J Vasc Interv Radiol. 2020 Jun;31(6):1018-1024.e4. doi: 10.1016/j.jvir.2019.11.030. Epub 2020 May 4. |
| 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. |
| 32835314 | Background | Nemati M, Ansary J, Nemati N. Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data. Patterns (N Y). 2020 Aug 14;1(5):100074. doi: 10.1016/j.patter.2020.100074. Epub 2020 Jul 4. |
| 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 |
| 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 |
Not provided
Not provided