Publications citing MIMIC-III Waveform Database Matched Subset v1.0

Generalized Zero-shot ICD Coding
Congzheng Song, Shanghang Zhang, Najmeh Sadoughi et al. arXiv (Cornell University), 2019. Cites: paper

An awakening in medicine: the partnership of humanity and intelligent machines
Leo Anthony Celi, Benjamin Fine, David J. Stone. The Lancet Digital Health, 2019. Cites: paper

DC3 -- A Diagnostic Case Challenge Collection for Clinical Decision Support
Carsten Eickhoff, Floran Gmehlin, Anu V. Patel et al. ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR), 2019. Cites: paper

Medical device surveillance with electronic health records
Alison Callahan, Jason Fries, Christopher Ré et al. npj Digital Medicine, 2019. Cites: paper

GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
Maksim Belousov, Nikola Milošević, Ghada Alfattni et al. arXiv (Cornell University), 2019. Cites: paper

AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction
Weitong Chen, Guodong Long, Lina Yao et al. World Wide Web, 2019. Cites: paper

Generating Evidence for Chronic Obstructive Pulmonary Disease (COPD) Clinical Guidelines Using EHR Data
Amber M. Johnson, Marvi Bikak, Paul M. Griffin et al. medRxiv, 2019. Cites: paper

Machine learning based prediction of antibiotic sensitivity in patients with critical illness
Debabrata Ghosh, Shivam Sharma, Eeshan Hasan et al. medRxiv, 2019. Cites: paper

Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database
Zhongheng Zhang, Min Yao, Kwok Ming Ho et al. Scientific Reports, 2019. Cites: paper

Representation Learning for Electronic Health Records
Wei‐Hung Weng, Peter Szolovits B. arXiv (Cornell University), 2019. Cites: paper

Red Blood Cell Distribution Width Is Associated with All-Cause Mortality in Critically Ill Patients with Cardiogenic Shock
Benji Wang, Gulandanmu Aihemaiti, Bihuan Cheng et al. Medical Science Monitor, 2019. Cites: paper

Elevated urea-to-creatinine ratio provides a biochemical signature of muscle catabolism and persistent critical illness after major trauma
Ryan W. Haines, Parjam Zolfaghari, Yize I. Wan et al. Intensive Care Medicine, 2019. Cites: paper

Factors associated with outcomes of septic shock patients receiving high dose noradrenaline according to three primary infection sites
Kang-song Wu, Danyan Gu, Tingting Wang et al. Journal of International Medical Research, 2019. Cites: paper

Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning
Deasy, Jacob, Ari Ercole, Píetro Lió. arXiv (Cornell University), 2019. Cites: paper

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications
Nathan Peiffer‐Smadja, Timothy M. Rawson, Raheelah Ahmad et al. Clinical Microbiology and Infection, 2019. Cites: paper

Toward Mitigating Adversarial Texts
Basemah Alshemali, Jugal Kumar Kalita. International Journal of Computer Applications, 2019. Cites: paper

A Hybrid Machine Learning Framework to Predict Early Risk of Mortality in Paralytic Ileus Patients using Electronic Health Records
Fahad Shabbir Ahmed, Liaqat Ali, Raza Ul Mustafa et al. medRxiv, 2019. Cites: paper

Deep Augmented Multiview Clustering
Ragunathan Mariappan, Jiang Liu, Vaibhav Rajan. bioRxiv (Cold Spring Harbor Laboratory), 2019. Cites: paper

Machine learning in healthcare -- a system's perspective
Ashfaq, Awais, Sławomir Nowaczyk. arXiv (Cornell University), 2019. Cites: paper

Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
Deasy, Jacob, Píetro Lió, Ari Ercole. arXiv (Cornell University), 2019. Cites: paper

Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls
Jose Fernando Rodrigues Junior, Gabriel Spadon, Brandoli, Bruno et al. arXiv (Cornell University), 2019. Cites: paper

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review
Antje Wulff, Sara Montag, Michael Marschollek et al. Methods of Information in Medicine, 2019. Cites: paper

Critical Transitions in Intensive Care Units: A Sepsis Case Study
Pejman Farhadi Ghalati, Satya Swarup Samal, Jayesh Sudhir Bhat et al. Scientific Reports, 2019. Cites: paper

Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data
Yu-Ting Lin, Yu‐Lun Lo, Chen-Yun Lin et al. PLoS ONE, 2019. Cites: paper

