Published on in Vol 5, No 4 (2017): Oct-Dec

Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements

Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements

Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements

Journals

  1. Keim-Malpass J, Clark M, Lake D, Moorman J. Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. Journal of Clinical Monitoring and Computing 2020;34(4):797 View
  2. Raita Y, Camargo C, Macias C, Mansbach J, Piedra P, Porter S, Teach S, Hasegawa K. Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study. Scientific Reports 2020;10(1) View
  3. Goto T, Camargo C, Faridi M, Freishtat R, Hasegawa K. Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage. JAMA Network Open 2019;2(1):e186937 View
  4. Kirkendall E, Ni Y, Lingren T, Leonard M, Hall E, Melton K. Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support. Journal of Medical Internet Research 2019;21(5):e13047 View
  5. Goto T, Jo T, Matsui H, Fushimi K, Hayashi H, Yasunaga H. Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease. COPD: Journal of Chronic Obstructive Pulmonary Disease 2019;16(5-6):338 View
  6. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  7. Al-Mamun M, Brothers T, Newsome A. Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients. Annals of Pharmacotherapy 2021;55(4):421 View
  8. Mayampurath A, Jani P, Dai Y, Gibbons R, Edelson D, Churpek M. A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children*. Pediatric Critical Care Medicine 2020;21(9):820 View
  9. Lin Y, Zhou Y, Faghri F, Shaw M, Campbell R, Moskovitch R. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PLOS ONE 2019;14(7):e0218942 View
  10. Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone W, Endo T, Nizuma K, Tominaga T. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Reports 2020;1(1):8 View
  11. Giannini H, Ginestra J, Chivers C, Draugelis M, Hanish A, Schweickert W, Fuchs B, Meadows L, Lynch M, Donnelly P, Pavan K, Fishman N, Hanson C, Umscheid C. A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice*. Critical Care Medicine 2019;47(11):1485 View
  12. Sosa T, Dewan M, Tegtmeyer K. Back to the Basics or Back to the Future? The Art and Science of Predicting Clinical Deterioration in Hospitalized Children*. Pediatric Critical Care Medicine 2020;21(9):839 View
  13. Raita Y, Goto T, Faridi M, Brown D, Camargo C, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care 2019;23(1) View
  14. Peine A, Hallawa A, Schöffski O, Dartmann G, Fazlic L, Schmeink A, Marx G, Martin L. A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study. JMIR Medical Informatics 2019;7(4):e14806 View
  15. Hu Z, Du D, Kaderali L. A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction. PLOS ONE 2020;15(9):e0237724 View
  16. Sakib N, Ahamed S, Khan R, Griffin P, Haque M. Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology. JMIR Medical Informatics 2020;8(12):e18352 View
  17. Schwartz J, Moy A, Rossetti S, Elhadad N, Cato K. Clinician involvement in research on machine learning–based predictive clinical decision support for the hospital setting: A scoping review. Journal of the American Medical Informatics Association 2021;28(3):653 View
  18. Alshwaheen T, Hau Y, Ass'Ad N, Abualsamen M. A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network. IEEE Access 2021;9:3894 View
  19. Alexander J, Romito B, Çobanoğlu M. The present and future role of artificial intelligence and machine learning in anesthesiology. International Anesthesiology Clinics 2020;58(4):7 View
  20. Wu Z, Wang X, Pan R, Huang X, Li Y, Jiang L. Study of the Relationship between ICU Patient Recovery and TCM Treatment in Acute Phase: A Retrospective Study Based on Python Data Mining Technology. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
  21. Romero-Brufau S, Whitford D, Johnson M, Hickman J, Morlan B, Therneau T, Naessens J, Huddleston J. Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS). Journal of the American Medical Informatics Association 2021;28(6):1207 View
  22. Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K, Mitchell A. Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review. Academic Emergency Medicine 2021;28(2):184 View
  23. Al-Shwaheen T, Moghbel M, Hau Y, Ooi C. Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature. Artificial Intelligence Review 2022;55(2):1055 View
  24. Hekmatfar T, Haratizadeh S, Goliaei S. Embedding ranking-oriented recommender system graphs. Expert Systems with Applications 2021;181:115108 View
  25. Froud R, Hansen S, Ruud H, Foss J, Ferguson L, Fredriksen P. Relative Performance of Machine Learning and Linear Regression in Predicting Quality of Life and Academic Performance of School Children in Norway: Data Analysis of a Quasi-Experimental Study. Journal of Medical Internet Research 2021;23(7):e22021 View
  26. Newsome A, Murray B, Smith S, Brothers T, Al-Mamun M, Chase A, Rowe S, Buckley M, Murphy D, Devlin J. Optimization of critical care pharmacy clinical services: A gap analysis approach. American Journal of Health-System Pharmacy 2021;78(22):2077 View
