A clinically applicable approach to continuous prediction of future acute kidney injury
Open accessAbstract
The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients 1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records 2-17 and using acute kidney injury-a common and potentially life-threatening condition 18-as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests 9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.
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.
Integrity signals
Registry
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.
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?
Many research groups have tackled early prediction of AKI using electronic health records data, but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions.
“Many research groups have tackled early prediction of AKI using electronic health records (EHR) data [8–11], but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions [6, 12].”
From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [9]
The paper supports the need for actionable predictions with clinical context, but contradicts the claim that no model explains individual predictions. In its main text (page unavailable), it says its model identifies the factors most salient to each risk prediction; it also describes providing a list of salient clinical features for each prediction. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe AKI dataset was predominantly made up of data from older non-Black men; average age was 62 years old and over 90% were males.
“it was predominantly made up of data from older non-Black men; the average age was 62 years old and over 90% were males.”
From Bias in artificial intelligence algorithms and recommendations for mitigation (2023) · cites this paper as [27]
The main text reports that 6.38% of patients were female, supporting the claim that over 90% were male. Extended Data Table 5 gives average ages of 61.3 and 64.2 years for cohorts before and after a temporal split, but those figures do not establish an overall average of 62. The retrieved passages do not establish that patients were predominantly non-Black. (LLM, conf=0.80)
Experimental — accuracy not yet measuredAn AKI prediction algorithm was developed using clinical data from the US Department of Veteran Affairs.
“An example of this includes an algorithm developed to predict acute kidney injury (AKI) using clinical data from the US Department of Veteran Affairs [27].”
From Bias in artificial intelligence algorithms and recommendations for mitigation (2023) · cites this paper as [27]
The AKI dataset contained data from over 700,000 patients from multiple centers.
“Though the dataset used to develop the algorithm was large and diverse, containing data from over 700,000 patients from multiple centers”
From Bias in artificial intelligence algorithms and recommendations for mitigation (2023) · cites this paper as [27]
AI models that use EHR, laboratory, and physiological data predict outcomes such as acute kidney injury and sepsis or critical-care deterioration.
“Models that use EHR, laboratory, and physiological data predict outcomes such as acute kidney injury [14] and sepsis or critical-care deterioration [15,16].”
The continuous risk prediction model for acute kidney injury achieves a higher ROC AUC in shorter time windows.
“Model achieves a higher ROC AUC in shorter time windows.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [80]
The continuous risk prediction model for acute kidney injury achieves a lower PR AUC in shorter time windows.
“The model achieves a lower PR AUC in shorter time windows.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [80]
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320 · see citing works
(2010) · Cited by 14,134 · see citing works
Self-citationEmpirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
KDIGO Clinical Practice Guidelines for Acute Kidney Injury
Khwaja, A. (2012) · Nephron Clinical Practice · doi:10.1159/000339789 · Cited by 8,039 · see citing works
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2016) · arXiv · doi:10.48550/arxiv.1612.01474 · Cited by 3,666 · see citing works
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works
Self-citationScalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242 · see citing works
Predicting good probabilities with supervised learning
Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · International Conference on Machine Learning · Cited by 1,504 · see citing works
Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature · doi:10.1038/nature20101 · Cited by 1,412 · see citing works
Self-citationKomorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0213-5 · Cited by 1,290 · see citing works
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280 · see citing works
Platt, J. C. (2000) · Advances in Large-Margin Classifiers · doi:10.7551/mitpress/1113.003.0008 · Cited by 1,156 · see citing works
Graves, A., Wayne, G., & Danihelka, I. (2014) · arXiv · doi:10.48550/arxiv.1410.5401 · Cited by 1,058 · see citing works
Learning to Diagnose with LSTM Recurrent Neural Networks
Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015) · arXiv · doi:10.48550/arxiv.1511.03677 · Cited by 879 · 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
Acute Kidney Injury and Mortality in Hospitalized Patients
Wang, H. E., Muntner, P., Chertow, G. M., & Warnock, D. G. (2012) · American Journal of Nephrology · doi:10.1159/000337487 · Cited by 539 · see citing works
Quasi-Recurrent Neural NetworksPreprint
Bradbury, J., Merity, S., Xiong, C., & Socher, R. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1611.01576 · Cited by 481 · see citing works
Improving palliative care with deep learning
Avati, A., Jung, K., Harman, S. M., Downing, N. L., Ng, A. Y., & Shah, N. H. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217669 · Cited by 445 · see citing works
Machine Learning and Decision Support in Critical Care
Johnson, A. E. W., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D. A., & Clifford, G. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2501978 · Cited by 438 · see citing works
Risk Prediction with Electronic Health Records: A Deep Learning Approach
Cheng, Y., Wang, F., Zhang, P., & Hu, J. (2016) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974348.49 · Cited by 404 · see citing works
Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P. M., Lipori, G., Hogan, W. R., Efron, P. A., Moore, F., et al. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000002706 · Cited by 377 · see citing works
The development of a machine learning inpatient acute kidney injury prediction model.
