| P50 | author | Mervyn Singer | Q29643596 |
| | Ari Ercole | Q41625599 |
| | Peter Watkinson | Q56422868 |
| | Steve K Harris | Q57640849 |
| | Christopher Meiring | Q58885749 |
| | David Brealey | Q90077213 |
| P2093 | author name string | Stephen J Brett | |
| | Andrew Jones | |
| | Abhishek Dixit | |
| | Simon Ashworth | |
| | Richard Beale | |
| | Niall S MacCallum | |
| P2860 | cites work | SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description | Q24812156 |
| | SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission | Q24812173 |
| | Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) | Q26768292 |
| | MIMIC-III, a freely accessible critical care database | Q28871995 |
| | APACHE II: a severity of disease classification system | Q29547729 |
| | A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study | Q29615430 |
| | pROC: an open-source package for R and S+ to analyze and compare ROC curves | Q30050695 |
| | Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. | Q33797972 |
| | Clinical review: scoring systems in the critically ill. | Q33918938 |
| | Can health care costs be reduced by limiting intensive care at the end of life? | Q34118843 |
| | The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults | Q34991895 |
| | Machine learning applications in cancer prognosis and prediction | Q35143671 |
| | Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. | Q36150676 |
| | Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and Influence of Resuscitation Status on Model Performance | Q36300147 |
| | Time-Limited Trials of Intensive Care for Critically Ill Patients With Cancer: How Long Is Long Enough? | Q36463609 |
| | A Database-driven Decision Support System: Customized Mortality Prediction | Q36917929 |
| | High-performance detection and early prediction of septic shock for alcohol-use disorder patients | Q37124681 |
| | Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. | Q37135033 |
| | Recommendations for end-of-life care in the intensive care unit: a consensus statement by the American College [corrected] of Critical Care Medicine | Q37145783 |
| | Discussion of treatment trials in intensive care | Q37164856 |
| | The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation | Q37245219 |
| | Using electronic health record collected clinical variables to predict medical intensive care unit mortality. | Q37284588 |
| | Machine Learning in Medicine | Q38634420 |
| | End-of-life practices in European intensive care units: the Ethicus Study | Q39391618 |
| | Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome | Q40431413 |
| | Withdrawal of mechanical ventilation in anticipation of death in the intensive care unit | Q40561617 |
| | Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. | Q42376836 |
| | A computational approach to early sepsis detection | Q46589859 |
| | An initial comparison of intensive care in Japan and the United States | Q47284041 |
| | The Multiple Organ Dysfunction Score as a descriptor of patient outcome in septic shock compared with two other scoring systems | Q51460015 |
| | Evaluation of predictive ability of APACHE II system and hospital outcome in Canadian intensive care unit patients | Q52337426 |
| | Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database | Q57208242 |
| | Critical care data processing tools | Q58854192 |
| | Validation of the mortality prediction model for ICU patients | Q69747985 |
| | Assessment of the performance of five intensive care scoring models within a large Scottish database | Q73981290 |
| | External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study | Q78819690 |
| | Prognosis and prognostic research: what, why, and how? | Q83391342 |
| | Time-Limited Trials Near the End of Life | Q85042990 |
| | Time-limited trial of intensive care treatment: an overview of current literature | Q91094067 |
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P4510 | describes a project that uses | machine learning | Q2539 |
| P433 | issue | 11 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | machine learning | Q2539 |
| P304 | page(s) | e0206862 | |
| P577 | publication date | 2018-11-14 | |
| P1433 | published in | PLOS One | Q564954 |
| P1476 | title | Optimal intensive care outcome prediction over time using machine learning | |
| P478 | volume | 13 | |