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Hughes N, Jia Y, Sujan M, Lawton T, Habli I, McDermid J. Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units. APPLIED ERGONOMICS 2024; 118:104275. [PMID: 38574594 DOI: 10.1016/j.apergo.2024.104275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/06/2024]
Abstract
Weaning patients from ventilation in intensive care units (ICU) is a complex task. There is a growing desire to build decision-support tools to help clinicians during this process, especially those employing Artificial Intelligence (AI). However, tools built for this purpose should fit within and ideally improve the current work environment, to ensure they can successfully integrate into clinical practice. To do so, it is important to identify areas where decision-support tools may aid clinicians, and associated design requirements for such tools. This study analysed the work context surrounding the weaning process from mechanical ventilation in ICU environments, via cognitive task and work domain analyses. In doing so, both what cognitive processes clinicians perform during weaning, and the constraints and affordances of the work environment itself, were described. This study found a number of weaning process tasks where decision-support tools may prove beneficial, and from these a set of contextual design requirements were created. This work benefits researchers interested in creating human-centred decision-support tools for mechanical ventilation that are sensitive to the wider work system.
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Affiliation(s)
- Nathan Hughes
- University of York, Deramore Lane, York, YO10 5GH, UK.
| | - Yan Jia
- University of York, Deramore Lane, York, YO10 5GH, UK
| | | | - Tom Lawton
- University of York, Deramore Lane, York, YO10 5GH, UK; Improvement Academy, Bradford Institute for Health Research, Duckworth Lane, Bradford, BD9 6RJ, UK
| | - Ibrahim Habli
- University of York, Deramore Lane, York, YO10 5GH, UK
| | - John McDermid
- University of York, Deramore Lane, York, YO10 5GH, UK
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Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med 2024; 13:1505. [PMID: 38592696 PMCID: PMC10934889 DOI: 10.3390/jcm13051505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 04/10/2024] Open
Abstract
The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.
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Affiliation(s)
- Tamar Stivi
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Dan Padawer
- Department of Pulmonary Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel;
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| | - Noor Dirini
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Akiva Nachshon
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Baruch M. Batzofin
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Stephane Ledot
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
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Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne) 2022; 9:935366. [PMID: 36465940 PMCID: PMC9715756 DOI: 10.3389/fmed.2022.935366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2023] Open
Abstract
Background For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.
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Affiliation(s)
- Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Shian-Chin Ko
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Kuo-Chen Cheng
- Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Ming Chao
- Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying, Taiwan
- Department of Dental Laboratory Technology, Min-Hwei College of Health Care Management, Liouying, Taiwan
| | - Mei-I Sung
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Chen Hsing
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Chih-Cheng Lai
- Division of Hospital Medicine, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung, Taiwan
- Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan
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The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms. Crit Care Explor 2022; 4:e0718. [PMID: 35702351 PMCID: PMC9191311 DOI: 10.1097/cce.0000000000000718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
To evaluate the contribution of a preextubation chest X-ray (CXR) to identify the risk of extubation failure in mechanically ventilated patients.
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Chen WT, Huang HL, Ko PS, Su W, Kao CC, Su SL. A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients. J Pers Med 2022; 12:501. [PMID: 35330500 PMCID: PMC8950402 DOI: 10.3390/jpm12030501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/25/2022] [Accepted: 03/19/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. OBJECTIVES This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. METHODS This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital's Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., "patients weaned within 24 h" (n = 1042) and "patients not weaned within 24 h" (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. RESULTS The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. CONCLUSIONS The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.
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Affiliation(s)
- Wei-Teing Chen
- Division of Thoracic Medicine, Department of Medicine, Cheng Hsin General Hospital, Tri-Service General Hospital, National Defense Medical Center, Taipei 112401, Taiwan;
| | - Hai-Lun Huang
- School of Public Health, National Defense Medical Center, Taipei 114201, Taiwan; (H.-L.H.); (P.-S.K.); (W.S.)
| | - Pi-Shao Ko
- School of Public Health, National Defense Medical Center, Taipei 114201, Taiwan; (H.-L.H.); (P.-S.K.); (W.S.)
| | - Wen Su
- School of Public Health, National Defense Medical Center, Taipei 114201, Taiwan; (H.-L.H.); (P.-S.K.); (W.S.)
