Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Open accessAbstract
Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time during general anaesthesia, predicts the risk of hypoxemia and provides explanations of the risk factors. The system, which was trained on minute-by-minute data from the electronic medical records of over fifty thousand surgeries, improved the performance of anaesthesiologists when providing interpretable hypoxemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of hypoxemia events, with this system's assistance they would anticipate 30% of them, a large portion of which may benefit from early intervention because they are associated with modifiable factors. The system can help improve the clinical understanding of hypoxemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics.
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AI systems have been shown to outperform humans in various decision-making tasks across many domains.
“Artificial Intelligence (AI) systems have been shown to outperform humans in various decisionmaking tasks across many domains.”
From Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial Explanations (2025) · cites this paper as [49]
The Results section (“Prescience improves anaesthesiologist’s ability to predict hypoxemia”; page not supplied) reports that Prescience was considerably more accurate than anaesthesiologists on initial and real-time hypoxemia prediction. It does not support the broader claim that AI systems outperform humans across many domains and various decision-making tasks. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMany empirical studies on human-AI decision-making did not observe complementary performance.
“However, many empirical studies on human-AI decision-making did not observe complementary performance [6,16,37,52,58,61,68,82,84,87].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [61]
Lundberg et al. (2018b) studied the ability of explanations to assist physicians to detect hypoxemia risk during surgery.
“Lundberg et al. (2018b) studied the ability of explanations to assist physicians to detect hypoxemia risk during surgery”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lundberg et al., 2018b
Lundberg et al. (2018b) failed to isolate the marginal effect of explanations by not comparing the ML model plus explanations to just the ML model.
“However, their experiment failed to isolate the marginal effect of the explanations by failing to compare the performance of the ML model þ explanations to just the ML model.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lundberg et al., 2018b
Lundberg et al. (2018b) showed that their system Prescience, armed with an ML model and SHAP explanations, was able to outperform anesthesiologists in identifying real-time hypoxemia risk.
“They showed that their system -Prescience -armed with an ML model and SHAP explanations was able to outperform the anesthesiologists in identifying realtime hypoxemia risk.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lundberg et al., 2018b
Lundberg et al. (2018b) failed to evaluate the incremental impact of the explainable ML method because they did not compare users with data and ML prediction (no explanations) to users with all three pieces of information.
“However, the authors fail to compare the performance of users with access to data and ML prediction (no explanations) to the user performance with all three pieces of information. We argue that this is an essential component of an experimental design aimed at evaluating the incremental impact of explainable ML, and this oversight prevents us from attributing that performance increment to the”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lundberg et al. (2018b)
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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Self-citation"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works
Garg, A. X., Adhikari, N. K. J., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J., Sam, J., & Haynes, R. B. (2005) · JAMA · doi:10.1001/jama.293.10.1223 · Cited by 2,936 · see citing works
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286 · see citing works
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works
Consistent Individualized Feature Attribution for Tree Ensembles
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430 · see citing works
Self-citationMyles, P. S., Leslie, K., McNeil, J. J., Forbes, A. B., & Chan, M. (2004) · The Lancet · doi:10.1016/s0140-6736(04)16300-9 · Cited by 1,300 · see citing works
Anesthesia Awareness and the Bispectral Index
Avidan, M. S., Zhang, L., Burnside, B. A., Finkel, K. J., Searleman, A. C., Selvidge, J. A., Saager, L., Turner, M. S., Rao, S., Bottros, M. M., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0707361 · Cited by 982 · see citing works
The incidence and nature of surgical adverse events in Colorado and Utah in 1992
Gawande, A. A., Thomas, E. J., Zinner, M. J., & Brennan, T. A. (1999) · Surgery · doi:10.1067/msy.1999.98664 · Cited by 977 · see citing works
Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes
Weiser, T. G., Haynes, A. B., Molina, G., Lipsitz, S. R., Esquivel, M. M., Uribe‐Leitz, T., Fu, R., Azad, T. D., Chao, T. E., Berry, W. R., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(15)60806-6 · Cited by 961 · see citing works
The Shapley value : essays in honor of Lloyd S. Shapley
Roth, A. E. (1991) · Cited by 700 · see citing works
Surgical data science for next-generation interventions
Maier‐Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feußner, H., Forestier, G., Giannarou, S., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0132-7 · Cited by 616 · see citing works
A targeted real-time early warning score (TREWScore) for septic shock.
