AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

[1]  Eitan S. Agai,et al.  Are ChatGPT and large language models “the answer” to bringing us closer to systematic review automation? , 2023, Systematic Reviews.

[2]  J. Ayers,et al.  Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. , 2023, JAMA internal medicine.

[3]  H. Fukushima,et al.  Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase , 2023, Scientific Reports.

[4]  Dragomir R. Radev,et al.  Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations , 2023, ArXiv.

[5]  T. Yamaki,et al.  Derivation of Coagulation Phenotypes and the Association with Prognosis in Traumatic Brain Injury: A Cluster Analysis of Nationwide Multicenter Study , 2023, Neurocritical care.

[6]  Mason Marks,et al.  AI-Generated Medical Advice-GPT and Beyond. , 2023, JAMA.

[7]  F. Siddiqui,et al.  Implementation of Prediction Models in the Emergency Department from an Implementation Science Perspective-Determinants, Outcomes, and Real-World Impact: A Scoping Review. , 2023, Annals of emergency medicine.

[8]  M. Ong,et al.  Clustering of Environmental Parameters and the Risk of Acute Ischaemic Stroke , 2023, International journal of environmental research and public health.

[9]  Won Joon Yun,et al.  Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset , 2023, Comput. Biol. Medicine.

[10]  J. Stingone,et al.  Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects. , 2023, American journal of epidemiology.

[11]  B. van Calster,et al.  There is no such thing as a validated prediction model , 2023, BMC Medicine.

[12]  S. Narayan,et al.  Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies , 2023, EBioMedicine.

[13]  L. Cho,et al.  Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model. , 2023, JAMA.

[14]  J. Greenstein,et al.  A Pilot Study to Predict Cardiac Arrest in the Pediatric Intensive Care Unit. , 2023, Resuscitation.

[15]  R. Mohindra,et al.  The Ability of Machine Learning Algorithms to Predict Defibrillation Success during Cardiac Arrest: A Systematic Review. , 2023, Resuscitation.

[16]  Kyle Lam,et al.  ChatGPT: the future of discharge summaries? , 2023, The Lancet. Digital health.

[17]  Owen C. King,et al.  Self-fulfilling Prophecy in Practical and Automated Prediction , 2023, Ethical Theory and Moral Practice.

[18]  Sigurd L. Alnes,et al.  Auditory stimulation and deep learning predict awakening from coma after cardiac arrest , 2023, Brain : a journal of neurology.

[19]  Tiffany H. Kung,et al.  Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models , 2022, medRxiv.

[20]  G. Satzger,et al.  Artificial intelligence and machine learning , 2022, Electronic Markets.

[21]  Adrienne S. Kline,et al.  Multimodal machine learning in precision health: A scoping review , 2022, npj Digital Medicine.

[22]  Haoqi Sun,et al.  Classification of neurologic outcomes from medical notes using natural language processing , 2022, Expert Syst. Appl..

[23]  J. Elmer,et al.  Self-fulfilling prophecies and machine learning in resuscitation science. , 2022, Resuscitation.

[24]  A. Ercole,et al.  Steps to avoid overuse and misuse of machine learning in clinical research , 2022, Nature Medicine.

[25]  Kenneth L. Abbott,et al.  Phenotype clustering in health care: A narrative review for clinicians , 2022, Frontiers in Artificial Intelligence.

[26]  P. Pek,et al.  Clustering of Environmental Parameters and the Risk of Acute Myocardial Infarction , 2022, International journal of environmental research and public health.

[27]  T. Krafft,et al.  Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point , 2022, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.

[28]  F. Siddiqui,et al.  Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study , 2022, EClinicalMedicine.

[29]  W. Self,et al.  Individualized Treatment Effects of Bougie vs Stylet for Tracheal Intubation in Critical Illness. , 2022, American journal of respiratory and critical care medicine.

[30]  L. Halasyamani,et al.  The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial , 2022, Critical care medicine.

