CoChat
Sign Up
Cited by 35

A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

Chang, Ying-Jen, Hung, Kuo-Chuan, Wang, Li-Kai, Yu, Chia-Hung, Chen, Chao-Kun, Tay, Hung-Tze, Wang, Jhi-Joung, Liu, Chung-Feng·2021·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph18052713PMC PMC7967444
Open this paper in CoChat — free account

Abstract

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician–patient communication to achieve better patient comprehension.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/ijerph18052713

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 49

  1. [1]Tseng C.-H. Tsuang B.-J. Chiang C.-J. Ku K.-C. Tseng J.-S. Yang T.-Y. Hsu K.-H. Chen K.-C. Yu S.-L. Lee W.-C. The Relationship between Air Pollution and Lung Cancer in Nonsmokers in Taiwan J. Thorac. Oncol. 2019 14 784 792 10.1016/j.jtho.2018.12.033 30664991 · DOI
  2. [2]Popper H.H. Progression and metastasis of lung cancer Cancer Metastasis Rev. 2016 35 75 91 10.1007/s10555-016-9618-0 27018053 PMC4821869 · DOI
  3. [3]De Cabo C.M. Alarza F.H. Rodriguez A.C. Delgado M.M. Perioperative management in thoracic surgery Med. Intensiv. (Engl. Ed.) 2020 44 185 191 10.1016/j.medine.2019.10.006 31870510 · DOI
  4. [4]Alía I. Esteban A. Weaning from mechanical ventilation Crit. Care 2000 4 72 80 10.1186/cc660 11094496 PMC137330 · DOI
  5. [5]Gruartmoner G. Mesquida J. Masip J. Martínez M.L. Villagra A. Baigorri F. Pinsky M.R. Artigas A. Thenar oxygen saturation during weaning from mechanical ventilation: An observational study Eur. Respir. J. 2013 43 213 220 10.1183/09031936.00126312 23314894 · DOI
  6. [6]Miller R.D. Miller’s Anesthesia 8th ed. Elsevier/Saunders Philadelphia, PA, USA 2015 1945 1953
  7. [7]Royse C. Principles and Practice of Anesthesia for Thoracic Surgery 2nd ed. Springer Science & Business Media Berlin, Germany 2019 Volume 129
  8. [8]Vaporciyan A.A. Merriman K.W. Ece F. Roth J.A. Smythe W. Swisher S.G. Walsh G.L. Nesbitt J.C. Putnam J.B. Incidence of major pulmonary morbidity after pneumonectomy: Association with timing of smoking cessation Ann. Thorac. Surg. 2002 73 420 426 10.1016/S0003-4975(01)03443-9 11845853 · DOI
  9. [9]Jaklitsch M. Billmeier S. Preoperative Evaluation and Risk Assessment for Elderly Thoracic Surgery Patients Thorac. Surg. Clin. 2009 19 301 312 10.1016/j.thorsurg.2009.07.004 20066942 · DOI
  10. [10]Hawkins J. Khanna S. Argalious M. Sugammadex for Reversal of Neuromuscular Blockade: Uses and Limitations Curr. Pharm. Des. 2019 25 2140 2148 10.2174/1381612825666190704101145 31272347 · DOI
  11. [11]Carron M. Ieppariello G. Benefit of sugammadex in a morbidly obese patient with multiple sclerosis and severe res-piratory dysfunction J. Clin. Anesth. 2019 52 119 120 10.1016/j.jclinane.2018.09.024 30245284 · DOI
  12. [12]Cho H.C. Lee J.H. Lee S.C. Park S.Y. Rim J.C. Choi S.R. Use of sugammadex in lung cancer patients undergoing video-assisted thoracoscopic lobectomy Korean J. Anesthesiol. 2017 70 420 425 10.4097/kjae.2017.70.4.420 28794837 PMC5548944 · DOI
  13. [13]Cappellini I. Ostento D. Loriga B. Tofani L. De Gaudio A.R. Adembri C. Comparison of neostigmine vs. sugam-madex for recovery of muscle function after neuromuscular block by means of diaphragm ultrasonography in micro-laryngeal surgery: A randomised controlled trial Eur. J. Anaesthesiol. 2020 37 44 51 10.1097/EJA.0000000000001055 31356374 · DOI
  14. [14]Zein H. Baratloo A. Negida A. Safari S. Ventilator Weaning and Spontaneous Breathing Trials; an Educational Review Emergency 2016 4 65 71 27274515 PMC4893753
  15. [15]Harada T. Shimizu T. Kaji Y. Suyama Y. Matsumoto T. Kosaka C. Shimizu H. Nei T. Watanuki S. A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools Int. J. Environ. Res. Public Health 2020 17 6110 10.3390/ijerph17176110 32842581 PMC7504543 · DOI
  16. [16]Hargraves I.G. Montori V.M. Brito J.P. Kunneman M. Shaw K. Lavecchia C. Wilson M. Walker L. Thorsteinsdottir B. Purposeful SDM: A problem-based approach to caring for patients with shared decision making Patient Educ. Couns. 2019 102 1786 1792 10.1016/j.pec.2019.07.020 31353170 PMC6717012 · DOI