Koyner, J. L., Carey, K. A., Edelson, D. P., & Churpek, M. M. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003123 · Cited by 323 · see citing works
Alge, J. L., & Arthur, J. M. (2014) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.12191213 · Cited by 294 · see citing works
Recurrent Highway NetworksPreprint
Zilly, J., Srivastava, R., Koutník, J., & Schmidhuber, J. (2016) · arXiv (Cornell University) · doi:10.3929/ethz-b-000233981 · Cited by 243 · see citing works
Stenhouse, C. W., Coates, S., Tivey, M., Allsop, P., & Parker, T. (2000) · British Journal of Anaesthesia · doi:10.1093/bja/84.5.663 · Cited by 206 · 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 1× · see citing works
“Future work will need to address the under-representation of sub-populations in the training data 28 and overcome the impact of potential confounding factors related to hospital processes 29 .”
Mohamadlou, H., Lynn-Palevsky, A., Barton, C., Chettipally, U. K., Shieh, L. Y., Calvert, J. S., Saber, N., & Das, R. (2018) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358118776326 · Cited by 161 · see citing works
Relational recurrent neural networksPreprint
Santoro, A., Faulkner, R., Raposo, D. C., Rae, J. W., Chrzanowski, M., Weber, T., Wierstra, D., Vinyals, O., Pascanu, R., & Lillicrap, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01822 · Cited by 153 · see citing works
Self-citationCapacity and Trainability in Recurrent Neural NetworksPreprint
Collins, J., Sohl‐Dickstein, J., & Sussillo, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.09913 · Cited by 149 · see citing works
Park, S., Baek, S. H., Ahn, S., Lee, K.-H., Hwang, H. Y., Ryu, J., Ahn, S. Y., Chin, H. J., Na, K. Y., Chae, D., et al. (2017) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2017.06.008 · Cited by 134 · see citing works
Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv024 · Cited by 128 · see citing works
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierPreprint
Futoma, J., Hariharan, S., & Heller, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04152 · Cited by 122 · see citing works
Lachance, P., Villeneuve, P.-M., Rewa, O. G., Wilson, F. P., Selby, N. M., Featherstone, R. M., & Bagshaw, S. M. (2016) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfw424 · Cited by 115 · see citing works
Development of a Multicenter Ward–Based AKI Prediction Model
Koyner, J. L., Adhikari, R., Edelson, D. P., & Churpek, M. M. (2016) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.00280116 · Cited by 113 · see citing works
Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes
Alaa, A. M., Yoon, J., Hu, S. B., & van der Schaar, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2698602 · Cited by 70 · see citing works
NCEPOD report on acute kidney injury—must do better
MacLeod, A. M. (2009) · The Lancet · doi:10.1016/s0140-6736(09)61843-2 · Cited by 68 · see citing works
Dovgan, E., Gradišek, A., Luštrek, M., Uddin, M., Nursetyo, A. A., Annavarajula, S. K., Li, Y., & Syed-Abdul, S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233976 · Cited by 55 · see citing works
Patient risk stratification with time-varying parameters: a multitask learning approach
Wiens, J., Guttag, J. V., & Horvitz, E. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 54 · see citing works
Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?
