Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System
We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one's own institution whose features are similar to an index patient as regards an outcome of interest, in order to predict the utility of diagnostic tests an...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Korean Institute of Information Scientists and Engineers
2012
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| Online Access: | http://hdl.handle.net/1721.1/70971 https://orcid.org/0000-0001-8593-9321 https://orcid.org/0000-0002-6318-2978 https://orcid.org/0000-0002-6554-061X |
| _version_ | 1826211508485357568 |
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| author | Celi, Leo Anthony G. Mark, Roger Greenwood Lee, Joon Scott, Daniel Panch, Trishan |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Celi, Leo Anthony G. Mark, Roger Greenwood Lee, Joon Scott, Daniel Panch, Trishan |
| author_sort | Celi, Leo Anthony G. |
| collection | MIT |
| description | We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one's own institution whose features are similar to an index patient as regards an outcome of interest, in order to predict the utility of diagnostic tests and interventions, as well as inform prognosis. The Laboratory of Computational Physiology at the Massachusetts Institute of Technology developed and maintains MIMIC-II, a public deidentified high- resolution database of patients admitted to Beth Israel Deaconess Medical Center. It hosts teams of clinicians (nurses, doctors, pharmacists) and scientists (database engineers, modelers, epidemiologists) who translate the day-to-day questions during rounds that have no clear answers in the current medical literature into study designs, perform the modeling and the analysis and publish their findings. The studies fall into the following broad categories: identification and interrogation of practice variation, predictive modeling of clinical outcomes within patient subsets and comparative effectiveness research on diagnostic tests and therapeutic interventions. Clinical databases such as MIMIC-II, where recorded health care transactions - clinical decisions linked with patient outcomes - are constantly uploaded, become the centerpiece of a learning system. |
| first_indexed | 2024-09-23T15:07:06Z |
| format | Article |
| id | mit-1721.1/70971 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:07:06Z |
| publishDate | 2012 |
| publisher | Korean Institute of Information Scientists and Engineers |
| record_format | dspace |
| spelling | mit-1721.1/709712022-10-02T00:41:14Z Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System Celi, Leo Anthony G. Mark, Roger Greenwood Lee, Joon Scott, Daniel Panch, Trishan Harvard University--MIT Division of Health Sciences and Technology Celi, Leo Anthony G. Celi, Leo Anthony G. Mark, Roger Greenwood Lee, Joon Scott, Daniel Panch, Trishan We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one's own institution whose features are similar to an index patient as regards an outcome of interest, in order to predict the utility of diagnostic tests and interventions, as well as inform prognosis. The Laboratory of Computational Physiology at the Massachusetts Institute of Technology developed and maintains MIMIC-II, a public deidentified high- resolution database of patients admitted to Beth Israel Deaconess Medical Center. It hosts teams of clinicians (nurses, doctors, pharmacists) and scientists (database engineers, modelers, epidemiologists) who translate the day-to-day questions during rounds that have no clear answers in the current medical literature into study designs, perform the modeling and the analysis and publish their findings. The studies fall into the following broad categories: identification and interrogation of practice variation, predictive modeling of clinical outcomes within patient subsets and comparative effectiveness research on diagnostic tests and therapeutic interventions. Clinical databases such as MIMIC-II, where recorded health care transactions - clinical decisions linked with patient outcomes - are constantly uploaded, become the centerpiece of a learning system. National Space Biomedical Research Institute (grant R01 EB001659) Massachusetts Institute of Technology Beth Israel Deaconess Medical Center Philips Healthcare Nederland 2012-05-31T20:28:18Z 2012-05-31T20:28:18Z 2012-03 2011-12 Article http://purl.org/eprint/type/JournalArticle 1976-4677 2093-8020 http://hdl.handle.net/1721.1/70971 Celi, Leo A. et al. “Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System.” Journal of Computing Science and Engineering 6.1 (2012): 51–59. Web. https://orcid.org/0000-0001-8593-9321 https://orcid.org/0000-0002-6318-2978 https://orcid.org/0000-0002-6554-061X en_US http://dx.doi.org/10.5626/JCSE.2012.6.1.51 Journal of Computing Science and Engineering Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Korean Institute of Information Scientists and Engineers Korean Institute of Information Scientists and Engineers |
| spellingShingle | Celi, Leo Anthony G. Mark, Roger Greenwood Lee, Joon Scott, Daniel Panch, Trishan Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System |
| title | Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System |
| title_full | Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System |
| title_fullStr | Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System |
| title_full_unstemmed | Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System |
| title_short | Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System |
| title_sort | collective experience a database fuelled inter disciplinary team led learning system |
| url | http://hdl.handle.net/1721.1/70971 https://orcid.org/0000-0001-8593-9321 https://orcid.org/0000-0002-6318-2978 https://orcid.org/0000-0002-6554-061X |
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