The objective of this Bioengineering Research Partnership, established in October 2003, is to develop and evaluate advanced ICU patient monitoring and decision support systems that will improve the efficiency, accuracy, and timeliness of clinical decision-making in critical care. The partnership combines the resources of a powerful interdisciplinary team from academia (MIT), industry (Philips Medical Systems and Philips Research North America), and clinical medicine (Beth Israel Deaconess Medical Center). During the initial funding period of this BRP substantial progress has been achieved, including the development of a massive new research database from more than 30,000 ICU patients (the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II database) and a number of promising advanced monitoring concepts and algorithms. Our initial work has substantiated the hypothesis that sophisticated analysis of the rich multi-parameter data gathered from ICU patients can illuminate their changing pathophysiologic state, and can even provide alerts of impending changes in state. The major goals for the second phase of this BRP are to develop and demonstrate the effectiveness of advanced monitoring concepts and algorithms in laboratory studies utilizing the resources of MIMIC II, and then to carry successful concepts forward into clinical tests in the ICUs of Beth Israel Deaconess Medical Center (BIDMC) and elsewhere with the collaboration of our clinical and industrial partners. We also will enhance the value and availability of the MIMIC II database by adding new adult and neonatal data, designing and improving sophisticated data mining and signal processing tools, and freely distributing to the research community the database and its associated exploration tools via PhysioNet (www.physiionet.org).
The objective of this Bioengineering Research Partnership, established in October 2003, is to develop and evaluate advanced ICU patient monitoring and decision support systems that will improve the efficiency, accuracy, and timeliness of clinical decision-making in critical care.
| Wiens, Jenna; Snyder, Graham M; Finlayson, Samuel et al. (2018) Potential Adverse Effects of Broad-Spectrum Antimicrobial Exposure in the Intensive Care Unit. Open Forum Infect Dis 5:ofx270 |
| Pollard, Tom J; Johnson, Alistair E W; Raffa, Jesse D et al. (2018) The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 5:180178 |
| Li, Taibo; Matsushima, Minoru; Timpson, Wendy et al. (2018) Epidemiology of patient monitoring alarms in the neonatal intensive care unit. J Perinatol 38:1030-1038 |
| Lehman, Li-Wei H; Mark, Roger G; Nemati, Shamim (2018) A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series. IEEE J Biomed Health Inform 22:56-66 |
| Zalewski, Aaron; Long, William; Johnson, Alistair E W et al. (2017) Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text. IEEE EMBS Int Conf Biomed Health Inform 2017:449-452 |
| Dai, Yang; Lokhandwala, Sharukh; Long, William et al. (2017) Phenotyping Hypotensive Patients in Critical Care Using Hospital Discharge Summaries. IEEE EMBS Int Conf Biomed Health Inform 2017:401-404 |
| Marshall, Dominic C; Salciccioli, Justin D; Goodson, Ross J et al. (2017) The association between sodium fluctuations and mortality in surgical patients requiring intensive care. J Crit Care 40:63-68 |
| Lee, Joon; Mark, Roger G; Celi, Leo Anthony et al. (2016) Proton Pump Inhibitors Are Not Associated With Acute Kidney Injury in Critical Illness. J Clin Pharmacol 56:1500-1506 |
| Johnson, Alistair E W; Ghassemi, Mohammad M; Nemati, Shamim et al. (2016) Machine Learning and Decision Support in Critical Care. Proc IEEE Inst Electr Electron Eng 104:444-466 |
| Johnson, Alistair E W; Pollard, Tom J; Shen, Lu et al. (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3:160035 |
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