{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/33105"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/33105","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"The Annotation Station : an open source technology for data visualization and annotation of large biomedical databases","abstract":"Over the past 20 years, there has been a large ongoing effort in the Laboratory for Computational Physiology (LCP) to collect and annotate large databases of physiologic signals in order to facilitate an open initiative to develop algorithms to automate a variety of clinical tasks [1]. The success of this approach motivated the collection of a temporal database of signals typically collected in the intensive care unit (ICU) from the Beth Israel Deaconess Medical Center (BIDMC), known as the Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC) database [11]. Although certain basic clinical information was recorded about the patients in this database, much of the information available from the ICU, such as nursing notes, were not recorded. Approximately 5 years ago, the LCP began an initiative to collect a massive temporal database consisting of almost all the information available in the BIDMC ICU [3] known as MIMIC II. This database not only consists of the beside monitor waveforms collected in the original MIMIC database, but also most of the sources of information available to the clinical staff, ranging from medication drip rates and fluid balances, to subjective evaluation scales and discharge and clinical progress notes detailing patient interventions, reactions, clinical visits and past medical history.","abstract_html":"Over the past 20 years, there has been a large ongoing effort in the Laboratory for Computational Physiology (LCP) to collect and annotate large databases of physiologic signals in order to facilitate an open initiative to develop algorithms to automate a variety of clinical tasks [1]. The success of this approach motivated the collection of a temporal database of signals typically collected in the intensive care unit (ICU) from the Beth Israel Deaconess Medical Center (BIDMC), known as the Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC) database [11]. 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This database not only consists of the beside monitor waveforms collected in the original MIMIC database, but also most of the sources of information available to the clinical staff, ranging from medication drip rates and fluid balances, to subjective evaluation scales and discharge and clinical progress notes detailing patient interventions, reactions, clinical visits and past medical history.","abstract_has_math":false,"creators":["Abdala, Omar T"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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