Cheng, P., Waitman, L. R., Hu, Y. Q., & Liu, M. (2017) · PubMed Central · Cited by 51 · see citing works
Temporal Convolutional Neural Networks for Diagnosis from Lab TestsPreprint
Razavian, N., & Sontag, D. A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07938 · Cited by 49 · see citing works
Will AI Replace Ophthalmologists?
Korot, E., Wagner, S. K., Faes, L., Liu, X., Huemer, J. C., Ferraz, D., Keane, P. A., & Balaskas, K. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.2 · Cited by 39 · see citing works
Deng, F., Peng, M., Li, J., Chen, Y., Zhang, B., & Zhao, S. (2020) · Renal Failure · doi:10.1080/0886022x.2020.1761832 · Cited by 39 · see citing works
Artificial intelligence and computer simulation models in critical illness
Lal, A., Pinevich, Y., Gajic, O., Herasevich, V., & Pickering, B. W. (2020) · World Journal of Critical Care Medicine · doi:10.5492/wjccm.v9.i2.13 · Cited by 35 · see citing works
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
Ding, D. Y., Simpson, C., Pfohl, S., Kale, D., Jung, K., & Shah, N. H. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0003 · Cited by 27 · see citing works
Soleimani, H., Subbaswamy, A., & Saria, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02038 · Cited by 23 · see citing works
Disease-Atlas: Navigating Disease Trajectories using Deep Learning
Lim, B. Y., & van der Schaar, M. (2018) · Machine Learning for Healthcare Conference · Cited by 19 · see citing works
Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics.
Corbin, C. K., Medford, R. J., Osei, K., & Chen, J. H. (2020) · PubMed Central · Cited by 19 · see citing works
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process ExpertsPreprint
Alaa, A. M., Yoon, J., Hu, S. B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.00959 · Cited by 12 · see citing works
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical CarePreprint
Pan, Z., Du, H. Y., Ngiam, K. Y., Wang, F., Shum, P., & Feng, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.04364 · Cited by 10 · see citing works
Matsuno, K., Tanaka, S., Hashimoto, T., Nakamichi, H., & Komura, E. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfx149.sp428 · Cited by 9 · see citing works
Telenti, A., Steinhubl, S. R., & Topol, E. J. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30538-5 · Cited by 7 · see citing works
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078
Flagged first, then by citations
Research works printed but not linked: 5
- [29]Schulam P and Saria S, “ Reliable decision support using counterfactual models,” in Advances in Neural Information Processing Systems, (Guyon I, Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, and Garnett R, eds.), vol. 30, pp. 1697– 1708, 2762017.
- [34]Zilly JG, Srivastava RK, Koutník J, and Schmidhuber J, “ Recurrent highway net-works,” in Proceedings of the International Conference on Machine Learning (Precupand D Teh YW, eds.), vol. 70, pp. 4189– 4198, 2017.
- [38]Lei T and Zhang Y, “ Training RNNs as fast as CNNs,” arXiv Preprint, arXiv:1709.02755, 2017. · arXiv
- [44]Caruana R, Baluja S, and Mitchell T, “ Using the future to “sort out” the present: Rankprop and multitask learning for medical risk evaluation,” in Advances in Neural Infor-mation Processing Systems (Mozer M, Jordan M, and Petsche T, eds.), vol. 9, pp. 959– 965, 1996.
- [50]Platt JC, “ Probabilistic outputs for support vector machines and comparisons to regular-ized likelihood methods,” in Advances in Large-Margin Classifiers, pp. 61– 74, MIT Press, 1999.