[31]  Freneka F. Minter,et al.  Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission? , 2022, Journal of the American Heart Association.

[32]  M. V. van Putten,et al.  Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods , 2022, Neurocritical Care.

[33]  T. Goto,et al.  Experimental Implementation of NSER Mobile App for Efficient Real-Time Sharing of Prehospital Patient Information With Emergency Departments: Interrupted Time-Series Analysis , 2022, JMIR formative research.

[34]  Chang Liu,et al.  Deep learning of early brain imaging to predict post-arrest electroencephalography. , 2022, Resuscitation.

[35]  Y. Hayashi,et al.  Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis , 2022, Acute medicine & surgery.

[36]  M. Ghassemi,et al.  Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning , 2021, IEEE Transactions on Biomedical Engineering.

[37]  Jordan D. Fuhrman,et al.  Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest , 2021, Neurocritical Care.

[38]  A. Hirayama,et al.  Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm　- Machine Learning-Based Unsupervised Cluster Analysis. , 2021, Circulation journal : official journal of the Japanese Circulation Society.

[39]  T. Sonoo,et al.  External validation of the POP score for predicting obstetric and gynecological diseases in the emergency department. , 2021, The American journal of emergency medicine.

[40]  D. Seger,et al.  Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. , 2021, The Lancet. Digital health.

[41]  Liang Zhao,et al.  Heterogeneous treatment effect analysis based on machine‐learning methodology , 2021, CPT: pharmacometrics & systems pharmacology.

[42]  J. Kors,et al.  Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review , 2021, medRxiv.

[43]  G. Collins,et al.  Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review , 2021, BMJ.

[44]  Y. Hayashi,et al.  Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest. , 2021, Resuscitation.

[45]  Jimeng Sun,et al.  Predicting Neurological Outcome in Comatose Patients after Cardiac Arrest with Multiscale Deep Neural Networks. , 2021, Resuscitation.

[46]  Owen C. King,et al.  Can we learn from hidden mistakes? Self-fulfilling prophecy and responsible neuroprognostic innovation , 2021, Journal of Medical Ethics.

[47]  Albert Y. Chen,et al.  Machine Learning–Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation , 2021, Journal of medical Internet research.

[48]  N. Matsuda,et al.  Outcome Related to Level of Targeted Temperature Management in Postcardiac Arrest Syndrome of Low, Moderate, and High Severities: A Nationwide Multicenter Prospective Registry , 2021, Critical care medicine.

[49]  J. Soar,et al.  European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care , 2021, Intensive Care Medicine.

[50]  T. Goto,et al.  Coagulation phenotypes in sepsis and effects of recombinant human thrombomodulin: an analysis of three multicentre observational studies , 2021, Critical Care.

[51]  A. Claesson,et al.  Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study. , 2021, Resuscitation.

[52]  K. Chon,et al.  Deep Neural Network Approach for Continuous ECG‐Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation , 2021, Journal of the American Heart Association.

[53]  L. Celi,et al.  Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care , 2021, npj Digital Medicine.

[54]  C. Palmieri,et al.  The discovery of biological subphenotypes in ARDS: a novel approach to targeted medicine? , 2021, Journal of Intensive Care.

[55]  T. Kitamura,et al.  Machine learning-based prediction models for accidental hypothermia patients , 2021, Journal of Intensive Care.

[56]  P. Kudenchuk,et al.  Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services , 2021, JAMA network open.

[57]  Chava L. Ramspek,et al.  External validation of prognostic models: what, why, how, when and where? , 2020, Clinical kidney journal.

[58]  K. Delucchi,et al.  Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. , 2020, Critical care medicine.

[59]  B. Goldstein,et al.  Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations , 2020, JAMA network open.

[60]  J. Kwon,et al.  Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography , 2020, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.

[61]  T. Sonoo,et al.  Validation of chief complaints, medical history, medications, and physician diagnoses structured with an integrated emergency department information system in Japan: the Next Stage ER system , 2020, Acute medicine & surgery.