- Institute of Aerospace and Undersea Medic, National Defense Medical Center, Taipei 114201, Taiwan
| | - Chung-Cheng Kao
- Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei 105309, Taiwan;
| | - Sui-Lung Su
- School of Public Health, National Defense Medical Center, Taipei 114201, Taiwan; (H.-L.H.); (P.-S.K.); (W.S.)
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The Feasibility of a Machine Learning Approach in Predicting Successful Ventilator Mode Shifting for Adult Patients in the Medical Intensive Care Unit. Medicina (B Aires) 2022; 58:medicina58030360. [PMID: 35334536 PMCID: PMC8949015 DOI: 10.3390/medicina58030360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Objectives: Traditional assessment of the readiness for the weaning from the mechanical ventilator (MV) needs respiratory parameters in a spontaneous breath. Exempted from the MV disconnecting and manual measurements of weaning parameters, a prediction model based on parameters from MV and electronic medical records (EMRs) may help the assessment before spontaneous breath trials. The study aimed to develop prediction models using machine learning techniques with parameters from the ventilator and EMRs for predicting successful ventilator mode shifting in the medical intensive care unit. Materials and Methods: A retrospective analysis of 1483 adult patients with mechanical ventilators for acute respiratory failure in three medical intensive care units between April 2015 and October 2017 was conducted by machine learning techniques to establish the predicting models. The input candidate parameters included ventilator setting and measurements, patients’ demographics, arterial blood gas, laboratory results, and vital signs. Several classification algorithms were evaluated to fit the models, including Lasso Regression, Ridge Regression, Elastic Net, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine, and Artificial Neural Network according to the area under the Receiver Operating Characteristic curves (AUROC). Results: Two models were built to predict the success shifting from full to partial support ventilation (WPMV model) or from partial support to the T-piece trial (sSBT model). In total, 3 MV and 13 nonpulmonary features were selected for the WPMV model with the XGBoost algorithm. The sSBT model was built with 8 MV and 4 nonpulmonary features with the Random Forest algorithm. The AUROC of the WPMV model and sSBT model were 0.76 and 0.79, respectively. Conclusions: The weaning predictions using machine learning and parameters from MV and EMRs have acceptable performance. Without manual measurements, a decision-making system would be feasible for the continuous prediction of mode shifting when the novel models process real-time data from MV and EMRs.
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Fleuren LM, Dam TA, Tonutti M, de Bruin DP, Lalisang RCA, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, Dongelmans DA, de Jong R, Peters M, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Arbous S, Vonk SJJ, Fornasa M, Machado T, Houwert T, Hovenkamp H, Noorduijn Londono R, Quintarelli D, Scholtemeijer MG, de Beer AA, Cinà G, Kantorik A, de Ruijter T, Herter WE, Beudel M, Girbes ARJ, Hoogendoorn M, Thoral PJ, Elbers PWG. Predictors for extubation failure in COVID-19 patients using a machine learning approach. Crit Care 2021; 25:448. [PMID: 34961537 PMCID: PMC8711075 DOI: 10.1186/s13054-021-03864-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/13/2021] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. CONCLUSION The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.