  27. Peelen R, Eddahchouri Y, Koeneman M, van de Belt T, van Goor H, Bredie S. Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review. Journal of Hospital Medicine 2021;16(10):612 View
  28. Clarke S, Parmesar K, Saleem M, Ramanan A. Future of machine learning in paediatrics. Archives of Disease in Childhood 2022;107(3):223 View
  29. Singh P, Nagori A, Lodha R, Sethi T. Early prediction of hypothermia in pediatric intensive care units using machine learning. Frontiers in Physiology 2022;13 View
  30. Wu C, Wu M, Chen L, Lo Y, Huang C, Yu H, Pardeshi M, Lo W, Sheu R. AEP-DLA: Adverse Event Prediction in Hospitalized Adult Patients Using Deep Learning Algorithms. IEEE Access 2021;9:55673 View
  31. Jerng J, Chen L, Chen S, Kuo L, Tsan C, Hsieh P, Chen C, Chuang P, Huang H, Huang S. Effect of implementing decision support to activate a rapid response system by automated screening of verified vital sign data: A retrospective database study. Resuscitation 2022;173:23 View
  32. Jentzer J, Kashou A, Murphree D. Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit. Intelligence-Based Medicine 2023;7:100089 View
  33. Al-Dailami A, Kuang H, Wang J. Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion. Computers in Biology and Medicine 2022;151:106278 View
  34. Sosa T, Sitterding M, Dewan M, Coleman M, Seger B, Bedinghaus K, Hawkins D, Maddock B, Hausfeld J, Falcone R, Brady P, Simmons J, White C. Optimizing Situation Awareness to Reduce Emergency Transfers in Hospitalized Children. Pediatrics 2021;148(4) View
  35. Garcia-Canadilla P, Isabel-Roquero A, Aurensanz-Clemente E, Valls-Esteve A, Miguel F, Ormazabal D, Llanos F, Sanchez-de-Toledo J. Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery. Frontiers in Pediatrics 2022;10 View
  36. Mayampurath A, Sanchez-Pinto L, Hegermiller E, Erondu A, Carey K, Jani P, Gibbons R, Edelson D, Churpek M. Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU. Pediatric Critical Care Medicine 2022;23(7):514 View
  37. Ning H, Li R, Zhou T. Machine learning for microalgae detection and utilization. Frontiers in Marine Science 2022;9 View
  38. Rust L, Gorham T, Bambach S, Bode R, Maa T, Hoffman J, Rust S. The Deterioration Risk Index: Developing and Piloting a Machine Learning Algorithm to Reduce Pediatric Inpatient Deterioration*. Pediatric Critical Care Medicine 2023;24(4):322 View
  39. Kim M, Park S, Kim C, Choi M. Diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients: A systematic review. International Journal of Nursing Studies 2023;138:104411 View
  40. El-Bouri R, Taylor T, Youssef A, Zhu T, Clifton D. Machine learning in patient flow: a review. Progress in Biomedical Engineering 2021;3(2):022002 View
  41. von Gerich H, Moen H, Block L, Chu C, DeForest H, Hobensack M, Michalowski M, Mitchell J, Nibber R, Olalia M, Pruinelli L, Ronquillo C, Topaz M, Peltonen L. Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies 2022;127:104153 View
  42. Wu J, Lin Y, Li P, Hu Y, Zhang L, Kong G. Predicting Prolonged Length of ICU Stay through Machine Learning. Diagnostics 2021;11(12):2242 View
  43. Su D, Zhang X, He K, Chen Y, Wu N. Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches. Frontiers in Public Health 2022;10 View
  44. Chang C, Chen C, Hsieh J, Jeng J. Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset. Scientific Reports 2023;13(1) View
  45. Li M, Cheng K, Ku K, Li J, Hu H, Ung C. Modelling 30-day hospital readmission after discharge for COPD patients based on electronic health records. npj Primary Care Respiratory Medicine 2023;33(1) View
  46. Mestrom E, Bakkes T, Ourahou N, Korsten H, Serra P, Montenij L, Mischi M, Turco S, Bouwman R, Pasquali S. Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors. PLOS ONE 2023;18(8):e0286818 View
  47. Chen J, Qi T, Vu J, Wen Y. A deep learning approach for inpatient length of stay and mortality prediction. Journal of Biomedical Informatics 2023;147:104526 View
  48. Cheyne H, Gandomi A, Hosseini Vajargah S, Catterson V, Mackoy T, McCullagh L, Musso G, Hajizadeh N. Drivers of mortality in COVID ARDS depend on patient sub-type. Computers in Biology and Medicine 2023;166:107483 View
  49. Su K, Yuan X, Huang Y, Yuan Q, Yang M, Sun J, Li S, Long X, Liu L, Li T, Yuan Z. Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost. Indian Journal of Orthopaedics 2023;57(10):1667 View
  50. Charan G, Charan A, Khurana M, Narang G. Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review. Galician Medical Journal 2023;30(4) View
  51. Foote H, Shaikh Z, Witt D, Shen T, Ratliff W, Shi H, Gao M, Nichols M, Sendak M, Balu S, Osborne K, Kumar K, Jackson K, McCrary A, Li J. Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration. Hospital Pediatrics 2024;14(1):11 View
  52. McCaffery K, Carey K, Campbell V, Gifford S, Smith K, Edelson D, Churpek M, Mayampurath A. Predicting transfers to intensive care in children using CEWT and other early warning systems. Resuscitation Plus 2024;17:100540 View

Books/Policy Documents

  1. Chang A. Intelligence-Based Medicine. View
  2. Yao J, Liu Y, Li B, Gou S, Pou-Prom C, Murray J, Verma A, Mamdani M, Ghassemi M. Explainable AI in Healthcare and Medicine. View