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aab3719 · Cited by 598 · see citing works
Adverse events in surgical patients in Australia
Kable, A. K., Gibberd, R. W., & Spigelman, A. D. (2002) · International Journal for Quality in Health Care · doi:10.1093/intqhc/14.4.269 · Cited by 453 · see citing works
Unintended Return of Consciousness in a Patient during Surgery and General Anesthesia
Mędrzycka-Dąbrowska, W. A., Dąbrowski, S., Gutysz‐Wojnicka, A., Ozga, D., & Wojtaszek, M. (2017) · European Neurology · doi:10.1159/000471510 · Cited by 425 · see citing works
Integrated monitoring and analysis for early warning of patient deterioration
Tarassenko, L., Hann, A., & Young, D. (2006) · British Journal of Anaesthesia · doi:10.1093/bja/ael113 · Cited by 250 · see citing works
Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants
Saria, S., Rajani, A. K., Gould, J. B., Koller, D., & Penn, A. A. (2010) · Science Translational Medicine · doi:10.1126/scitranslmed.3001304 · Cited by 177 · see citing works
The incidence of hypoxemia during surgery: evidence from two institutions
Ehrenfeld, J. M., Funk, L. M., van Schalkwyk, J., Merry, A., Sandberg, W. S., & Gawande, A. A. (2010) · Canadian Journal of Anesthesia/Journal canadien d anesthésie · doi:10.1007/s12630-010-9366-5 · Cited by 136 · see citing works
Kooij, F. O., Klok, T., Hollmann, M. W., & Kal, J. E. (2008) · Anesthesia & Analgesia · doi:10.1213/ane.0b013e31816194fb · Cited by 124 · see citing works
Memarian, N., Kim, S., Dewar, S. R., Engel, J., & Staba, R. J. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.06.008 · Cited by 109 · see citing works
Guay, J. M. Y., & Ochroch, E. A. (2015) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011151.pub2 · Cited by 94 · see citing works
Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems.
Nair, B., Gabel, E., Hofer, I., Schwid, H., & Cannesson, M. (2017) · Anesthesia and Analgesia · doi:10.1213/ane.0000000000001636 · Cited by 75 · see citing works
Self-citationEpstein, R., Dexter, F., & Patel, N. (2015) · Anesthesia and Analgesia · doi:10.1213/ane.0000000000000677 · Cited by 62 · see citing works
Guay, J. M. Y., Ochroch, E. A., & Kopp, S. L. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011151.pub3 · Cited by 62 · see citing works
Dunham, C. M., Hileman, B. M., Hutchinson, A. E., Chance, E. A., & Huang, G. S. (2014) · BMC Anesthesiology · doi:10.1186/1471-2253-14-43 · Cited by 41 · see citing works
Multi-Step Ahead Predictions for Critical Levels in Physiological Time Series
ElMoaqet, H., Tilbury, D. M., & Ramachandran, S. K. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2561974 · Cited by 37 · see citing works
Learning (predictive) risk scores in the presence of censoring due to interventions
Dyagilev, K., & Saria, S. (2015) · Machine Learning · doi:10.1007/s10994-015-5527-7 · Cited by 36 · see citing works
Increasing body mass index and the incidence of intraoperative hypoxemia
Kendale, S. M., & Blitz, J. D. (2016) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2016.03.020 · Cited by 35 · see citing works
Phenotyping of Clinical Time Series with LSTM Recurrent Neural NetworksPreprint
Lipton, Z. C., Kale, D. C., & Wetzel, R. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.07641 · Cited by 31 · see citing works
Strachan, L. M., & Noble, D. W. (2001) · PubMed · Cited by 23 · see citing works
Lumachi, F., Marzano, B., Fanti, G., Basso, S. M. M., Mazza, F., & Chiara, G. B. (2010) · PubMed · Cited by 17 · see citing works
Summers, R. L., Pipke, M., Wegerich, S., et al. (2014) · PubMed · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [25]National Center for Health Statistics, Health, United States, 2016: With Chartbook on Long-term Trends in Health, Cent. Dis. Control, 314– 317 (2017). 28910066
- [34]Guay J, Ochroch EA, in Cochrane Database of Systematic Reviews, Guay J, Ed. (John Wiley & Sons, Ltd, Chichester, UK, 2015), p. CD011151.
