-
1
-
-
54049107492
-
-
National Patient Safety Agency
-
Thomson, R., Luettel, D., Healey, F. & Scobie, S. Safer Care for the Acutely Ill Patient: Learning from Serious Incidents (National Patient Safety Agency, 2007).
-
(2007)
Safer Care for the Acutely Ill Patient: Learning from Serious Incidents
-
-
Thomson, R.1
Luettel, D.2
Healey, F.3
Scobie, S.4
-
2
-
-
84938704873
-
A targeted real-time early warning score (TREWscore) for septic shock
-
Henry, K. E., Hager, D. N., Pronovost, P. J. & Saria, S. A targeted real-time early warning score (TREWscore) for septic shock. Sci. Transl. Med. 7, 299ra122 (2015).
-
(2015)
Sci. Transl. Med.
, vol.7
, pp. 299ra122
-
-
Henry, K.E.1
Hager, D.N.2
Pronovost, P.J.3
Saria, S.4
-
3
-
-
85127431078
-
Scalable and accurate deep learning with electronic health records. npj Digit
-
Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digit. Med. 1, 18 (2018).
-
(2018)
Med.
, vol.1
, pp. 18
-
-
Rajkomar, A.1
-
4
-
-
85019718543
-
Development of a multicenter ward-based AKI prediction model
-
Koyner, J. L., Adhikari, R., Edelson, D. P. & Churpek, M. M. Development of a multicenter ward-based AKI prediction model. Clin. J. Am. Soc. Nephrol. 11, 1935–1943 (2016).
-
(2016)
Clin. J. Am. Soc. Nephrol.
, vol.11
, pp. 1935-1943
-
-
Koyner, J.L.1
Adhikari, R.2
Edelson, D.P.3
Churpek, M.M.4
-
5
-
-
85058726453
-
Predicting inpatient acute kidney injury over different time horizons: How early and accurate?
-
American Medical Informatics Association
-
Cheng, P., Waitman, L. R., Hu, Y. & Liu, M. Predicting inpatient acute kidney injury over different time horizons: how early and accurate? In AMIA Annual Symposium Proceedings 565 (American Medical Informatics Association, 2017).
-
(2017)
AMIA Annual Symposium Proceedings
, vol.565
-
-
Cheng, P.1
Waitman, L.R.2
Hu, Y.3
Liu, M.4
-
6
-
-
85053003984
-
The development of a machine learning inpatient acute kidney injury prediction model
-
Koyner, J. L., Carey, K. A., Edelson, D. P. & Churpek, M. M. The development of a machine learning inpatient acute kidney injury prediction model. Crit. Care Med. 46, 1070–1077 (2018).
-
(2018)
Crit. Care Med.
, vol.46
, pp. 1070-1077
-
-
Koyner, J.L.1
Carey, K.A.2
Edelson, D.P.3
Churpek, M.M.4
-
7
-
-
85055475795
-
The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
-
COI: 1:CAS:528:DC%2BC1cXhvF2lsbrF
-
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C. & Faisal, A. A. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat. Med. 24, 1716–1720 (2018).
-
(2018)
Nat. Med.
, vol.24
, pp. 1716-1720
-
-
Komorowski, M.1
Celi, L.A.2
Badawi, O.3
Gordon, A.C.4
Faisal, A.A.5
-
9
-
-
85160722598
-
Disease-Atlas: navigating disease trajectories with deep learning
-
Lim, B. & van der Schaar, M. Disease-Atlas: navigating disease trajectories with deep learning. Proc. Mach. Learn. Res. 85, 137–160 (2018).
-
(2018)
Proc. Mach. Learn. Res.
, vol.85
, pp. 137-160
-
-
Lim, B.1
van der Schaar, M.2
-
10
-
-
85048389332
-
Learning to detect sepsis with a multitask Gaussian process RNN classifier
-
(eds Precup, D. & Teh, Y. W.)
