The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
How to develop machine learning models for healthcare
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“However, if these samples are not representative of the overall patient population, there is an increased risk of the reported model performance not being representative of the true model performance 37 .”
From Empirical data drift detection experiments on real-world medical imaging data (2024) · cites this paper as [37]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., Elbourne, D., Egger, M., & Altman, D. G. (2010) · BMJ · doi:10.1136/bmj.c869 · Cited by 8,883
(2012) · Cited by 8,009
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Self-citationThe class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
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
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
Research works printed but not linked: 7
- Xu, K. et al. Preprint at https://arxiv.org/abs/1502.03044 (2015). · arXiv
- Zoph, B., Vasudevan, V., Shlens, J. & Le, Q. V. Preprint at https://arxiv.org/abs/1707.07012 (2017). · arXiv
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. in IEEE Conference on Computer Vision and Pattern Recognition 4510–4520 (IEEE, 2018).
- Bishop, C. Pattern Recognition and Machine Learning (Springer, 2006).
- Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Preprint at https://arxiv.org/abs/1611.03530 (2016). · arXiv
- Bergstra, J. & Bengio, Y. J. Mach. Learn. Res. 13, 281–305 (2012).
- Niculescu-Mizil, A. & Caruana, R. in Proc. 22nd International Conference on Machine Learning 625–632 (ACM, 2005).
Cited by
Sorted by citations
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Significance of machine learning in healthcare: Features, pillars and applications
Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2022.05.002 · Cited by 760
How Machine Learning Will Transform Biomedicine
Goecks, J., Jalili, V., Heiser, L. M., & Gray, J. W. (2020) · Cell · doi:10.1016/j.cell.2020.03.022 · Cited by 604
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
“As it is pretty obvious, ML applications for healthcare wearables are a multidisciplinary field that requires standards about naming conventions, evaluation metrics, ethical reporting of research results, and clinical impact as suggested in [101] as these devices may directly affect human life.”
BackgroundAI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
Wasilewski, T., Kamysz, W., & Gębicki, J. (2024) · Biosensors · doi:10.3390/bios14070356 · Cited by 328
“The ML model must be deployed and utilized in both retrospective and prospective studies, with the clinical impact assessed [146].”
Method
Metadata
- Authors
- Chen, P.-H. C., Liu, Y., & Peng, L.
- Affiliations
- Google (United States) (US)
- Corresponding author
- Chen, Po-Hsuan Cameron
- Venue
- Nature Materials
- Published
- 2019-04-18
- Volume
- 18
- Issue
- 5
- Pages
- 410-414
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W2936086693
- Semantic Scholar
- e13c152b9f9945578e55885bb4ea6a079769cec3
- Cited by
- 274
- References
- 25
- Citations by year
- 2026: 16 · 2025: 32 · 2024: 41 · 2023: 47 · 2022: 56 · 2021: 43 · 2020: 35 · 2019: 4
- FWCI
- 15.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning models, health care, clinical decision support, model validation, model implementation, patient care
- MeSH terms
- Machine Learning, Algorithms, Cardiovascular Diseases, Diabetic Retinopathy, Diagnosis, Computer-Assisted, Diagnostic Errors, Echocardiography, Humans, Image Processing, Computer-Assisted, Medical Informatics, Models, Theoretical, Predictive Value of Tests, Prognosis, Radiotherapy Planning, Computer-Assisted, Reproducibility of Results, Drug Discovery
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.
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