MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Open accessAbstract
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of dataset representation bias as well as interpretability and prediction fairness of deep learning models for in-hospital mortality prediction. In terms of interpretability, we observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models; (2) demographic features are important for prediction. In terms of fairness, we observe that (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) all of the studied mortality predictors are generally fair while the IMV-LSTM (Interpretable Multi-Variable Long Short-Term Memory) model provides the most accurate and unbiased predictions across all protected groups. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors.
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MIMIC-IV was selected due to its large-scale, detailed documentation, generalizable formatting, corroborated use in AI/ML applications, and prior evaluations in terms of AI/ML interpretability, fairness, and bias.
“We selected MIMIC-IV due to its large-scale, detailed documentation, generalizable formatting, corroborated use in AI/ML applications35, and prior evaluations in terms of AI/ML interpretability, fairness, and bias36.”
From Integrated multimodal artificial intelligence framework for healthcare applications (2022) · cites this paper as [36]
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655 · see citing works
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
Jain, S., & Wallace, B. C. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-1357 · Cited by 2,287 · see citing works
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al. (2018) · arXiv · doi:10.48550/arxiv.1810.01943 · Cited by 1,581 · see citing works
SmoothGrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467 · see citing works
Data preprocessing techniques for classification without discrimination
Kamiran, F., & Calders, T. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0463-8 · Cited by 1,305 · see citing works
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2017) · arXiv · doi:10.48550/arxiv.1711.11279 · Cited by 1,240 · see citing works
Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164 · see citing works
The accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071 · see citing works
Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 1× · see citing works
“Interestingly, it has been proven that some of the competing definitions and statistics previously proposed are mutually exclusive [74].”
BackgroundInherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · arXiv · doi:10.48550/arxiv.1609.05807 · Cited by 941 · see citing works
Song, W., Shi, C., Xiao, Z., Duan, Z., Xu, Y., Zhang, M., & Tang, J. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357925 · Cited by 869 · see citing works
Lambrecht, A., & Tucker, C. (2019) · Management Science · doi:10.1287/mnsc.2018.3093 · Cited by 854 · see citing works
Raji, I. D., & Buolamwini, J. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314244 · Cited by 651 · see citing works
Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000249929 · Cited by 634 · see citing works
Polynomial calculation of the Shapley value based on sampling
Castro, J., Gómez, D., & Tejada, J. (2009) · Computers & Operations Research · doi:10.1016/j.cor.2008.04.004 · Cited by 601 · see citing works
An Efficient Explanation of Individual Classifications using Game Theory
Štrumbelj, E., & Kononenko, I. (2010) · Repository of the University of Ljubljana (University of Ljubljana) · doi:10.5555/1756006.1756007 · Cited by 570 · see citing works
Interpretable Machine Learning
Molnar, C. (2020) · Cited by 506 · see citing works
Attention is not ExplanationPreprint
Jain, S., & Wallace, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10186 · Cited by 468 · see citing works
The Measure and Mismeasure of Fairness
Corbett-Davies, S., Gaebler, J. D., Nilforoshan, H., Shroff, R., & Goel, S. (2018) · arXiv · doi:10.48550/arxiv.1808.00023 · Cited by 444 · see citing works
Companion Proceedings of the Web Conference 2021
Leskovec, J., Leskovec, J., Grobelnik, M., Najork, M., Tang, J., Zia, L., Slovenia, W. '. T. W. C. 2. 2. L., Association for Computing Machinery-Digital Library, ACM Special Interest Group on Hypertext, H. A. W., & SIGs, A. (2021) · doi:10.1145/3442442 · Cited by 439 · see citing works
A Benchmark for Interpretability Methods in Deep Neural NetworksPreprint
Hooker, S., Erhan, D., Kindermans, P.-J., & Kim, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10758 · Cited by 438 · see citing works
Benchmarking deep learning models on large healthcare datasets
Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.04.007 · Cited by 406 · see citing works
Self-citationCan AI Help Reduce Disparities in General Medical and Mental Health Care?
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.167 · Cited by 401 · see citing works
Inherent Tradeoffs in the Fair Determination of Risk Scores
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works
Recommendations as Treatments: Debiasing Learning and EvaluationPreprint
Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., & Joachims, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.05352 · Cited by 296 · see citing works
Interpretable Basis Decomposition for Visual Explanation
Zhou, B., Sun, Y., Bau, D., & Torralba, A. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_8 · Cited by 270 · see citing works
Sharma, S., Henderson, J., & Ghosh, J. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375812 · Cited by 226 · see citing works
Fairness in Deep Learning: A Computational Perspective
Du, M., Yang, F., Zou, N., & Hu, X. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.3000681 · Cited by 218 · see citing works
Why is my classifier discriminatory?
Chen, I., Johansson, F. D., & Sontag, D. (2018) · arXiv · doi:10.48550/arxiv.1805.12002 · Cited by 185 · cited 2× · see citing works
“(4) Explanation with high-level concepts30–32.”
