Aug. 17, 2020
We are delighted to announce that MIMIC-IV has been published on PhysioNet! MIMIC-IV, the latest version of MIMIC, is a database comprising comprehensive clinical information on hospital stays for patients admitted to a tertiary academic medical center in Boston, MA, USA.
Major changes from MIMIC-III include: (1) a modular structure that links core hospital data to multiple data sources, including chest x-ray images; (2) an approach to date shifting that provides approximate year of admission; (3) new sources of data; such as the electronic medicine administration record.
The dataset is available from PhysioNet, and access is managed in the same way as MIMIC-III. If you already have access to MIMIC-III, then you will be granted access after signing the Data Use Agreement in the "Files" section of the project. New users will need to complete the credentialing process first (see: https://mimic-iv.mit.edu/docs/access/ for more details). For detailed guidelines on using MIMIC-IV, see the documentation!
Aug. 11, 2020
The MIT Laboratory for Computational Physiology is seeking a Post-Doctoral Associate to conduct independent research in health care informatics. The Laboratory is an NIH-supported multi-disciplinary group of clinicians, data scientists and engineers that produced the publicly distributed and growing MIMIC database. It is a rich and open research resource that supports signal processing and machine learning research leading to new knowledge and patient-specific prognostic and therapeutic guidance for critical care.
The Postdoc will contribute to the design and management of the current and future MIMIC databases, and will conduct multidisciplinary original research together with clinicians. The position provides the opportunity to interact with a world-class laboratory comprised of engineers, mathematicians and clinical staff working at the frontiers of translational medicine and advanced research in the domain of critical care informatics and machine learning. The Research Fellow will both contribute to ongoing research projects and propose innovative new projects suitable for research grant funding.
The ideal candidate has a doctoral degree in science or engineering, or a related discipline to assure high level understanding of the research environment. Experience in relational database development and administration is important, ideally in a medical environment. Competence and experience in a subset of the following is expected: Linux, Python, data management. Knowledge of medical terminology is desirable. Strong interpersonal and communication skills are essential. For more details, send a letter of interest and CV to Prof. Roger Mark at: rgmark AT mit.edu.
June 5, 2020
We are pleased to announce the release of the HiRID critical care dataset, developed as part of a collaboration between Bern University Hospital and the Swiss Federal Institute of Technology (ETH). HiRID is a freely accessible critical care dataset containing data relating to more than 33 thousand admissions to the Department of Intensive Care Medicine of the Bern University Hospital, Switzerland, an interdisciplinary 60-bed unit admitting >6,500 patients per year.
Read more: https://physionet.org/content/hirid/
March 25, 2020
We are currently dealing with a high volume of applications for credentialed access to PhysioNet, so please expect significant delays in the review process. We are doing our best to deal with the waitlist quickly, handling applications in the order in which they are received. To help ensure that your application is successful, please remember to:
- Include a copy of your CITI training report (not the certificate).
- Remind your reference to reply promptly when contacted.
- Check your application details are correct before submitting.
We apologize for the inconvenience. Please bear with us during this busy time!
News from: WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction v1.0.0.
March 2, 2020
Congratulations to the winners of the 2020 Women in Data Science (WiDS) Challenge. The event attracted 951 teams from over 80 countries who competed on models to predict the outcome of critically ill patients. The dataset used in the study, sourced from the GOSSIS (Global Open Source Severity of Illness Score) Consortium, comprised detailed information on over 130,000 ICU stays. For updates on future challenges by WiDS, subscribe to the WiDS mailing list.