Cited by
Sorted by citations
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
Self-citationAcosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662 · see citing works
Improving the accuracy of medical diagnosis with causal machine learning
Richens, J. G., Lee, C. M., & Johri, S. (2020) · Nature Communications · doi:10.1038/s41467-020-17419-7 · Cited by 598 · see citing works
Bias in artificial intelligence algorithms and recommendations for mitigation
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., Hicklen, R. S., Moukheiber, L., Moukheiber, D., Ma, H., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000278 · Cited by 621 · see citing works
Ostermann, M., Bellomo, R., Burdmann, E. A., Doi, K., Endre, Z. H., Goldstein, S. L., Kane‐Gill, S. L., Liu, K. D., Prowle, J. R., Shaw, A., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.04.020 · Cited by 581 · see citing works
Ethical Machine Learning in Healthcare
Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645 · see citing works
Artificial intelligence and algorithmic bias: implications for health systems
Panch, T., Mattie, H., & Atun, R. (2019) · Journal of Global Health · doi:10.7189/jogh.09.020318 · Cited by 533 · see citing works
Health system-scale language models are all-purpose prediction engines
Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06160-y · Cited by 460 · see citing works
Early prediction of circulatory failure in the intensive care unit using machine learning
Hyland, S. L., Faltys, M., Hüser, M., Lyu, X., Gumbsch, T., Esteban, C., Bock, C., Horn, M., Moor, M., Rieck, B., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0789-4 · Cited by 477 · see citing works
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI · doi:10.1056/aioa2300138 · Cited by 483 · see citing works
Self-citationIntegrated 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
Axes of a revolution: challenges and promises of big data in healthcare
Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370 · see citing works
A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories
Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02332-5 · Cited by 349 · see citing works
Health data poverty: an assailable barrier to equitable digital health care
Ibrahim, H., Liu, X., Zariffa, N., Morris, A. D., & Denniston, A. K. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30317-4 · Cited by 326 · see citing works
Predicting conversion to wet age-related macular degeneration using deep learning
Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0867-7 · Cited by 297 · see citing works
Self-citationSong, X., Liu, X., Liu, F., & Wang, C. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104484 · Cited by 301 · see citing works
Haimovich, A. D., Ravindra, N. G., Stoytchev, S., Young, H. P., Wilson, F. P., van Dijk, D., Schulz, W. L., & Taylor, R. A. (2020) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2020.07.022 · Cited by 289 · see citing works
Machine learning in clinical decision making
Adlung, L., Cohen, Y., Mor, U., & Elinav, E. (2021) · Med · doi:10.1016/j.medj.2021.04.006 · Cited by 304 · see citing works
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y., & Ghassemi, M. (2020) · Biocomputing 2021 · doi:10.1142/9789811232701_0022 · Cited by 275 · see citing works
“Identifying and eliminating disparities is particularly important as large datasets begin to be used by high-capacity neural models, but are based on highly skewed population, e.g., kidney injury prediction in a population that is 93.6% male [43].”
BackgroundInfluentialIntegrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 272 · see citing works
Digital Twins for Multiple Sclerosis
Voigt, I., Inojosa, H., Dillenseger, A., Haase, R., Akgün, K., & Ziemssen, T. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.669811 · Cited by 279 · see citing works
Scalia, G., Grambow, C. A., Pernici, B., Li, Y., & Green, W. H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00975 · Cited by 283 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T., Williamson, D. F. K., Zhao, M. M., Shady, M., Lipková, J., & Mahmood, F. (2021) · RePEc: Research Papers in Economics · Cited by 263 · see citing works
Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care
Dong, J., Feng, T., Thapa-Chhetry, B., Cho, B. G., Shum, T., Inwald, D. P., Newth, C. J. L., & Vaidya, V. U. (2021) · Critical Care · doi:10.1186/s13054-021-03724-0 · Cited by 233 · see citing works