- [37]Roth AE, The Shapley Value - Cambridge University PressCambridge Univ. Press (1988) (available at http://www.cambridge.org/catalogue/catalogue.asp?isbn=0511829728).
Cited by
Sorted by citations
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works
Self-citationHigh-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917 · see citing works
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834 · see citing works
Explainable Deep Learning Models in Medical Image Analysis
Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020) · Journal of Imaging · doi:10.3390/jimaging6060052 · Cited by 718 · see citing works
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750 · see citing works
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757 · see citing works
“Indeed, at first glance, it appears that researchers have already confirmed the utility of explanations on tasks ranging from medical diagnosis [10, 47] and data annotation [57] to deception detection [36].”
BackgroundInfluentialZitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works
Explainable machine learning in deployment
Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375624 · Cited by 633 · see citing works
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance
Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5285 · Cited by 466 · see citing works
Metabolomic profiles predict individual multidisease outcomes
Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01980-3 · Cited by 406 · see citing works
Interpretability of machine learning-based prediction models in healthcare
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1379 · Cited by 541 · see citing works
“For example, SHAP was used in interpretation of predictions for the prevention of hypoxaemia during surgery (Lundberg, et al., 2018), which increased the anaesthesiologists anticipation of hypoxaemia events by 15%, or in the detection of acute intracranial haemorrhage from images of CT scans (Lee,…”
MethodExplanations Can Reduce Overreliance on AI Systems During Decision-Making
Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., & Krishna, R. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579605 · Cited by 402 · see citing works
El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363 · see citing works
Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. M., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00343-w · Cited by 300 · see citing works
Self-citationArtificial intelligence in surgery
Varghese, C., Harrison, E. M., O’Grady, G., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-02970-3 · Cited by 287 · see citing works
Abbas, Q., Jeong, W., & Lee, S. W. (2025) · Healthcare · doi:10.3390/healthcare13172154 · Cited by 236 · see citing works
Self-citationHu, C., Li, L., Huang, W., Wu, T., Xu, Q., Liu, J., & Hu, B. (2022) · Infectious Diseases and Therapy · doi:10.1007/s40121-022-00628-6 · Cited by 160 · see citing works
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102941 · Cited by 170 · see citing works
Looking beyond the hype: applied AI and machine learning in translational medicine
Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159 · see citing works
Data-driven de novo design of super-adhesive hydrogels
Liao, H., Hu, S., Yang, H., Wang, L., Tanaka, S., Takigawa, I., Li, W., Fan, H., & Gong, J. P. (2025) · Nature · doi:10.1038/s41586-025-09269-4 · Cited by 169 · see citing works
“To assess the influence of ϕ i on F a , we used SHAP (SHaply Additive exPlanations) 39 with the RFR model trained on the final 341-hydrogel dataset.”