BackgroundExploring Interpretable LSTM Neural Networks over Multi-Variable DataPreprint
Guo, T., Lin, T., & Antulov-Fantulin, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12034 · Cited by 143 · see citing works
Proceedings of the 2016 ACM Conference on Economics and Computation
(2016) · doi:10.1145/2940716 · Cited by 140 · see citing works
Achieving Fairness through Adversarial Learning: an Application to Recidivism PredictionPreprint
Wadsworth, C., Vera, F., & Piech, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00199 · Cited by 139 · see citing works
Fairness without Demographics through Adversarially Reweighted LearningPreprint
Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., & Ed, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13114 · Cited by 135 · see citing works
Association Between Immigrant Status and End-of-Life Care in Ontario, Canada
Yarnell, C. J., Fu, L., Manuel, D. G., Tanuseputro, P., Stukel, T. A., Pinto, R. L., Scales, D. C., Laupacis, A., & Fowler, R. (2017) · JAMA · doi:10.1001/jama.2017.14418 · Cited by 128 · see citing works
Hurtful words: quantifying biases in clinical contextual word embeddings
Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning · doi:10.1145/3368555.3384448 · Cited by 122 · see citing works
Invariant Representations without Adversarial TrainingPreprint
Moyer, D., Gao, S., Brekelmans, R., Steeg, G. V., & Galstyan, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09458 · Cited by 121 · see citing works
AI and Algorithmic Bias: Source, Detection, Mitigation and Implications
Fu, R., Huang, Y. H., & Singh, P. V. (2020) · doi:10.1287/educ.2020.0215 · Cited by 119 · see citing works
Benchmarking Deep Learning Interpretability in Time Series PredictionsPreprint
Ismail, A. A., Gunady, M. K., Bravo, H. C., & Feizi, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13924 · Cited by 118 · see citing works
How Important Is a Neuron?Preprint
Dhamdhere, K., Sundararajan, M., & Yan, Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12233 · Cited by 100 · see citing works
A Roadmap for a Rigorous Science of Interpretability.Preprint
Doshi‐Velez, F., & Kim, B. (2017) · arXiv (Cornell University) · Cited by 97 · see citing works
Explaining Explanations: Axiomatic Feature Interactions for Deep NetworksPreprint
Janizek, J. D., Sturmfels, P., & Lee, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04138 · Cited by 84 · see citing works
Clinical Intervention Prediction and Understanding using Deep NetworksPreprint
Suresh, H., Hunt, N. J., Johnson, A. E. W., Celi, L. A., Peter Szolovits, B., & Ghassemi, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08498 · Cited by 81 · see citing works
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
Davoust, A., & Rovatsos, M. (2020) · doi:10.1145/3375627 · Cited by 81 · see citing works
Invariant Representations without Adversarial Training
Moyer, D., Gao, S., Brekelmans, R., et al. (2018) · neural information processing systems · Cited by 75 · see citing works
Evaluating Attribution for Graph Neural Networks
Sánchez-Lengeling, B., Wei, J. N., Lee, B. K., Reif, E., Wang, P. Y., Qian, W. W., McCloskey, K., Colwell, L. J., & Wiltschko, A. B. (2020) · neural information processing systems · Cited by 74 · see citing works
Detecting statistical interactions with additive groves of trees
Sorokina, D., Caruana, R., Riedewald, M., & Fink, D. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390282 · Cited by 71 · see citing works
The Influence of Race/Ethnicity and Education on Family Ratings of the Quality of Dying in the ICU
Lee, J. J., Long, A. C., Curtis, J. R., & Engelberg, R. A. (2015) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2015.08.008 · Cited by 70 · see citing works
Influence-Directed Explanations for Deep Convolutional Networks
Leino, K. R. M., Sen, S., Datta, A., Fredrikson, M., & Li, L. (2018) · IEEE International Test Conference (ITC) · doi:10.1109/test.2018.8624792 · Cited by 60 · see citing works
Addressing bias in prediction models by improving subpopulation calibration
Barda, N., Yona, G., Rothblum, G. N., Greenland, P., Leibowitz, M., Balicer, R. D., Bachmat, E., & Dagan, N. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa283 · Cited by 59 · see citing works
Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
Kleinberg, J. M., & Mullainathan, S. (2019) · National Bureau of Economic Research · doi:10.3386/w25854 · Cited by 54 · see citing works
Minimax Pareto Fairness: A Multi Objective Perspective
Martínez, N., Bertrán, M., & Sapiro, G. (2020) · PubMed · doi:10.48550/arxiv.2011.01821 · Cited by 54 · see citing works
Proceedings of the ACM Conference on Health, Inference, and Learning
(2020) · doi:10.1145/3368555 · Cited by 53 · see citing works
Interpretable machine learning
Molnar, C. (2020) · doi:10.58248/pn633 · Cited by 51 · see citing works
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIPreprint
Wang, S., McDermott, M. B. A., Chauhan, G., Hughes, M. C., Naumann, T., & Ghassemi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08322 · Cited by 47 · see citing works
Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box ModelsPreprint
Adebayo, J., & Kagal, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04967 · Cited by 44 · see citing works
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · arXiv · doi:10.48550/arxiv.2003.11515 · Cited by 37 · see citing works
How does this interaction affect me? Interpretable attribution for feature interactionsPreprint
Tsang, M., Rambhatla, S., & Liu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10965 · Cited by 37 · see citing works
Self-citationTsang, M., Liu, H., Purushotham, S., Murali, P., & Liu, Y. (2018) · neural information processing systems · Cited by 36 · see citing works
Self-citationKleinberg, J. M., & Mullainathan, S. (2019) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3328526.3329621 · Cited by 36 · see citing works
Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models.