Machine Learning for Clinical Outcome Prediction
Shamout, F. E., Zhu, T.-T., & Clifton, D. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3007816 · Cited by 210 · see citing works
Time Series Prediction Using Deep Learning Methods in Healthcare
Morid, M. A., Sheng, O. R. L., & Dunbar, J. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3531326 · Cited by 256 · see citing works
Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., & Kather, J. N. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100443 · Cited by 232 · see citing works
Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020) · Journal of Medical Internet Research · doi:10.2196/16649 · Cited by 221 · see citing works
Ni, Q., Sun, Z. Y., Li, Q., Chen, W., Yang, Y., Wang, L., Zhang, X., Liu, Y., Fang, Y., Xing, Z., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07044-9 · Cited by 221 · see citing works
Causal machine learning for healthcare and precision medicine
Sanchez, P., Voisey, J. P., Xia, T., Watson, H. I., O’Neil, A. Q., & Tsaftaris, S. A. (2022) · Royal Society Open Science · doi:10.1098/rsos.220638 · Cited by 224 · see citing works
Bica, I., Alaa, A. M., Lambert, C., & van der Schaar, M. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1907 · Cited by 202 · see citing works
Developing a delivery science for artificial intelligence in healthcare
Li, R. C., Asch, S., & Shah, N. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00318-y · Cited by 159 · see citing works
Banerjee, M., Chiew, D., Patel, K. T., Johns, I., Chappell, D., Linton, N., Cole, G. D., Francis, D. P., Szram, J., Ross, J., et al. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02870-x · Cited by 168 · see citing works
Preventing CKD in Developed Countries
Luyckx, V. A., Cherney, D. Z. I., & Bello, A. K. (2019) · Kidney International Reports · doi:10.1016/j.ekir.2019.12.003 · Cited by 183 · see citing works
An introduction to digital determinants of health
Chidambaram, S., Jain, B., Jain, U., Mwavu, R., Baru, R., Thomas, B., Greaves, F., Jayakumar, S., Jain, P., Rojo, M., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000346 · Cited by 175 · see citing works
Clinical Applications of Artificial Intelligence—An Updated Overview
Busnatu, Ș., Niculescu, A.-G., Bolocan, A., Petrescu, G. E. D., Păduraru, D. N., Năstasă, I., Lupușoru, M. O. D., Geantă, M., Andronic, O., Grumezescu, A. M., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082265 · Cited by 181 · see citing works
A Path for Translation of Machine Learning Products into Healthcare Delivery
(2020) · EMJ Innovations · doi:10.33590/emjinnov/19-00172 · Cited by 172 · see citing works
Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
Subudhi, S., Verma, A., Patel, A. B., Hardin, C., Khandekar, M. J., Lee, H., McEvoy, D. S., Stylianopoulos, T., Munn, L. L., Dutta, S., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00456-x · Cited by 168 · see citing works
Shaping the future of AI in healthcare through ethics and governance
Bouderhem, R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02894-w · Cited by 195 · see citing works
Let’s talk about sex: Differences in drug therapy in males and females
Madla, C. M., Gavins, F. K. H., Merchant, H. A., Orlu, M., Murdan, S., & Basit, A. W. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.05.014 · Cited by 167 · see citing works
Nakamori, Y., Park, E. J., & Shimaoka, M. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.624279 · Cited by 168 · see citing works
Ioannou, G. N., Tang, W., Beste, L. A., Tincopa, M. A., Su, G. L., Van, T., Tapper, E. B., Singal, A. G., Zhu, J., & Waljee, A. K. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.15626 · Cited by 165 · see citing works
Zhang, Y., Wang, S., Hermann, A. D., Joly, R., & Pathak, J. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.09.113 · Cited by 164 · see citing works
Algorithmic fairness in computational medicine
Xu, J., Xiao, Y., Wang, W. H., Ning, Y., Shenkman, E. A., Bian, J., & Wang, F. (2022) · eBioMedicine · doi:10.1016/j.ebiom.2022.104250 · Cited by 170 · see citing works
Personality Research and Assessment in the Era of Machine Learning
Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S. S., Gosling, S. D., & Bühner, M. (2020) · European Journal of Personality · doi:10.1002/per.2257 · Cited by 158 · see citing works
Rank, N., Pfahringer, B., Kempfert, J., Stamm, C., Kühne, T., Schoenrath, F., Falk, V., Eickhoff, C., & Meyer, A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00346-8 · Cited by 155 · see citing works
Bioactive NIR-II gold clusters for three-dimensional imaging and acute inflammation inhibition
Ma, H., Zhang, X., Liu, L., Huang, Y., Sun, S., Chen, K., Qi, X., Liu, P., Yan, Y., Wang, Y., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh7828 · Cited by 151 · see citing works
AKI!Now Initiative: Recommendations for Awareness, Recognition, and Management of AKI
Liu, K. D., Goldstein, S. L., Vijayan, A., Parikh, C. R., KASHANI, K. B., Okusa, M. D., Agarwal, A., Cerdá, J., & on behalf of the AKI!Now Initiative of the American Society of Nephrology (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.15611219 · Cited by 136 · see citing works
Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence
Tong, L., Shi, W., Isgut, M., Zhong, Y., Lais, P., Gloster, L., Sun, J., Swain, A., Giuste, F., & Wang, M. D. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3324264 · Cited by 130 · see citing works
Sheu, R.-K., & Pardeshi, M. S. (2022) · Sensors · doi:10.3390/s22208068 · Cited by 129 · see citing works
Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01895-z · Cited by 127 · see citing works
Machine and deep learning for longitudinal biomedical data: a review of methods and applications
Cascarano, A., Mur-Petit, J., Hernández-González, J., Camacho, M., Eadie, N. D. T., Gkontra, P., Chadeau‐Hyam, M., Vitria, J. M., & Lekadir, K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10561-w · Cited by 133 · see citing works
Machine learning in haematological malignancies
Radakovich, N., Nagy, M. R., & Nazha, A. (2020) · The Lancet Haematology · doi:10.1016/s2352-3026(20)30121-6 · Cited by 123 · see citing works
Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning
Yang, H. S., Hou, Y., Vasović, L. V., Steel, P. A. D., Chadburn, A., Racine‐Brzostek, S., Velu, P., Cushing, M. M., Loda, M. F., Kaushal, R., et al. (2020) · Clinical Chemistry · doi:10.1093/clinchem/hvaa200 · Cited by 125 · see citing works
Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury
Churpek, M. M., Carey, K. A., Edelson, D. P., Singh, T., Astor, B. C., Gilbert, E. R., Winslow, C. J., Shah, N. S., Afshar, M., & Koyner, J. L. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.12892 · Cited by 122 · see citing works
How Do Machines Learn? Artificial Intelligence as a New Era in Medicine
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., & Mackiewicz, A. A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11010032 · Cited by 124 · see citing works
Ravaut, M., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Harish, V., Watson, T., Lewis, G. F., Weisman, A., Poutanen, T., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00394-8 · Cited by 121 · see citing works
Song, X., Yu, A. S. L., Kellum, J. A., Waitman, L. R., Matheny, M. E., Simpson, S. Q., Hu, Y., & Liu, M. (2020) · Nature Communications · doi:10.1038/s41467-020-19551-w · Cited by 120 · see citing works
Moor, M., Bennett, N., Plečko, D., Horn, M., Rieck, B., Meinshausen, N., Bühlmann, P., & Borgwardt, K. (2023) · eClinicalMedicine · doi:10.1016/j.eclinm.2023.102124 · Cited by 118 · see citing works
Applications of machine learning in routine laboratory medicine: Current state and future directions
Rabbani, N., Kim, G. Y. E., Suarez, C. J., & Chen, J. H. (2022) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2022.02.011 · Cited by 118 · see citing works
Adamichou, C., Genitsaridi, I., Nikolopoulos, D., Nikoloudaki, M., Repa, A., Bortoluzzi, A., Fanouriakis, A., Sidiropoulos, P. I., Boumpas, D. T., & Bertsias, G. K. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-219069 · Cited by 120 · see citing works
Characterizing Uncertainty in Machine Learning for Chemistry
Heid, E., McGill, C. J., Vermeire, F. H., & Green, W. H. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00373 · Cited by 113 · see citing works
Deep imputation of missing values in time series health data: A review with benchmarking
Kazijevs, M., & Samad, M. D. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104440 · Cited by 113 · see citing works
Tomašev, N., Harris, N., Baur, S., Mottram, A., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Magliulo, V., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00513-5 · Cited by 104 · see citing works
Self-citationMorin, O., Vallières, M., Braunstein, S. E., Ginart, J. B., Upadhaya, T., Woodruff, H. C., Zwanenburg, A., Chatterjee, A., Villanueva-Meyer, J. E., Valdés, G., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00236-2 · Cited by 118 · see citing works
Machine learning for early detection of sepsis: an internal and temporal validation study
Bedoya, A. D., Futoma, J., Clement, M. E., Corey, K., Brajer, N., Lin, A., Simons, M. G., Gao, M., Nichols, M., Balu, S., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa006 · Cited by 109 · see citing works
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
Emerging early diagnostic methods for acute kidney injury
Xiao, Z., Huang, Q., Yang, Y., Liu, M., Chen, Q., Huang, J., Xiang, Y., Long, X., Zhao, T., Wang, X., et al. (2022) · Theranostics · doi:10.7150/thno.71064 · Cited by 107 · see citing works
Beyond generalization: a theory of robustness in machine learning
Freiesleben, T., & Grote, T. (2023) · Synthese · doi:10.1007/s11229-023-04334-9 · Cited by 106 · see citing works
Machine learning to assist clinical decision-making during the COVID-19 pandemic
Debnath, S., Barnaby, D. P., Coppa, K., Makhnevich, A., Kim, E. J., Chatterjee, S., Tóth, V., Levy, T. J., Paradis, M. D., Cohen, S. L., et al. (2020) · Bioelectronic Medicine · doi:10.1186/s42234-020-00050-8 · Cited by 104 · see citing works
Artificial intelligence-enabled decision support in nephrology
Loftus, T. J., Shickel, B., Ozrazgat‐Baslanti, T., Ren, Y., Glicksberg, B. S., Cao, J., Singh, K., Chan, L., Nadkarni, G. N., & Bihorac, A. (2022) · Nature Reviews Nephrology · doi:10.1038/s41581-022-00562-3 · Cited by 109 · see citing works
Applications of artificial intelligence and machine learning in respiratory medicine
Gonem, S., Janssens, W., Das, N., & Topalovic, M. (2020) · Thorax · doi:10.1136/thoraxjnl-2020-214556 · Cited by 102 · see citing works
Artificial intelligence in medicine and the disclosure of risks
Kiener, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01085-w · Cited by 97 · see citing works
Biomarkers and predicting acute kidney injury
Luft, F. C. (2020) · Acta Physiologica · doi:10.1111/apha.13479 · Cited by 101 · see citing works
Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension
Lee, H., Yun, D., Yoo, J., Yoo, K., Kim, Y. C., Kim, D. K., Oh, K., Joo, K. W., Kim, Y. S., Kwak, N., et al. (2021) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.09280620 · Cited by 97 · see citing works
Hong, J. C., Eclov, N. C. W., Dalal, N., Thomas, S. M., Stephens, S. J., Malicki, M., Shields, S., Cobb, A., Mowery, Y. M., Niedzwiecki, D., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.20.01688 · Cited by 93 · see citing works
Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research
Sheu, Y.-H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.551299 · Cited by 94 · see citing works
Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark
Roy, S., Asif, U., Tang, J., & Harrer, S. (2020) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb50085.2020.9353642 · Cited by 96 · see citing works
Sheu, Y.-H., Magdamo, C., Miller, M. R., Das, S., Blacker, D., & Smoller, J. W. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00817-8 · Cited by 90 · see citing works
Tang, S., Davarmanesh, P., Song, Y., Koutra, D., Sjoding, M. W., & Wiens, J. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa139 · Cited by 88 · see citing works
An overview and a roadmap for artificial intelligence in hematology and oncology
Rösler, W., Altenbuchinger, M., Baeßler, B., Beißbarth, T., Beutel, G., Bock, R., von Bubnoff, N. C. C., Eckardt, J., Foersch, S., Loeffler, C. M. L., et al. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04667-5 · Cited by 91 · see citing works
Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools
Adedinsewo, D. A., Pollak, A. W., Phillips, S. D., Smith, T. L., Svatikova, A., Hayes, S. N., Mulvagh, S. L., Norris, C., Roger, V. L., Noseworthy, P. A., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.319876 · Cited by 92 · see citing works
Demystifying artificial intelligence in pharmacy
Nelson, S. D., Walsh, C. G., Olsen, C. A., McLaughlin, A. J., LeGrand, J., Schutz, N., & Lasko, T. A. (2020) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaa218 · Cited by 87 · see citing works