Risk assessment with gut microbiome and metabolite markers in NAFLD development
Leung, H., Long, X., Ni, Y., Qian, L., Nychas, E., Siliceo, S. L., Pohl, D., Hanhineva, K., Liu, Y., Xu, A., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abk0855 · Cited by 152 · see citing works
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
Adler, A. I., & Painsky, A. (2022) · Entropy · doi:10.3390/e24050687 · Cited by 149 · see citing works
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Chen, V., Liao, Q. V., Wortman Vaughan, J., & Bansal, G. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610219 · Cited by 145 · see citing works
Zaitsev, A., Chelushkin, M., Dyikanov, D., Cheremushkin, I., Shpak, B., Nomie, K., Zyrin, V., Nuzhdina, E., Lozinsky, Y., Zotova, A., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.006 · Cited by 139 · see citing works
Inkpen, K., Chappidi, S., Mallari, K., Nushi, B., Ramesh, D., Michelucci, P., Mandava, V., Vepřek, L. H., & Quinn, G. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3534561 · Cited by 132 · see citing works
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · see citing works
Practical early prediction of students’ performance using machine learning and eXplainable AI
Jang, Y., Choi, S., Jung, H., & Kim, H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11120-6 · Cited by 114 · see citing works
Jiang, Z., Bo, L., Xu, Z., Song, Y., Wang, J., Wen, P., Wan, X., Yang, T., Deng, X., & Bian, J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106040 · Cited by 86 · see citing works
Bioactivity descriptors for uncharacterized chemical compounds
Bertoni, M., Duran-Frigola, M., Badia-i-Mompel, P., Pauls, E., Orozco-Ruiz, M., Guitart-Pla, O., Alcalde, V., Diaz, V. M., Berenguer-Llergo, A., Brun-Heath, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24150-4 · Cited by 92 · see citing works
Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
Wang, Q., Huang, K., Chandak, P., Zitnik, M., & Gehlenborg, N. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209435 · Cited by 82 · see citing works
Arina, P., Kaczorek, M. R., Hofmaenner, D. A., Pisciotta, W., Refinetti, P., Singer, M., Mazomenos, E. B., & Whittle, J. (2024) · Anesthesiology · doi:10.1097/aln.0000000000004764 · Cited by 81 · see citing works
Trust in AI: why we should be designing for APPROPRIATE reliance
Benda, N. C., Novak, L. L., Reale, C., & Ancker, J. S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab238 · Cited by 76 · see citing works
Hu, C., Li, L., Li, Y., Wang, F., Hu, B., & Peng, Z. (2022) · Infectious Diseases and Therapy · doi:10.1007/s40121-022-00671-3 · Cited by 63 · see citing works
Explainable machine learning for public policy: Use cases, gaps, and research directions
Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73 · see citing works
Zhu, B., Shin, U., & Shoaran, M. (2021) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3112756 · Cited by 67 · see citing works
Pereira, F. D., Fonseca, S. C., Oliveira, E. H. T., Cristea, A. I., Bellhauser, H., Rodrigues, L., Oliveira, D. B. F., Isotani, S., & Carvalho, L. S. G. (2021) · IEEE Access · doi:10.1109/access.2021.3105956 · Cited by 63 · see citing works
Zytek, A., Liu, D., Vaithianathan, R., & Veeramachaneni, K. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114864 · Cited by 62 · see citing works
Fok, R., & Weld, D. S. (2023) · arXiv · doi:10.48550/arxiv.2305.07722 · Cited by 54 · see citing works
Joshi, M., Mecklai, K., Rozenblum, R., & Samal, L. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac022 · Cited by 46 · see citing works
Jiang, Z., Bo, L., Wang, L., Xie, Y., Cao, J., Yao, Y., Lu, W., Deng, X., Yang, T., & Bian, J. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107772 · Cited by 38 · see citing works
Artificial intelligence for dementia prevention
Newby, D., Orgeta, V., Marshall, C. R., Lourida, I., Albertyn, C. P., Tamburin, S., Raymont, V., Veldsman, M., Koychev, I., Bauermeister, S., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13463 · Cited by 42 · see citing works
Prelaj, A., Galli, E. G., Miskovic, V., Pesenti, M., Viscardi, G., Pedica, B., Mazzeo, L., Bottiglieri, A., Provenzano, L., Spagnoletti, A., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1078822 · Cited by 39 · see citing works
Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes
Maritsch, M., Föll, S., Lehmann, V., Bérubé, C., Kraus, M., Feuerriegel, S., Kowatsch, T., Züger, T., Stettler, C., Fleisch, E., et al. (2020) · Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3334480.3382808 · Cited by 35 · see citing works
de Jong, S., Paananen, V., Tag, B., & van Berkel, N. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710946 · Cited by 32 · see citing works
Powell, W., Song, X., Mohamed, Y., Walsh, D., Parks, E. J., McMahon, T. M., Khan, M., & Waitman, L. R. (2023) · Obesity · doi:10.1002/oby.23859 · Cited by 32 · see citing works
Chmiel, F. P., Burns, D. K., Azor, M., Borca, F., Boniface, M. J., Zlatev, Z. D., White, N. M., Daniels, T. W. V., & Kiuber, M. (2020) · doi:10.1101/2020.12.02.20239194 · Cited by 30 · see citing works
Explainable AI decision support improves accuracy during telehealth strep throat screening
Gomez, C., Smith, B.-L., Zayas, A., Unberath, M., & Canares, T. (2024) · Communications Medicine · doi:10.1038/s43856-024-00568-x · Cited by 30 · see citing works
Potential uses of AI for perioperative nursing handoffs: a qualitative study
King, C. R., Shambe, A., & Abraham, J. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad015 · Cited by 27 · see citing works
“60 Very few AI studies have gone beyond initial development phases or shown benefits to stakeholders, 49,50 and the more developed use-cases are often highly specialized, such as rapid-response-team alarms.”