-
Futoma, J., Hariharan, S. & Heller, K. A. Learning to detect sepsis with a multitask Gaussian process RNN classifier. In Proc. International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 1174–1182 (2017).
-
(2017)
Proc. International Conference on Machine Learning
, pp. 1174-1182
-
-
Futoma, J.1
Hariharan, S.2
Heller, K.A.3
-
11
-
-
84968813824
-
Deep Patient: an unsupervised representation to predict the future of patients from the electronic health records
-
COI: 1:CAS:528:DC%2BC28Xot1Gnu7s%3D
-
Miotto, R., Li, L., Kidd, B. A. & Dudley, J. T. Deep Patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016).
-
(2016)
Sci. Rep.
, vol.6
-
-
Miotto, R.1
Li, L.2
Kidd, B.A.3
Dudley, J.T.4
-
12
-
-
85083954099
-
-
Lipton, Z. C., Kale, D. C., Elkan, C. & Wetzel, R. Learning to diagnose with LSTM recurrent neural networks. Preprint at https://arxiv.org/abs/1511.03677 (2016).
-
(2016)
Learning to Diagnose with LSTM Recurrent Neural Networks
-
-
Lipton, Z.C.1
Kale, D.C.2
Elkan, C.3
Wetzel, R.4
-
13
-
-
84991721533
-
Risk prediction with electronic health records: A deep learning approach
-
(eds Venkatasubramanian, S. C. & Meria, W.)
-
Cheng, Y. P. Z. J. H. & Wang, F. Risk prediction with electronic health records: a deep learning approach. In Proc. SIAM International Conference on Data Mining (eds Venkatasubramanian, S. C. & Meria, W.) 432–440 (2016).
-
(2016)
Proc. SIAM International Conference on Data Mining
, pp. 432-440
-
-
Cheng, Y.P.Z.J.H.1
Wang, F.2
-
14
-
-
85031126284
-
Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions
-
AUAI Press Corvallis
-
Soleimani, H., Subbaswamy, A. & Saria, S. Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence (AUAI Press Corvallis, 2017).
-
(2017)
Proc. 33Rd Conference on Uncertainty in Artificial Intelligence
-
-
Soleimani, H.1
Subbaswamy, A.2
Saria, S.3
-
15
-
-
85041956992
-
Personalized risk scoring for critical care prognosis using mixtures of Gaussian process experts
-
&
-
Alaa, A. M., Yoon, J., Hu, S. & van der Schaar, M. Personalized risk scoring for critical care prognosis using mixtures of Gaussian process experts. IEEE Trans. Biomed. Eng. 65, 207–218 (2018).
-
(2018)
IEEE Trans. Biomed. Eng
, vol.65
, pp. 207-218
-
-
Alaa, A.M.1
Yoon, J.2
Hu, S.3
van der Schaar, M.4
-
16
-
-
84940373302
-
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis
-
Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D. & Elhadad, N. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J. Am. Med. Inform. Assoc. 22, 872–880 (2015).
-
(2015)
J. Am. Med. Inform. Assoc.
, vol.22
, pp. 872-880
-
-
Perotte, A.1
Ranganath, R.2
Hirsch, J.S.3
Blei, D.4
Elhadad, N.5
-
17
-
-
85055912186
-
MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery
-
Bihorac, A. et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann. Surg. 269, 652–662 (2019).
-
(2019)
Ann. Surg.
, vol.269
, pp. 652-662
-
-
Bihorac, A.1
-
18
-
-
84864808953
-
KDIGO clinical practice guidelines for acute kidney injury
-
Khwaja, A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin. Pract. 120, c179–c184 (2012).
-
(2012)
Nephron Clin. Pract.
, vol.120
, pp. c179-c184
-
-
Khwaja, A.1
-
19
-
-
0001372014
-
Prospective evaluation of a modified early warning score to aid earlier detection of patients developing critical illness on a general surgical ward
-
Stenhouse, C., Coates, S., Tivey, M., Allsop, P. & Parker, T. Prospective evaluation of a modified early warning score to aid earlier detection of patients developing critical illness on a general surgical ward. Br. J. Anaesth. 84, 663P (2000).
-
(2000)
Br. J. Anaesth.
, vol.84
, pp. 663P
-
-
Stenhouse, C.1
Coates, S.2
Tivey, M.3
Allsop, P.4
Parker, T.5
-
20
-
-
84923346326
-
Biomarkers of AKI: a review of mechanistic relevance and potential therapeutic implications
-
COI: 1:CAS:528:DC%2BC2MXitVChs78%3D
-
Alge, J. L. & Arthur, J. M. Biomarkers of AKI: a review of mechanistic relevance and potential therapeutic implications. Clin. J. Am. Soc. Nephrol. 10, 147–155 (2015).
-
(2015)
Clin. J. Am. Soc. Nephrol.
, vol.10
, pp. 147-155
-
-
Alge, J.L.1
Arthur, J.M.2
-
21
-
-
84859128929
-
Acute kidney injury and mortality in hospitalized patients
-
Wang, H. E., Muntner, P., Chertow, G. M. & Warnock, D. G. Acute kidney injury and mortality in hospitalized patients. Am. J. Nephrol. 35, 349–355 (2012).
-
(2012)
Am. J. Nephrol.
, vol.35
, pp. 349-355
-
-
Wang, H.E.1
Muntner, P.2
Chertow, G.M.3
Warnock, D.G.4
-
22
-
-
70350095380
-
NCEPOD report on acute kidney injury—must do better
-
MacLeod, A. NCEPOD report on acute kidney injury—must do better. Lancet 374, 1405–1406 (2009).
-
(2009)
Lancet
, vol.374
, pp. 1405-1406
-
-
MacLeod, A.1
-
23
-
-
85016034442
-
Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review
-
Lachance, P. et al. Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review. Nephrol. Dial. Transplant. 32, 265–272 (2017).
-
(2017)
Nephrol. Dial. Transplant.
, vol.32
, pp. 265-272
-
-
Lachance, P.1
-
24
-
-
84962092181
-
Machine learning and decision support in critical care
-
Johnson, A. E. W. et al. Machine learning and decision support in critical care. Proc. IEEE Inst. Electr. Electron Eng. 104, 444–466 (2016).
-
(2016)
Proc. IEEE Inst. Electr. Electron Eng.
, vol.104
, pp. 444-466
-
-
Johnson, A.E.W.1
-
25
-
-
85061418276
-
Prediction of acute kidney injury with a machine learning algorithm using electronic health record data
-
Mohamadlou, H. et al. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Can. J. Kidney Health Dis. 5, 1–9 (2018).
-
(2018)
Can. J. Kidney Health Dis.
, vol.5
, pp. 1-9
-
-
Mohamadlou, H.1
-
27
-
-
85025597307
-
Impact of electronic acute kidney injury (AKI) alerts with automated nephrologist consultation on detection and severity of AKI: a quality improvement study
-
Park, S. et al. Impact of electronic acute kidney injury (AKI) alerts with automated nephrologist consultation on detection and severity of AKI: a quality improvement study. Am. J. Kidney Dis. 71, 9–19 (2018).
-
(2018)
Am. J. Kidney Dis.
, vol.71
, pp. 9-19
-
-
Park, S.1
-
29
-
-
85046992528
-
Reliable decision support using counterfactual models
-
eds Guyon, I. et al
-
Schulam, P. & Saria, S. Reliable decision support using counterfactual models. In Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) 1697–1708 (2017).
-
(2017)
Advances in Neural Information Processing Systems
, vol.30
, pp. 1697-1708
-
-
Schulam, P.1
Saria, S.2
-
30
-
-
85043501933
-
Rethinking the medical record
-
Telenti, A., Steinhubl, S. R. & Topol, E. J. Rethinking the medical record. Lancet 391, 1013 (2018).
-
(2018)
Lancet
, vol.391
, pp. 1013
-
-
Telenti, A.1
Steinhubl, S.R.2
Topol, E.J.3
-
33
-
-
0242456763
-
Transforming classifier scores into accurate multiclass probability estimates
-
eds, Zaïane, O. R. et al, ACM
-
Zadrozny, B. & Elkan, C. Transforming classifier scores into accurate multiclass probability estimates. In Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds, Zaïane, O. R. et al.) 694–699 (ACM, 2002).
-
(2002)
Proc. 8Th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, pp. 694-699
-
-
Zadrozny, B.1
Elkan, C.2
-
34
-
-
85013996214
-
Recurrent highway networks
-
eds Precup, D. & Teh, Y. W
-
Zilly, J. G., Srivastava, R. K., Koutník, J. & Schmidhuber, J. Recurrent highway networks. In Proc. International Conference on Machine Learning (vol. 70) (eds Precup, D. & Teh, Y. W.) 4189–4198 (2017).
-
(2017)
Proc. International Conference on Machine Learning
, vol.70
, pp. 4189-4198
-
-
Zilly, J.G.1
Srivastava, R.K.2
Koutník, J.3
Schmidhuber, J.4
-
35
-
-
0031573117
-
Long short-term memory
-
COI: 1:STN:280:DyaK1c%2FhvVahsQ%3D%3D
-
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
-
(1997)
Neural Comput.
, vol.9
, pp. 1735-1780
-
-
Hochreiter, S.1
Schmidhuber, J.2
-
36
-
-
85088225685
-
Capacity and trainability in recurrent neural networks
-
eds Bengio, Y. & LeCun, Y
-
Collins, J., Sohl-Dickstein, J. & Sussillo, D. Capacity and trainability in recurrent neural networks. In International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) https://openreview.net/forum?id=BydARw9ex (2017).
-
(2017)
International Conference on Learning Representations
-
-
Collins, J.1
Sohl-Dickstein, J.2
Sussillo, D.3
-
37
-
-
85088231695
-
Quasi-recurrent neural networks
-
eds Bengio, Y. & LeCun, Y
-
Bradbury, J., Merity, S., Xiong, C. & Socher, R. Quasi-recurrent neural networks. In International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) https://openreview.net/forum?id=H1zJ-v5xl (2017).
-
(2017)
International Conference on Learning Representations
-
-
Bradbury, J.1
Merity, S.2
Xiong, C.3
Socher, R.4
-
39
-
-
84939821078
-
-
Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modelling. Preprint at https://arxiv.org/abs/1412.3555 (2014).
-
(2014)
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelling
-
-
Chung, J.1
Gulcehre, C.2
Cho, K.3
Bengio, Y.4
-
41
-
-
84998717754
-
Meta-learning with memory-augmented neural networks
-
Weinberger, K. Q
-
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D. & Lillicrap, T. Meta-learning with memory-augmented neural networks. In Proc. International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 1842–1850 (2016).
-
(2016)
Proc. International Conference on Machine Learning
, pp. 1842-1850
-
-
Santoro, A.1
Bartunov, S.2
Botvinick, M.3
Wierstra, D.4
Lillicrap, T.5
-
42
-
-
84993949467
-
Hybrid computing using a neural network with dynamic external memory
-
Graves, A. et al. Hybrid computing using a neural network with dynamic external memory. Nature 538, 471–476 (2016).
-
(2016)
Nature
, vol.538
, pp. 471-476
-
-
Graves, A.1
-
43
-
-
85059833004
-
Relational recurrent neural networks
-
Bengio, S. et al
-
Santoro, A. et al. Relational recurrent neural networks. In Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al.) 7310–7321 (2018).
-
(2018)
Advances in Neural Information Processing Systems
, vol.31
, pp. 7310-7321
-
-
Santoro, A.1
-
44
-
-
85156259646
-
-
eds Mozer, M. et al
-
Caruana, R., Baluja, S. & Mitchell, T. in Advances in Neural Information Processing Systems (eds Mozer, M. et al.) 959–965 (1996).