  17. [17]Mathijssen E.G. Bemt B.J.V.D. Hoogen F.H.V.D. Popa C.D. Vriezekolk J.E. Interventions to support shared decision making for medication therapy in long term conditions: A systematic review Patient Educ. Couns. 2020 103 254 265 10.1016/j.pec.2019.08.034 31493959 · DOI
  18. [18]Ousseine Y.M. Durand M.-A. Bouhnik A.-D. Smith A. Mancini J. Multiple health literacy dimensions are associated with physicians’ efforts to achieve shared decision-making Patient Educ. Couns. 2019 102 1949 1956 10.1016/j.pec.2019.05.015 31130338 · DOI
  19. [19]Hur C. Wi J. Kim Y. Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records Int. J. Environ. Res. Public Health 2020 17 8303 10.3390/ijerph17228303 PMC7697823 33182703 · DOI
  20. [20]Blagus R. Lusa L. SMOTE for high-dimensional class-imbalanced data BMC Bioinform. 2013 14 106 10.1186/1471-2105-14-106 PMC3648438 23522326 · DOI
  21. [21]Karabulut E.M. Ibrikci T. Effective Automated Prediction of Vertebral Column Pathologies Based on Logistic Model Tree with SMOTE Preprocessing J. Med. Syst. 2014 38 50 10.1007/s10916-014-0050-0 24753003 · DOI
  22. [22]Pandey S.K. Janghel R.R. Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE Australas. Phys. Eng. Sci. Med. 2019 42 1129 1139 10.1007/s13246-019-00815-9 31728941 · DOI
  23. [23]Seo J.-H. Kim Y.-H. Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection Comput. Intell. Neurosci. 2018 2018 9704672 10.1155/2018/9704672 30515202 PMC6236522 · DOI
  24. [24]Wang S. Wang D. Li J. Huang T. Cai Y.-D. Identification and analysis of the cleavage site in a signal peptide using SMOTE, dagging, and feature selection methods Mol. Omics 2017 14 64 73 10.1039/C7MO00030H 29725682 · DOI
  25. [25]Wang Y. Simon M. Bonde P. Harris B.U. Teuteberg J.J. Kormos R.L. Antaki J.F. Prognosis of Right Ventricular Failure in Patients with Left Ventricular Assist Device Based on Decision Tree with SMOTE IEEE Trans. Inf. Technol. Biomed. 2012 16 383 390 10.1109/TITB.2012.2187458 22334033 · DOI
  26. [26]Wang Y. Wu X. Chen Z. Ren F. Feng L. Du Q. Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China Int. J. Environ. Res. Public Health 2019 16 368 10.3390/ijerph16030368 30696105 PMC6388203 · DOI
  27. [27]Nakamura M. Kajiwara Y. Otsuka A. Kimura H. LVQ-SMOTE—Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data BioData Min. 2013 6 16 10.1186/1756-0381-6-16 24088532 PMC4016036 · DOI
  28. [28]Hsieh M.H. Chen C.-M. Hsieh C.-C. Chao C.-M. Lai C.-C. Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units Sci. Rep. 2018 8 1 7 10.1038/s41598-018-35582-2 30459331 PMC6244193 · DOI
  29. [29]Jan S.U. Lee Y.-D. Shin J. Koo I. Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features IEEE Access 2017 5 8682 8690 10.1109/ACCESS.2017.2705644 · DOI
  30. [30]Zhang J. Mucs D. Norinder U. Svensson F. LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets J. Chem. Inf. Model. 2019 59 4150 4158 10.1021/acs.jcim.9b00633 31560206 · DOI
  31. [32]Lou W. Wang X. Chen F. Chen Y. Jiang B. Zhang H. Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes PLoS ONE 2014 9 e86703 10.1371/journal.pone.0086703 24475169 PMC3901691 · DOI
  32. [33]Behnoush B. Bazmi E. Nazari S. Khodakarim S. Looha M. Soori H. Machine learning algorithms to predict seizure due to acute tramadol poisoning Hum. Exp. Toxicol. 2021 960327121991910 10.1177/0960327121991910 33538187 · DOI
  33. [34]Liu Y. Bai F. Tang Z. Liu N. Liu Q. Integrative transcriptomic, proteomic, and machine learning approach to iden-tifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease BMC Cardiovasc. Disord. 2021 21 52 10.1186/s12872-020-01819-0 33509101 PMC7842070 · DOI
  34. [35]Golpour P. Ghayour-Mobarhan M. Saki A. Esmaily H. Taghipour A. Tajfard M. Ghazizadeh H. Moohebati M. Ferns G.A. Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography Int. J. Environ. Res. Public Health 2020 17 6449 10.3390/ijerph17186449 PMC7558963 32899733 · DOI