BackgroundPredicting self-intercepted medication ordering errors using machine learning
King, C. R., Abraham, J., Fritz, B. A., Cui, Z., Galanter, W., Chen, Y., & Kannampallil, T. (2021) · PLOS ONE · doi:10.1371/journal.pone.0254358 · Cited by 22 · see citing works
Current challenges in adopting machine learning to critical care and emergency medicine
Kang, C.-Y., & Yoon, J. H. (2023) · Clinical and Experimental Emergency Medicine · doi:10.15441/ceem.23.041 · Cited by 20 · see citing works
Yang, J., Wang, T., Li, K., & Wāng, Y. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2024.04.022 · Cited by 15 · see citing works
“SHAP can conduct local and global inter-pretability concurrently, and it has a solid theoretical foundation compared to other approaches [55].”
User-centered design of a machine learning dashboard for prediction of postoperative complications
Fritz, B. A., Pugazenthi, S., Budelier, T. P., Tellor Pennington, B. R., King, C. R., Avidan, M. S., & Abraham, J. (2024) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006577 · Cited by 15 · see citing works
Harnessing AI in critical care: opportunities, challenges and key steps for success
Kalimouttou, A., Stevens, R. D., & Pirracchio, R. (2026) · Thorax · doi:10.1136/thorax-2024-222125 · Cited by 12 · see citing works
Assessment of the timeliness and robustness for predicting adult sepsis
Guan, Y., Wang, X., Chen, X., Yi, D., Chen, L., & Jiang, X. (2021) · iScience · doi:10.1016/j.isci.2021.102106 · Cited by 7 · see citing works
Wu, L., Mai, L., Wang, H., Huang, J., He, X., Zhan, X., Khalemsky, A., Kumar, V. A., Paxton, J. H., Tsilimingras, D., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02522-8 · Cited by 8 · see citing works
- Authors
- Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., & Lee, S.-I.
- Affiliations
- University of Washington (US)
- VA Puget Sound Health Care System (US)
- Seattle Children's Hospital (US)
- Corresponding author
- Lee, Su-In
- Venue
- Nature Biomedical Engineering
- Published
- 2018-10-10
- Volume
- 2
- Issue
- 10
- Pages
- 749-760
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC6467492
- OpenAlex
- W2892741787
- Cited by
- 1,967
- References
- 35
- Citations by year
- 2026: 235 · 2025: 367 · 2024: 342 · 2023: 312 · 2022: 305 · 2021: 234 · 2020: 131 · 2019: 35 · 2018: 4
- FWCI
- 56.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R35GM128638)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Keywords
- general anesthesia, explainable machine learning, risk factors, electronic health records, early intervention
- MeSH terms
- Machine Learning, Anesthesiologists, Adult, Aged, Aged, 80 and over, Anesthesia, General, Hypoxia, Female, Humans, Male, Middle Aged, Risk Factors, ROC Curve, Surgical Procedures, Operative, Area Under Curve, Electronic Health Records
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