Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

[1]  Luca Longo,et al.  Notions of explainability and evaluation approaches for explainable artificial intelligence , 2021, Inf. Fusion.

[2]  Sitthichok Chaichulee,et al.  Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients , 2021, Journal of personalized medicine.

[3]  M. Salukvadze,et al.  Compendium , 2020, The Golden Rule of Ethics.

[4]  Pietro Liò,et al.  Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation , 2020, Scientific Reports.

[5]  Søren Brunak,et al.  Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. , 2020, The Lancet. Digital health.

[6]  G. Gutierrez Artificial Intelligence in the Intensive Care Unit , 2020, Critical Care.

[7]  Jacob Deasy,et al.  Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning , 2019, ArXiv.

[8]  Ida Scheel,et al.  Time-to-Event Prediction with Neural Networks and Cox Regression , 2019, J. Mach. Learn. Res..

[9]  S. Brunak,et al.  Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records. , 2019, The Lancet. Digital health.

[10]  R. Courtland Bias detectives: the researchers striving to make algorithms fair , 2018, Nature.

[11]  Balasubramanian Narasimhan,et al.  A scalable discrete-time survival model for neural networks , 2018, PeerJ.

[12]  Jeffrey Dean,et al.  Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.

[13]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[14]  Cheng Guo,et al.  Entity Embeddings of Categorical Variables , 2016, ArXiv.

[15]  Jason P. Fine,et al.  Statistical Primer for Cardiovascular Research Introduction to the Analysis of Survival Data in the Presence of Competing Risks , 2022 .

[16]  Sigrun Alba Johannesdottir Schmidt,et al.  The Danish National Patient Registry: a review of content, data quality, and research potential , 2015, Clinical epidemiology.

[17]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[18]  M. Soares,et al.  ICU severity of illness scores: APACHE, SAPS and MPM , 2014, Current opinion in critical care.

[19]  Henrik Toft Sørensen,et al.  The Danish Civil Registration System as a tool in epidemiology , 2014, European Journal of Epidemiology.

[20]  A. Rapsang,et al.  Scoring systems in the intensive care unit: A compendium , 2014, Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine.

[21]  H. Brisse,et al.  Results of a multicenter prospective study on the postoperative treatment of unilateral retinoblastoma after primary enucleation. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  D. Kahneman,et al.  Before you make that big decision... , 2011, Harvard business review.

[23]  Gavin C. Cawley,et al.  On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..

[24]  Hemant Ishwaran,et al.  Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.

[25]  Elia Biganzoli,et al.  A time‐dependent discrimination index for survival data , 2005, Statistics in medicine.

[26]  Lionel Tarassenko,et al.  Non‐linear survival analysis using neural networks , 2004, Statistics in medicine.

[27]  K. Carroll,et al.  On the use and utility of the Weibull model in the analysis of survival data. , 2003, Controlled clinical trials.

[28]  T. Osler,et al.  Identifying quality outliers in a large, multiple-institution database by using customized versions of the Simplified Acute Physiology Score II and the Mortality Probability Model II0* , 2002, Critical care medicine.

[29]  D C Angus,et al.  Outcomes research in critical care: results of the American Thoracic Society Critical Care Assembly Workshop on Outcomes Research. The Members of the Outcomes Research Workshop. , 1999, American journal of respiratory and critical care medicine.

[30]  C. Sprung,et al.  Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine. , 1998, Critical care medicine.

[31]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[32]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[33]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

[34]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[35]  L. Lyons,et al.  Practical Statistics , 1888, Publications of the American Statistical Association.

[36]  Nikhil Ketkar,et al.  Deep Learning with Python , 2017 .

[37]  David D. Cox,et al.  Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms , 2013, SciPy.

[38]  Clement J. McDonald,et al.  Clinical Laboratory Sciences Data Transmission: The NPU Coding System , 2009, MIE.

[39]  G. Bernard,et al.  Results of the American Thoracic Society Critical Care Assembly Workshop on Outcomes Research , 1999 .