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A proposal for a new method of evaluation of the newborn
Apgar, V · 1952
Earlier work this paper cites.
A coefficient of agreement for nominal scales
Cohen, J · 1960
Earlier work this paper cites.
Coefficient kappa: Some uses, misuses, and alternatives
Brennan, R. L. & Prediger, D. J · 1981
Earlier work this paper cites.
A simplified acute physiology score for icu patients
Le Gall, J.-R. et al · 1984
Earlier work this paper cites.
The relationship between severity of illness and hospital length of stay and mortality
Horn, S. D. et al · 1991
Earlier work this paper cites.
Simulated neural networks to predict outcomes, costs, and length of stay among orthopedic rehabilitation patients
Grigsby, J., Kooken, R. & Hershberger, J · 1994
Earlier work this paper cites.
A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit
Buchman, T. G., Kubos, K. L., Seidler, A. J. & Siegforth, M. J · 1994
Earlier work this paper cites.
Artificial neural network predictions of lengths of stay on a post-coronary care unit
Mobley, B. A., Leasure, R. & Davidson, L · 1995
Earlier work this paper cites.
Using the future to "sort out" the present: Rankprop and multitask learning for medical risk evaluation
Caruana, R., Baluja, S. & Mitchell, T · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. & Schmidhuber, J · 1997
Earlier work this paper cites.
Predicting survival, length of stay, and cost in the surgical intensive care unit: Apache ii versus iciss
Osler, T. M. et al · 1998
Earlier work this paper cites.
Use of an artificial neural network to predict length of stay in acute pancreatitis
Pofahl, W. E., Walczak, S. M., Rhone, E. & Izenberg, S. D · 1998
Earlier work this paper cites.
Recurrent nets that time and count
Gers, F. A. & Schmidhuber, J · 2000
Earlier work this paper cites.
Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic fmultion models
Clermont, G., Angus, D. C., DiRusso, S. M., Griffin, M. & Linde-Zwirble, W. T · 2001
Earlier work this paper cites.
Validation of a modified early warning score in medical admissions
Subbe, C., Kruger, M., Rutherford, P. & Gemmel, L · 2001
Earlier work this paper cites.
Early indicators of prolonged intensive care unit stay: Impact of illness severity, physician staffing, and pre–intensive care unit length of stay
Higgins, T. L. et al · 2003
Earlier work this paper cites.
Acute physiology and chronic health evaluation (apache) iv: hospital mortality assessment for today’s critically ill patients
Zimmerman, J. E., Kramer, A. A., McNair, D. S. & Malila, F. M · 2006
Earlier work this paper cites.
The relationship between precision-recall and roc curves
Davis, J. & Goadrich, M · 2006
Earlier work this paper cites.
Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit
Paterson, R. et al · 2006
Earlier work this paper cites.
Triage in medicine, part i: concept, history, and types
Iserson, K. V. & Moskop, J. C · 2007
Earlier work this paper cites.
The multitasking clinician: Decision-making and cognitive demand during and after team handoffs in emergency care
Laxmisan, A. et al · 2007
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Collobert, R. & Weston, J · 2008
Earlier work this paper cites.
Overview of biocreative ii gene mention recognition
Smith, L. et al · 2008
Earlier work this paper cites.
Factorial switching linear dynamical systems applied to physiological condition monitoring
Quinn, J. A., Williams, C. K. & McIntosh, N · 2009
Earlier work this paper cites.
Probabilistic detection of short events, with application to critical care monitoring
Aleks, N. et al · 2009
Earlier work this paper cites.
Views – towards a national early warning score for detecting adult inpatient deterioration
Prytherch, D. R., Smith, G. B., Schmidt, P. E. & Featherstone, P. I · 2010
Earlier work this paper cites.
National Early Warning Score (NEWS): Standardising the assessment of acute-illness severity in the NHS
Williams, B. et al · 2012
Cited alongside, same era.
The high cost of low-acuity icu outliers
Dahl, D. et al · 2012
Cited alongside, same era.
A database-driven decision support system: customized mortality prediction
Celi, L. A. et al · 2012
Cited alongside, same era.
Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Silva, I., Moody, G., Scott, D. J., Celi, L. A. & Mark, R. G · 2012
Cited alongside, same era.
