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For: Schaefer J, Lehne M, Schepers J, Prasser F, Thun S. The use of machine learning in rare diseases: a scoping review. Orphanet J Rare Dis. 2020;15:145. [PMID: 32517778 DOI: 10.1186/s13023-020-01424-6] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Solomon BD. Can artificial intelligence save medical genetics? Am J Med Genet A 2021. [PMID: 34633139 DOI: 10.1002/ajmg.a.62538] [Reference Citation Analysis]
2 Tabaie A, Orenstein EW, Nemati S, Basu RK, Kandaswamy S, Clifford GD, Kamaleswaran R. Predicting presumed serious infection among hospitalized children on central venous lines with machine learning. Comput Biol Med 2021;132:104289. [PMID: 33667812 DOI: 10.1016/j.compbiomed.2021.104289] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Si Y, Bernstam EV, Roberts K. