Multiparameter Intelligent Monitoring in Intensive Care II : A public-access intensive care unit database

By Mohammed Saeed, M. Villarroel, Andrew T. Reisner, G. Clifford, Li-wei H. Lehman, G. Moody, T. Heldt, Tin H. Kyaw, Benjamin Moody, R. G. Mark. 2011

Subfields: Evaluation & Benchmarks, Medical & Health AI

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Citations in the corpus

Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, methodology): “ion like decision rules or tree node features from our mimic methods for better diagnosis interpretability. We will also apply our proposed approaches on a larger healthcare dataset, such as MIMIC-II [38] which is derived from multiple clinical sources, to further verify our methods. We also plan to”
Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020, methodology): “2 MIMIC-II: Mortality Prediction in ICUs Figure 5 shows 16 of the shape functions learned by the Neural Additive Model for the MIMIC-II dataset [Saeed et al., 2011] to predict mortality in intensive care unit (ICUs).”
A Survey on Causal Inference (2020, methodology): “it. The data was sourced from the publicly available MIMIC III database which documents a diverse and very large population of ICU patient stays and contains comprehensive and detailed clinical data. [114]. TCGA. The Cancer Genome Atlas (TCGA) is the world’s largest and richest collection of genomic”
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, methodology): “6) admitted to the medical and surgical at the Beth Israel Deaconess Medical Center (BIDMC, Boston, MA) since 2002 (14) and is freely available on PhysioNet (16).”
Clinical Abbreviation Disambiguation Using Neural Word Embeddings (2015, methodology): “In order to train the neural word embeddings, we utilized the unlabeled clinical notes from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II corpus (Saeed et al., 2011).”
Methods of Blood Pressure Measurement in the ICU (2013, methodology): “Adult patients were selected from the publicly available Multiparameter Intelligent Monitoring for Intensive Care (MIMIC)II Database (20, 21).”
Bidirectional LSTM-CRF for Clinical Concept Extraction (2016, unknown): “the MIMIC-II corpus (Saeed et al., 2011)).”
The BigDAWG Polystore System (2015, methodology): “At the Intel Science and Technology Center for Big Data, we have constructed a medical example of this new class of applications, based on the MIMIC II dataset [18].”
Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach (2016, methodology): “[8], we extracted data for 4470 patients from the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II database [9] that received a heparin intravenous infusion at some point during their ICU stay (randomly assigned to 80% training”
Predicting ICU Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements (2016, methodology): “We use physiologic time series from the MIMIC-II database (Saeed et al. 2011).”