Unsupervised pattern discovery in electronic health care data using probabilistic clustering models
Marlin, B. M., Kale, D. C., Khemani, R. G. & Wetzel, R. C · 2012
Cited alongside, same era.
Gaussian process regression in vital-sign early warning systems
Learning genomic representations to predict clinical outcomes in cancer
Yousefi, S., Song, C., Nauata, N. & Cooper, L · 2016
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Deep survival analysis
Ranganath, R., Perotte, A., Elhadad, N. & Blei, D · 2016
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Doctor AI: Predicting clinical events via recurrent neural networks
Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F. & Sun, J · 2016
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Multi-task prediction of disease onsets from longitudinal lab tests
Razavian, N., Marcus, J. & Sontag, D · 2016
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Learning to diagnose with LSTM recurrent neural networks
Lipton, Z. C., Kale, D. C., Elkan, C. & Wetzel, R · 2016
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Learning statistical models of phenotypes using noisy labeled training data
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Clifton, L., Clifton, D. A., Pimentel, M. A., Watkinson, P. J. & Tarassenko, L · 2012
Cited alongside, same era.
Clinical Classifications Software (CCS) for ICD-9-CM fact sheet
2012
Cited alongside, same era.
Watson: beyond jeopardy!
Ferrucci, D., Levas, A., Bagchi, S., Gondek, D. & Mueller, E. T · 2013
Cited alongside, same era.
Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data
Lasko, T. A., Denny, J. C. & Levy, M. A · 2013
Cited alongside, same era.
Introduction to the HCUP National Inpatient Sample (NIS) 2012
2014
Cited alongside, same era.
Big data in health care: using analytics to identify and manage high-risk and high-cost patients
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. & Escobar, G · 2014
Cited alongside, same era.
Selecting quality and resource use measures: A decision guide for community quality collaboratives
Romano, P., Hussey P. & Ritley, D. · 2014
Cited alongside, same era.
Agarwal, V. et al · 2016
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Electronic medical record phenotyping using the anchor and learn framework
Halpern, Y., Horng, S., Choi, Y. & Sontag, D · 2016
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Modeling missing data in clinical time series with rnns
Lipton, Z. C., Kale, D. C. & Wetzel, R · 2016
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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Choi, E. et al · 2016
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Customization of a severity of illness score using local electronic medical record data
Lee, J. & Maslove, D. M · 2017
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Reproducibility in critical care: a mortality prediction case study
Johnson, A., Pollard, T. & Mark, R · 2017
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CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning
Rajpurkar, P. et al · 2017
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Scalable and accurate deep learning with electronic health records
Rajkomar, A. et al · 2018
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Using features from pre-trained timenet for clinical predictions
Gupta, P., Malhotra, P., Vig, L. & Shroff, G · 2018
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Transfer learning for clinical time series analysis using recurrent neural networks
Gupta, P., Malhotra, P., Vig, L. & Shroff, G · 2018
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Improving hospital mortality prediction with medical named entities and multimodal learning
Jin, M. et al · 2018
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Learning to exploit invariances in clinical time-series data using sequence transformer networks
Oh, J., Wang, J. & Wiens, J · 2018
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Learning representations of missing data for predicting patient outcomes
Malone, B., Garcia-Duran, A. & Niepert, M · 2018
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Dynamic measurement scheduling for adverse event forecasting using deep RL
Chang, C.-H., Mai, M. & Goldenberg, A · 2018
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Raim: Recurrent attentive and intensive model of multimodal patient monitoring data
Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O. & Sun, J · 2018
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Mixed effect composite RNN-GP: A personalized and reliable prediction model for healthcare
Chung, I., Kim, S., Lee, J., Hwang, S. J. & Yang, E · 2018
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Spectral capsule networks
Bahadori, M. T · 2018
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Interpretable deep learning framework for predicting all-cause 30-day ICU readmissions
Rafi, P., Pakbin, A. & Pentyala, S. K · 2018
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Attend and diagnose: Clinical time series analysis using attention models
Song, H., Rajan, D., Thiagarajan, J. J. & Spanias, A · 2018
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Benchmarking deep learning models on large healthcare datasets
Purushotham, S., Meng, C., Che, Z. & Liu, Y · 2018
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Zenodo https://doi.org/10.5281/zenodo.1306527 (2018)
Harutyunyan, H. et al. MIMIC-III benchmark repository · 2018
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