Abdulaal, A., Patel, A. M., Charani, E., Denny, S., Mughal, N., & Moore, L. S. P. (2020) · Journal of Medical Internet Research · doi:10.2196/20259 · Cited by 85 · see citing works
Human-centred mechanism design with Democratic AI
Koster, R., Balaguer, J., Tacchetti, A., Weinstein, A., Zhu, T., Hauser, O., Williams, D., Campbell-Gillingham, L., Thacker, P., Botvinick, M., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01383-x · Cited by 84 · see citing works
Artificial Intelligence-Assisted Surgery: Potential and Challenges
Bodenstedt, S., Wagner, M., Müller-Stich, B. P., Weitz, J., & Speidel, S. (2020) · Visceral Medicine · doi:10.1159/000511351 · Cited by 84 · see citing works
Neyra, J. A., Ortiz-Soriano, V., Liu, L. J., Smith, T. D., Li, X., Xie, D., ADAMS-HUET, B. A., Moe, O. W., Toto, R. D., & Chen, J. (2022) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2022.06.004 · Cited by 82 · see citing works
Knight, D. R. T., Aakre, C. A., Anstine, C. V., Munipalli, B., Biazar, P., Mitri, G., Valery, J. R., Brigham, T., Niazi, S. K., Perlman, A. I., et al. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2023.100824 · Cited by 94 · see citing works
Li, F., Wang, S., Gao, Z., Qing, M., Pan, S. L., Liu, Y., & Hu, C. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2024.1510792 · Cited by 87 · see citing works
Artificial Intelligence in Hypertension
Padmanabhan, S., Tran, T., & Dominiczak, A. F. (2021) · Circulation Research · doi:10.1161/circresaha.121.318106 · Cited by 81 · see citing works
Lal, A., Li, G., Cubro, E., Chalmers, S. J., Li, H., Herasevich, V., Dong, Y., Pickering, B. W., Kılıçkaya, O., & Gajic, O. (2020) · Critical Care Explorations · doi:10.1097/cce.0000000000000249 · Cited by 80 · see citing works
Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01940-7 · Cited by 78 · see citing works
Healthsheet: Development of a Transparency Artifact for Health Datasets
Rostamzadeh, N., Mincu, D., Roy, S., Smart, A., Wilcox, L. G., Pushkarna, M., Schrouff, J., Amironesei, R., Moorosi, N., & Heller, K. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533239 · Cited by 79 · see citing works
Deep learning prediction models based on EHR trajectories: A systematic review
Amirahmadi, A., Ohlsson, M., & Etminani, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104430 · Cited by 87 · see citing works
Analyzing Patient Trajectories With Artificial Intelligence
Allam, A., Feuerriegel, S., Rebhan, M., & Krauthammer, M. (2021) · Journal of Medical Internet Research · doi:10.2196/29812 · Cited by 77 · see citing works
Pimentel, M. A. F., Redfern, O. C., Malycha, J., Meredith, P., Prytherch, D., Briggs, J., Young, J. D., Clifton, D. A., Tarassenko, L., & Watkinson, P. J. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202007-2700oc · Cited by 84 · see citing works
Role of Artificial Intelligence in Kidney Disease
Yuan, Q., Zhang, H., Deng, T., Tang, S., Yuan, X., Tang, W., Xie, Y., Ge, H., Wang, X., Zhou, Q., et al. (2020) · International Journal of Medical Sciences · doi:10.7150/ijms.42078 · Cited by 81 · see citing works
- Authors
- Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Ledsam, J. R., & Mohamed, S.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- University College London (GB)
- University College Hospital (GB)
- United States Department of Veterans Affairs (US)
- University of Utah (US)
- University of Nevada, Reno (US)
- Corresponding author
- Tomašev, Nenad
- Ledsam, Joseph R.
- ORCID
- Venue
- Nature
- Published
- 2019-07-31
- Volume
- 572
- Issue
- 7767
- Pages
- 116-119
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6722431
- OpenAlex
- W2964696298
- Cited by
- 1,086
- References
- 60
- Citations by year
- 2026: 123 · 2025: 149 · 2024: 122 · 2023: 150 · 2022: 154 · 2021: 169 · 2020: 180 · 2019: 39
- FWCI
- 61.95
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institutes of Health (VA999999)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- acute kidney injury, deep learning, electronic health records, clinical deterioration, dialysis requirement, confidence estimation, false alarms, clinical decision support
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Computer Simulation, False Positive Reactions, Female, Humans, Male, Middle Aged, ROC Curve, Risk Assessment, Clinical Laboratory Techniques, Pulmonary Disease, Chronic Obstructive, Uncertainty, Young Adult, Acute Kidney Injury, Datasets as Topic
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
