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Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust.
Auditing pointwise reliability subsequent to training
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Human-centered tools for coping with imperfect algorithms during medical decision-making
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The sofa (sepsis-related organ failure assessment) score to describe organ dysfunction/failure
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Prytherch, D. R., Smith, G. B., Schmidt, P. E., and Featherstone, P. I. (2010) · 2010
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Perceptions of geoengineering: public attitudes, stakeholder perspectives, and the challenge of ‘upstream’engagement
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Evaluating alert fatigue over time to ehr-based clinical trial alerts: findings from a randomized controlled study
Embi, P. J. and Leonard, A. C. (2012) · 2012
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Writing interview protocols and conducting interviews: Tips for students new to the field of qualitative research
Jacob, S. A. and Furgerson, S. P. (2012) · 2012
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Supervised patient similarity measure of heterogeneous patient records
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The ability of the national early warning score (news) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death
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Robust learning under uncertain test distributions: Relating covariate shift to model misspecification
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
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Distilling knowledge from deep networks with applications to healthcare domain
Che, Z., Purushotham, S., Khemani, R., and Liu, Y. (2015) · 2015
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Clinician perception of the effectiveness of an automated early warning and response system for sepsis in an academic medical center
Guidi, J. L., Clark, K., Upton, M. T., Faust, H., Umscheid, C. A., Lane-Fall, M. B., Mikkelsen, M. E., Schweickert, W. D., Vanzandbergen, C. A., Betesh, J., et al · 2015
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Distilling the knowledge in a neural network
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Development, implementation, and impact of an automated early warning and response system for sepsis
Umscheid, C. A., Betesh, J., VanZandbergen, C., Hanish, A., Tait, G., Mikkelsen, M. E., French, B., and Fuchs, B. D. (2015) · 2015
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Falling rule lists
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Understanding neural networks through deep visualization
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H. (2015) · 2015
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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Choi, E., Bahadori, M. T., Sun, J., Kulas, J., Schuetz, A., and Stewart, W. (2016) · 2016
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Deep and confident prediction for time series at uber
Zhu, L. and Laptev, N. (2017) · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. (2018) · 2018
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Interpretable machine learning in healthcare
Ahmad, M. A., Eckert, C., and Teredesai, A. (2018) · 2018
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The stakes of uncertainty: Developing and integrating machine learning in clinical care
Elish, M. C. (2018) · 2018
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A survey of methods for explaining black box models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D. (2018) · 2018
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Validation of deep-learning-based triage and acuity score using a large national dataset
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Piloting electronic medical record–based early detection of inpatient deterioration in community hospitals
Escobar, G. J., Turk, B. J., Ragins, A., Ha, J., Hoberman, B., LeVine, S. M., Ballesca, M. A., Liu, V., and Kipnis, P. (2016) · 2016
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Uncertainty in deep learning
Gal, Y. (2016) · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Kim, B., Khanna, R., and Koyejo, O. O. (2016) · 2016
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Interpretable decision sets: A joint framework for description and prediction
Lakkaraju, H., Bach, S. H., and Leskovec, J. (2016) · 2016
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The mythos of model interpretability
Lipton, Z. C. (2016) · 2016
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Toward a better understanding of task demands, workload, and performance during physician-computer interactions
Mazur, L. M., Mosaly, P. R., Moore, C., Comitz, E., Yu, F., Falchook, A. D., Eblan, M. J., Hoyle, L. M., Tracton, G., Chera, B. S., et al · 2016
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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
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., et al · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2018) · 2018
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Model cards for model reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T. (2018) · 2018
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Narayanan, M., Chen, E., He, J., Kim, B., Gershman, S., and Doshi-Velez, F. (2018) · 2018
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Towards accountable ai: Hybrid human-machine analyses for characterizing system failure
Nushi, B., Kamar, E., and Horvitz, E. (2018) · 2018
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Manipulating and measuring model interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Vaughan, J. W., and Wallach, H. (2018) · 2018
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Anchors: High-precision model-agnostic explanations
Ribeiro, M. T., Singh, S., and Guestrin, C. (2018) · 2018
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Counterfactual normalization: proactively addressing dataset shift using causal mechanisms
Subbaswamy, A. and Saria, S. (2018) · 2018
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Prediction of cardiac arrest from physiological signals in the pediatric icu
Tonekaboni, S., Mazwi, M., Laussen, P., Eytan, D., Greer, R., Goodfellow, S. D., Goodwin, A., Brudno, M., and Goldenberg, A. (2018) · 2018
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Machine learning in medicine: Addressing ethical challenges
Vayena, E., Blasimme, A., and Cohen, I. G. (2018) · 2018
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Beyond sparsity: Tree regularization of deep models for interpretability
Wu, M., Hughes, M. C., Parbhoo, S., Zazzi, M., Roth, V., and Doshi-Velez, F. (2018) · 2018
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Raim: Recurrent attentive and intensive model of multimodal patient monitoring data
Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O., and Sun, J. (2018) · 2018
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Explaining therapy predictions with layer-wise relevance propagation in neural networks
Yang, Y., Tresp, V., Wunderle, M., and Fasching, P. A. (2018) · 2018
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Minimal impact of implemented early warning score and best practice alert for patient deterioration
Bedoya, A. D., Clement, M. E., Phelan, M., Steorts, R. C., O’brien, C., and Goldstein, B. A. (2019) · 2019
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Attention is not explanation
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Masino, A. J., Harris, M. C., Forsyth, D., Ostapenko, S., Srinivasan, L., Bonafide, C. P., Balamuth, F., Schmatz, M., and Grundmeier, R. W. (2019) · 2019
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