-
(1996)
Advances in Neural Information Processing Systems
, pp. 959-965
-
-
Caruana, R.1
Baluja, S.2
Mitchell, T.3
-
45
-
-
84979871458
-
Patient risk stratification with time-varying parameters: a multitask learning approach
-
Wiens, J., Guttag, J. & Horvitz, E. Patient risk stratification with time-varying parameters: a multitask learning approach. J. Mach. Learn. Res. 17, 1–23 (2016).
-
(2016)
J. Mach. Learn. Res.
, vol.17
, pp. 1-23
-
-
Wiens, J.1
Guttag, J.2
Horvitz, E.3
-
47
-
-
84862277874
-
Understanding the difficulty of training deep feedforward neural networks
-
Tehand, Y. W. & Titterington, M
-
Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In International Conference on Artificial Intelligence and Statistics (vol. 9) (eds Tehand, Y. W. & Titterington, M.) 249–256 (2010).
-
(2010)
International Conference on Artificial Intelligence and Statistics
, vol.9
, pp. 249-256
-
-
Glorot, X.1
Bengio, Y.2
-
48
-
-
85083951076
-
Adam: A method for stochastic optimization
-
(eds Bengio, Y. & LeCun, Y.)
-
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) https://dblp.org/rec/bib/journals/corr/KingmaB14 (2015).
-
(2015)
In International Conference on Learning Representations
-
-
Kingma, D.P.1
Ba, J.2
-
49
-
-
85047020009
-
On calibration of modern neural networks
-
Precup, D. & Teh, Y. W
-
Guo, C., Pleiss, G., Sun, Y. & Weinberger, K. Q. On calibration of modern neural networks. In Proc. International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 1321–1330 (2017).
-
(2017)
Proc. International Conference on Machine Learning
, pp. 1321-1330
-
-
Guo, C.1
Pleiss, G.2
Sun, Y.3
Weinberger, K.Q.4
-
51
-
-
0003010182
-
Verification of forecasts expressed in terms of probability
-
Brier, G. W. Verification of forecasts expressed in terms of probability. Mon. Weath. Rev. 78, 1–3 (1950).
-
(1950)
Mon. Weath. Rev.
, vol.78
, pp. 1-3
-
-
Brier, G.W.1
-
52
-
-
31844433358
-
Predicting good probabilities with supervised learning
-
eds Raedt, L. D. & Wrobel, S., ACM
-
Niculescu-Mizil, A. & Caruana, R. Predicting good probabilities with supervised learning. In Proc. International Conference on Machine Learning (eds Raedt, L. D. & Wrobel, S.) 625–632 (ACM, 2005).
-
(2005)
Proc. International Conference on Machine Learning
, pp. 625-632
-
-
Niculescu-Mizil, A.1
Caruana, R.2
-
53
-
-
84928911070
-
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
-
Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10, e0118432 (2015).
-
(2015)
PLoS ONE
, vol.10
-
-
Saito, T.1
Rehmsmeier, M.2
-
55
-
-
0002322469
-
On a test of whether one of two random variables is stochastically larger than the other
-
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).
-
(1947)
Ann. Math. Stat.
, vol.18
, pp. 50-60
-
-
Mann, H.B.1
Whitney, D.R.2
-
56
-
-
85046740096
-
Simple and scalable predictive uncertainty estimation using deep ensembles
-
eds Guyon, I. et al
-
Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in Neural Information Processing Systems (eds Guyon, I. et al.) 6402–6413 (2017).
-
(2017)
Advances in Neural Information Processing Systems
, pp. 6402-6413
-
-
Lakshminarayanan, B.1
Pritzel, A.2
Blundell, C.3
-
57
-
-
85052522615
-
Clinically applicable deep learning for diagnosis and referral in retinal disease
-
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
-
(2018)
Nat. Med.
, vol.24
, pp. 1342-1350
-
-
De Fauw, J.1
|