A privacy-preserving distributed filtering framework for NLP artifacts
Md Nazmus Sadat, Md Momin Al Aziz, Noman Mohammed et al. BMC Medical Informatics and Decision Making, 2019. Cites: paper

Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes
Jonas Kemp, Alvin Rajkomar, Andrew M. Dai. arXiv (Cornell University), 2019. Cites: paper

Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning
Eric M. Cramer, Martin G. Seneviratne, Husham Sharifi et al. eGEMs (Generating Evidence & Methods to improve patient outcomes), 2019. Cites: paper

Using the National Early Warning Score (NEWS/NEWS 2) in different Intensive Care Units (ICUs) to predict the discharge location of patients
Hassan Zaidi, Mohamed Bahy Bader-El-Den, James McNicholas. BMC Public Health, 2019. Cites: paper

Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding
Jianliang Yang, Yuenan Liu, Qian Minghui et al. Applied Sciences, 2019. Cites: paper

Learning Electronic Health Records through Hyperbolic Embedding of Medical Ontologies
Qiuhao Lu, Nisansa de Silva, Sabin Kafle et al. ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2019. Cites: paper

PEARL
Tianfan Fu, Tian Gao, Cao Xiao et al. ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2019. Cites: paper

Pleural Effusion Outcomes in Intensive Care: Analysis of a Large Clinical Database
Marjorie E. Bateman, Ala L. Alkhatib, Thomas John et al. Journal of Intensive Care Medicine, 2019. Cites: paper

Time-series as Background Data for Relating Medical Diagnoses Terms
Saket Gurukar, Srikanta Bedathur. ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2019. Cites: paper

Digital Health and the State of Interoperable Electronic Health Records
Jessica Germaine Shull. JMIR Medical Informatics, 2019. Cites: paper

Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research
Nicholas J Dobbins, Clifford H Spital, Robert A. Black et al. Journal of the American Medical Informatics Association, 2019. Cites: paper

Parameterizing neural networks for disease classification
Guryash Bahra, Lena Wiese. Expert Systems, 2019. Cites: paper

Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements
Ahmed Youssef Ali Amer, Julie Vranken, Femke Wouters et al. Applied Sciences, 2019. Cites: paper

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
Sam Henry, Kevin Buchan, Michele Filannino et al. Journal of the American Medical Informatics Association, 2019. Cites: paper

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review
Duncan Shillan, Jonathan A C Sterne, ALAN R. CHAMPNEYS et al. Critical Care, 2019. Cites: paper

A nomogram for predicting the risk of sepsis in patients with acute cholangitis
Qingqing Liu, Quan Zhou, Meina Song et al. Journal of International Medical Research, 2019. Cites: paper

Data-Driven Lexical Normalization for Medical Social Media
Anne R. Dirkson, Suzan Verberne, Abeed Sarker et al. Multimodal Technologies and Interaction, 2019. Cites: paper

Development and implementation of a dynamically updated big data intelligence platform from electronic health records for nasopharyngeal carcinoma research
Lin Li, Wei Liang, Chaofeng Li et al. British Journal of Radiology, 2019. Cites: paper

Predicting sepsis with a recurrent neural network using the MIMIC III database
Matthieu Scherpf, Felix Gräßer, Hagen Malberg et al. Computers in Biology and Medicine, 2019. Cites: paper

Curation of an intensive care research dataset from routinely collected patient data in an NHS trust.
Chris McWilliams, Joshua Inoue, Philip Wadey et al. F1000Research, 2019. Cites: paper

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
William Caicedo-Torres, Jairo Gutiérrez. Journal of Biomedical Informatics, 2019. Cites: paper

The “inconvenient truth” about AI in healthcare
Trishan Panch, Heather Mattie, Leo Anthony Celi. npj Digital Medicine, 2019. Cites: paper

Extracting entities with attributes in clinical text via joint deep learning
Xue Shi, Yingping Yi, Ying Xiong et al. Journal of the American Medical Informatics Association, 2019. Cites: paper

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Mike Wu, Sonali Parbhoo, Michael C. Hughes et al. arXiv (Cornell University), 2019. Cites: paper

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner et al. arXiv (Cornell University), 2019. Cites: paper

Red blood cell distribution width provides additional prognostic value beyond severity scores in adult critical illness
Yanqiu Han, Li Yan, Lei Zhang et al. Clinica Chimica Acta, 2019. Cites: paper

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