Grimsley, C., Mayfield, E., & Bursten, J. R. S. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 32 · see citing works
Measuring Unfairness Through Game-Theoretic Interpretability
Cesaro, J., & Cozman, F. G. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43823-4_22 · Cited by 16 · see citing works
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP
Chen, J., Berlot-Attwell, I., Wang, X., Hossain, S., & Rudzicz, F. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.33 · Cited by 16 · see citing works
The Shapley Taylor Interaction Index
Sundararajan, M., Dhamdhere, K., & Agarwal, A. (2020) · International Conference on Machine Learning · Cited by 11 · see citing works
Explaining an increase in predicted risk for clinical alerts
Hardt, M., Rajkomar, A., Flores, G., Dai, A. M., Howell, M., Corrado, G. S., Cui, C. M., & Hardt, M. (2020) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3368555.3384460 · Cited by 9 · see citing works
Computationally Efficient Measures of Internal Neuron ImportancePreprint
Shrikumar, A., Su, J., & Kundaje, A. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09946 · Cited by 6 · see citing works
Fair Predictors under Distribution ShiftPreprint
Singh, H., Singh, R., Mhasawade, V., & Chunara, R. (2019) · arXiv (Cornell University) · Cited by 6 · see citing works
Games for Fairness and Interpretability
Chu, E., Gillani, N., & Makini, S. P. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366424.3384374 · Cited by 5 · see citing works
Cui, S., Pan, W., Zhang, C., & Wang, F. (2020) · arXiv (Cornell University) · Cited by 3 · see citing works
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLPPreprint
Chen, J., Berlot-Attwell, I., Hossain, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09625 · Cited by 2 · see citing works
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm UtilityPreprint
Cui, S., Pan, W., Zhang, C., & Wang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08267 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Tsang, M., Cheng, D. & Liu, Y. Detecting statistical interactions from neural network weights. In International Conference on Learning Representations (2018).
- Ghorbani, A., Wexler, J., Zou, J. Y. & Kim, B. Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems, 9277-9286 (2019).
- Sharma, S., Henderson, J. & Ghosh, J. Certifai: Counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. arXiv preprint arXiv:1905.07857 (2019). · arXiv
- Jabbari, S., Ou, H.-C., Lakkaraju, H. & Tambe, M. An empirical study of the trade-offs between interpretability and fairness. ICML 2020 Work. on Hum. Interpret. Mach. Learn. (2020).
Cited by
Sorted by citations
Integrated multimodal artificial intelligence framework for healthcare applications
Soenksen, L. R., Ma, Y., Zeng, C., Boussioux, L., Villalobos Carballo, K., Na, L., Wiberg, H. M., Li, M. L., Fuentes, I., & Bertsimas, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00689-4 · Cited by 380 · see citing works
“We selected MIMIC-IV due to its large-scale, detailed documentation, generalizable formatting, corroborated use in AI/ML applications 35 , and prior evaluations in terms of AI/ ML interpretability, fairness, and bias 36 .”
MethodPeeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model.
Röösli, E., Bozkurt, S., & Hernandez-Boussard, T. (2022) · Scientific Data · doi:10.1038/s41597-021-01110-7 · Cited by 73 · see citing works
Fair Machine Learning in Healthcare: A Survey
Feng, Q., Du, M., Zou, N., & Hu, X. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3361836 · Cited by 12 · see citing works
- Authors
- Meng, C., Trinh, L., Xu, N., & Liu, Y.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Trinh, Loc
- Venue
- Research Square
- Publisher
- Research Square Platform LLC
- Published
- 2021-04-14
- OpenAlex
- W3132130152
- Cited by
- 21
- References
- 87
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 2 · 2022: 3 · 2021: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- Research Square (acceptedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- MIMIC-IV, interpretability, algorithmic fairness, in-hospital mortality prediction, representation bias, demographic bias, protected groups, feature importance, artificial intelligence in healthcare, intensive care
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