Table 7.

The 30 most cited articles on artificial intelligence in intensive care unit ranked in the descending order of the number of citations.

Rank	Article	Citations, n	CPYa	Rank by CPY
1	Matthieu Komorowski, Leo A. Celi et al “The Artificial Intelligence Clinician Learns Optimal Treatment Strategies for Sepsis in Intensive Care” Nature Medicine (2018)	290	73	1
2	Shamim Nemati, Andre Holder et al “An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU” Critical Care Medicine,46,4 (2018)	249	62	2
3	Tom J. Pollard, Alistair E. W. Johnson et al “The eICU Collaborative Research Database, a Freely Available Multi-Center Database for Critical Care Research” Scientific Data,5,1 (2018)	211	53	3
4	Thomas Desautels, Jacob Calvert et al “Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: a Machine Learning Approach” JMIR Med Inform,4,3 (2016)	203	34	7
5	Brendon P. Scicluna, Lonneke A. van Vught et al “Classification of Patients With Sepsis According to Blood Genomic Endotype: a Prospective Cohort Study” The Lancet Respiratory Medicine,5,10, (2017)	164	33	9
6	Alistair E. W. Johnson, Mohammad M. Ghassemi et al “Machine Learning and Decision Support in Critical Care” Proceedings of the IEEE,104,2, (2016)	162	27	11
7	Richard Dybowski, Peter Weller et al “Prediction of Outcome in Critically Ill Patients Using Artificial Neural Network Synthesised by Genetic Algorithm” Lancet,347,9009,4 (1996)	160	6	28
8	Romain Pirracchio, Maya L. Petersen et al “Mortality Prediction in Intensive Care Units With the Super ICU Learner Algorithm (SICULA): a Population-Based Study” The Lancet Respiratory Medicine,3,1 (2015)	160	23	15
9	Q. Li, G. D. Clifford “Dynamic Time Warping and Machine Learning for Signal Quality Assessment of Pulsatile Signals” Physiological Measurement,33,9 (2012a)	156	16	20
10	Anton Aboukhalil, Larry Nielsen et al “Reducing False Alarm Rates for Critical Arrhythmias Using the Arterial Blood Pressure Waveform” Journal of Biomedical Informatics,41,3,6 (2008)	151	11	23
11	Feras Hatib, Zhongping Jian et al “Machine-learning Algorithm to Predict Hypotension Based on High-Fidelity Arterial Pressure Waveform Analysis” Anesthesiology,129,4, 10 (2018)	147	37	5
12	U. Rajendra Acharya, Hamido Fujita et al “Automated Identification of Shockable and Non-Shockable Life-Threatening Ventricular Arrhythmias Using Convolutional Neural Network” Future Generation Computer Systems,79,2 (2018)	144	36	6
13	Qingqing Mao, Melissa Jay et al “Multicentre Validation of a Sepsis Prediction Algorithm Using Only Vital Sign Data in the Emergency Department, General Ward and ICU” BMJ Open,8,1 (2018)	134	34	8
14	Zhengping Che, Sanjay Purushotham et al “Interpretable Deep Models for ICU Outcome Prediction” AMIA. Annual Symposium Proceedings (2016)	132	22	17
15	Abbas K. Abbas, Konrad Heimann et al “Neonatal NonContact Respiratory Monitoring Based on Real-Time Infrared Thermography” BioMedical Engineering Online,10,1,10 (2011)	126	11	21
16	Jacob S. Calvert, Daniel A. Price et al “A Computational Approach to Early Sepsis Detection“ Computers in Biology and Medicine,74,7 (2016)	116	19	18
17	Gilles Clermont, Derek C. Angus et al “Predicting Hospital Mortality for Patients in the Intensive Care Unit: a Comparison of Artificial Neural Networks with Logistic Regression Models” Critical Care Medicine,29,2 (2001)	112	10	24
18	David W. Shimabukuro, Christopher W. Barton et al “Effect of a Machine Learning-Based Severe Sepsis Prediction Algorithm on Patient Survival and Hospital Length of Stay: a Randomised Clinical Trial” BMJ Open Respiratory Research,4,1,11 (2017)	112	22	16
19	Jan Claassen, Kevin Doyle et al “Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury” New England Journal of Medicine,380,26,6 (2019)	111	37	4
20	Sanjay Purushotham, Chuizheng Meng et al “Benchmarking Deep Learning Models on Large Healthcare Datasets” Journal of Biomedical Informatics,83,7 (2018)	105	26	12
21	Jay L. Kovner. Kyle A. Carey et al “The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model” Critical Care Medicine,46,7 (2018)	105	26	13
22	Alexander Meyer, Dina Zverinski et al “Machine Learning for Real-Time Prediction of Complications in Critical Care: a Retrospective Study” The Lancet Respiratory Medicine,6,12 (2018)	101	25	14
23	Michel Dojat, Laurent Brochard et al “A Knowledge-Based System for Assisted Ventilation of Patients in Intensive Care Units” International journal of clinical monitoring and computing 9,4 (1992)	99	3	30
24	Nicos Maglaveras, Telemachos Stamkopoulos et al “An Adaptive Backpropagation Neural Network for Real-Time Ischemia Episodes Detection: Development and Performance Analysis Using the European ST-T Database” IEEE Transactions on Biomedical Engineering,45,7 (1998)	96	4	29
25	Allison Sutherland, Mervyn Thomas et al “Development and Validation of a Novel Molecular Biomarker Diagnostic Test for the Early Detection of Sepsis” Critical Care,15,3,6 (2011)	96	9	25
26	Hye Jin Kam, Ha Young Kim “Learning Representations for the Early Detection of Sepsis With Deep Neural Networks” Computers in Biology and Medicine,89,10 (2017)	96	19	19
27	Michelle M. Clark, Amber Hildreth et al “Diagnosis of Genetic Diseases in Seriously Ill Children by Rapid Whole-Genome Sequencing and Automated Phenotyping and Interpretation” Science Translational Medicine,11,489,4 (2019)	95	32	10
28	Subramani Mani, Asli Ozdas et al “Medical Decision Support Using Machine Learning for Early Detection of Late-Onset Neonatal Sepsis” Journal of the American Medical Informatics Association,21,2,3 (2014)	89	11	22
29	K. Ashwin Kumar, Yashwardhan Singh et al “Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning for Domain Independent Clinical Decision Support in ICU” Expert Systems with Applications,36,1 (2009)	86	7	27
30	Qiao Li, Gari D. Clifford “Signal Quality and Data Fusion For False Alarm Reduction in the Intensive Care Unit” Journal of Electrocardiology,45,6,11 (2012b)	85	9	26

^aCPY: citations per year.