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Affiliation(s)
- Lucas M. Fleuren
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Tariq A. Dam
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Olaf L. Cremer
- Department of Intensive Care, UMC Utrecht, Utrecht, The Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis and Vlietland, Rotterdam, The Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dave A. Dongelmans
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands
| | - Marco Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands
| | | | | | - Ralph Nowitzky
- Intensive Care, HagaZiekenhuis, Den Haag, The Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | | | - Ellen G. M. Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands
| | | | - Tom Dormans
- Intensive Care, Zuyderland MC, Heerlen, The Netherlands
| | | | | | | | | | | | | | - Gert B. Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Alexander D. Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands
| | | | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Judith Lens
- ICU, IJsselland Ziekenhuis, Capelle Aan Den IJssel, The Netherlands
| | | | - A. Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Robert Entjes
- Department of Intensive Care, Adrz, Goes, The Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | - Thijs C. D. Rettig
- Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands
| | - Sesmu Arbous
- Department of Intensive Care, LUMC, Leiden, The Netherlands
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Martijn Beudel
- Department of Neurology, Amsterdam UMC, Universiteit Van Amsterdam, Amsterdam, The Netherlands
| | - Armand R. J. Girbes
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mark Hoogendoorn
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick J. Thoral
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Paul W. G. Elbers
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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Radhakrishnan S, Nair SG, Isaac J. Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning. Biomed Signal Process Control 2021; 71:103170. [PMID: 34567236 PMCID: PMC8450520 DOI: 10.1016/j.bspc.2021.103170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/17/2021] [Accepted: 09/07/2021] [Indexed: 02/02/2023]
Abstract
Background and objective In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals. Methods The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming. Results Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs. Conclusions Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.
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Affiliation(s)
- Sita Radhakrishnan
- Department of Instrumentation, Cochin University of Science and Technology, Kochi, Kerala 682022, India
| | - Suresh G Nair
- Anesthesia and Critical Care, Aster Medcity, Kochi, Kerala 682034, India
| | - Johney Isaac
- Department of Instrumentation, Cochin University of Science and Technology, Kochi, Kerala 682022, India
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Ossai CI, Wickramasinghe N. Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit - A critical overview. Int J Med Inform 2021; 150:104469. [PMID: 33906020 DOI: 10.1016/j.ijmedinf.2021.104469] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/16/2021] [Accepted: 04/18/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare. OBJECTIVE This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML). METHOD Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV). The quality of the articles identified was assessed with a modified Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sessional research. RESULTS A total of 26 articles were identified for the study that has IDS for TV (n = 2, 7.8 %), asynchrony (n = 9, 34.6 %), weaning (n = 12, 46.2 %), and others (n = 3, 11.5 %). It was affirmed that implementing IDS in MV management will enhance seamless ICU patient management following the utilization of various Machine Learning (ML) algorithms in decision support. The studies relied on (n = 14) ML algorithms to predict the TV, asynchrony, weaning, risk of PMV and Positive End-Expiratory Pressure (PEEP) changes of 11-20262 ICU patients records with model inputs ranging from (n = 1) for timeseries analysis of TV to (n = 47) for weaning prediction. CONCLUSIONS The small data size, poor study design, and result reporting, with the heterogeneity of techniques used in the various studies, hampered the development of a unified approach for managing MV efficiency in TV monitoring, asynchrony, and weaning predictions. Notwithstanding, the ensemble model was able to predict TV, asynchrony, and weaning to a higher accuracy than the other algorithms.
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Affiliation(s)
- Chinedu I Ossai
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia.
| | - Nilmini Wickramasinghe
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia; Epworth Healthcare Australia, Australia.
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Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19. Anaesth Crit Care Pain Med 2020; 39:691-693. [PMID: 33099016 PMCID: PMC7577289 DOI: 10.1016/j.accpm.2020.09.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/13/2020] [Accepted: 09/15/2020] [Indexed: 01/10/2023]
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Nguyen D, Ngo B, vanSonnenberg E. AI in the Intensive Care Unit: Up-to-Date Review. J Intensive Care Med 2020; 36:1115-1123. [PMID: 32985324 DOI: 10.1177/0885066620956620] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
AI is the latest technologic trend that likely will have a huge impact in medicine. AI's potential lies in its ability to process large volumes of data and perform complex pattern analyses. The ICU is an area of medicine that is particularly conducive to AI applications. Much AI ICU research currently is focused on improving high volumes of data on high-risk patients and making clinical workflow more efficient. Emerging topics of AI medicine in the ICU include AI sensors, sepsis prediction, AI in the NICU or SICU, and the legal role of AI in medicine. This review will cover the current applications of AI medicine in the ICU, potential pitfalls, and other AI medicine-related topics relevant for the ICU.
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Affiliation(s)
- Diep Nguyen
- University of Arizona College of Medicine Phoenix, AZ, USA
| | - Brandon Ngo
- University of Arizona College of Medicine Phoenix, AZ, USA
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