2026-09-18T22:56:22Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1061252026-06-06T00:48:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Thomas Heldt and George Verghese.
Dunitz, Max (Max H.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T16:30:00Z
2016-12-22T16:30:00Z
2016
2016
http://hdl.handle.net/1721.1/106125
965830764
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-127).
Sepsis, which occurs when an infection leads to a systemic inflammatory response, is believed to contribute to one in two to three hospital deaths in the United States. Using the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database of electronic medical records from Boston's Beth Israel Deaconess Medical Center (BIDMC), we worked to characterize sepsis at BIDMC's intensive care units (ICUs). Additionally, we developed a real-time algorithm to stratify patients with infectious complaints into different risk categories for progressing to septic shock. From arterial blood pressure waveform trends collected from bedside monitors and readily available among patients with an arterial catheter, high-resolution time signals of heart rate and arterial blood pressure measurements, as well as estimates of cardiac output and total peripheral resistance, we developed a variety of classifiers to place patients in risk categories based on serum lactate levels, a proxy for hypoperfusion and imminent circulatory shock.
by Max Dunitz.
M.Eng.
127 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Predicting hyperlactatemia in the ICU
Predicting hyperlactatemia in the intensive care unit
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>Sepsis, which occurs when an infection leads to a systemic inflammatory response, is believed to contribute to one in two to three hospital deaths in the United States. Using the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database of electronic medical records from Boston's Beth Israel Deaconess Medical Center (BIDMC), we worked to characterize sepsis at BIDMC's intensive care units (ICUs). Additionally, we developed a real-time algorithm to stratify patients with infectious complaints into different risk categories for progressing to septic shock. From arterial blood pressure waveform trends collected from bedside monitors and readily available among patients with an arterial catheter, high-resolution time signals of heart rate and arterial blood pressure measurements, as well as estimates of cardiac output and total peripheral resistance, we developed a variety of classifiers to place patients in risk categories based on serum lactate levels, a proxy for hypoperfusion and imminent circulatory shock.</Abstract>
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