[62]  C. Callaway,et al.  Association of Initial Illness Severity and Outcomes After Cardiac Arrest With Targeted Temperature Management at 36 °C or 33 °C , 2020, JAMA network open.

[63]  A. Gordon,et al.  Subphenotypes in critical care: translation into clinical practice. , 2020, The Lancet. Respiratory medicine.

[64]  R. Dunne,et al.  Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing. , 2020, Journal of emergency nursing.

[65]  C. Calfee,et al.  ARDS Subphenotypes: Understanding a Heterogeneous Syndrome , 2020, Critical Care.

[66]  Volker M. Lauschke,et al.  Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation , 2020, npj Genomic Medicine.

[67]  L. Celi,et al.  Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review , 2020, Journal of medical Internet research.

[68]  Paolo Favaro,et al.  EEG‐based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features , 2019, Human brain mapping.

[69]  J. Puyana,et al.  Emergency care surveillance and emergency care registries in low-income and middle-income countries: conceptual challenges and future directions for research , 2019, BMJ Global Health.

[70]  Nicholas W. Sterling,et al.  Prediction of emergency department patient disposition based on natural language processing of triage notes , 2019, Int. J. Medical Informatics.

[71]  Jeremy C. Weiss,et al.  Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. , 2019, JAMA.

[72]  M. Ong,et al.  Impact of population aging on the presentation of out‐of‐hospital cardiac arrest in the Pan Asian Resuscitation Outcomes Study , 2019, Acute medicine & surgery.

[73]  Luciano Floridi,et al.  Establishing the rules for building trustworthy AI , 2019, Nature Machine Intelligence.

[74]  Gloria P. Lipori,et al.  MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery , 2019, Annals of surgery.

[75]  Francisco Azuaje,et al.  Artificial intelligence for precision oncology: beyond patient stratification , 2019, npj Precision Oncology.

[76]  S. Athey,et al.  Estimating Treatment Effects with Causal Forests: An Application , 2019, Observational Studies.

[77]  J. Guttag,et al.  A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle , 2019, EAAMO.

[78]  Tadahiro Goto,et al.  Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage , 2019, JAMA network open.

[79]  C. Bergmeir,et al.  Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study , 2018, PLoS medicine.

[80]  Aldo A. Faisal,et al.  The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care , 2018, Nature Medicine.

[81]  Michael O Harhay,et al.  Discriminative Accuracy of Physician and Nurse Predictions for Survival and Functional Outcomes 6 Months After an ICU Admission , 2017, JAMA.

[82]  Alvaro Riascos,et al.  Risk Adjustment Revisited Using Machine Learning Techniques , 2017 .

[83]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[84]  Hideharu Tanaka,et al.  Outcomes for out-of-hospital cardiac arrests across 7 countries in Asia: The Pan Asian Resuscitation Outcomes Study (PAROS). , 2015, Resuscitation.

[85]  Stefan Wager,et al.  Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.

[86]  C. Callaway,et al.  Documentation discrepancies of time-dependent critical events in out of hospital cardiac arrest. , 2014, Resuscitation.

[87]  C. Callaway,et al.  Regional variation in out-of-hospital cardiac arrest incidence and outcome. , 2008, JAMA.

[88]  A. Bodenham,et al.  Can you justify not using ultrasound guidance for central venous access? , 2006, Critical care.

[89]  John E Billi,et al.  Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries: a statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European , 2004, Circulation.

[90]  L. Tarassenko,et al.  Development and Validation of a Novel Scoring System , 2021 .

[91]  Reneé C. Wurth,et al.  On assessing trustworthy AI in healthcare Best practice for machine learning as a supportive tool to recognize cardiac arrest in emergency calls , 2021 .

[92]  M. Churpek,et al.  Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study. , 2018, Annals of surgery.

[93]  David D. Salcido,et al.  Inaccuracy of patient care reports for identification of critical resuscitation events during out-of-hospital cardiac arrest. , 2015, The American journal of emergency medicine.