  35. [36]Saeed U. Jan S.U. Lee Y.-D. Koo I. Fault diagnosis based on extremely randomized trees in wireless sensor networks Reliab. Eng. Syst. Saf. 2021 205 107284 10.1016/j.ress.2020.107284 · DOI
  36. [37]Lee H.-C. Yoon H.-K. Nam K. Cho Y.J. Kim T.K. Kim W.H. Bahk J.-H. Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery J. Clin. Med. 2018 7 322 10.3390/jcm7100322 30282956 PMC6210196 · DOI
  37. [38]Ryan L. Lam C. Mataraso S. Allen A. Green-Saxena A. Pellegrini E. Hoffman J. Barton C. McCoy A. Das R. Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retro-spective study Ann. Med. Surg. (Lond.) 2020 59 207 216 10.1016/j.amsu.2020.09.044 33042536 PMC7532803 · DOI
  38. [39]Nhu V.-H. Shirzadi A. Shahabi H. Singh S.K. Al-Ansari N. Clague J.J. Jaafari A. Chen W. Miraki S. Dou J. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms Int. J. Environ. Res. Public Health 2020 17 2749 10.3390/ijerph17082749 32316191 PMC7215797 · DOI
  39. [40]Alghamdi M. Al-Mallah M. Keteyian S. Brawner C. Ehrman J. Sakr S. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project PLoS ONE 2017 12 e0179805 10.1371/journal.pone.0179805 28738059 PMC5524285 · DOI
  40. [41]Azarkhish I. Raoufy M.R. Gharibzadeh S. Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data J. Med. Syst. 2012 36 2057 2061 10.1007/s10916-011-9668-3 21503744 · DOI
  41. [42]Liu B. Guo S. Ding B. Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation Int. J. Environ. Res. Public Health 2020 17 516 10.3390/ijerph17020516 31947558 PMC7013832 · DOI
  42. [43]Lim J. Big Data-Driven Determinants of Length of Stay for Patients with Hip Fracture Int. J. Environ. Res. Public Health 2020 17 4949 10.3390/ijerph17144949 32659953 PMC7400185 · DOI
  43. [44]Kunneman M. Gionfriddo M.R. Toloza F.J. Gärtner F.R. Spencer-Bonilla G. Hargraves I.G. Erwin P.J. Montori V.M. Humanistic communication in the evaluation of shared decision making: A systematic review Patient Educ. Couns. 2019 102 452 466 10.1016/j.pec.2018.11.003 30458971 · DOI
  44. [45]Cheng W.-J. Hung K.-C. Ho C.-H. Yu C.-H. Chen Y.-C. Wu M.-P. Chu C.-C. Chang Y.-J. Satisfaction in parturients receiving epidural analgesia after prenatal shared decision-making intervention: A prospective, before-and-after cohort study BMC Pregnancy Child. 2020 20 1 13 10.1186/s12884-020-03085-6 32689958 PMC7370438 · DOI
  45. [46]Hsieh M.-H. Chen C.-M. Hsieh C.-C. Chao C.-M. Lai C.-C. Hsieh M.-J. An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units J. Clin. Med. 2018 7 240 10.3390/jcm7090240 30149612 PMC6162865 · DOI
  46. [47]Kwon J.M. Kim K.H. Jeon K.H. Lee S.E. Lee H.Y. Cho H.J. Choi J.O. Jeon E.S. Kim M.S. Kim J.J. Artificial in-telligence algorithm for predicting mortality of patients with acute heart failure PLoS ONE 2019 14 e0219302 10.1371/journal.pone.0219302 31283783 PMC6613702 · DOI
  47. [48]Syed L. Jabeen S. S. M. Alsaeedi A. Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques Futur. Gener. Comput. Syst. 2019 101 136 151 10.1016/j.future.2019.06.004 · DOI
  48. [49]Rau C.-S. Wu S.-C. Chuang J.-F. Liu H.-T. Chien P.-C. Hsieh C.-H. Machine Learning Models of Survival Prediction in Trauma Patients J. Clin. Med. 2019 8 799 10.3390/jcm8060799 31195670 PMC6616432 · DOI
  49. [50]Watson D.S. Krutzinna J. Bruce I.N. Griffiths C.E. McInnes I.B. Barnes M.R. Floridi L. Clinical applications of machine learning algorithms: Beyond the black box BMJ 2019 364 l886 10.1136/bmj.l886 30862612 · DOI · Paper page

Cited by

Sorted by citations

Authors
Chang, Y.-J., Hung, K.-C., Wang, L.-K., Yu, C.-H., Chen, C.-K., Tay, H.-T., Wang, J.-J., & Liu, C.-F.
Affiliations
  • Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
  • Department of Thoracic Surgery, Chi Mei Medical Center, Tainan 710, Taiwan
  • Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan 710, Taiwan
  • Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2021-03-08
Volume
18
Issue
5
Pages
2713
ISSN
1660-4601
DOI
10.3390/ijerph18052713
PMC
PMC7967444
Cited by
35
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Chi Mei Medical Center (CMFHR10928)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review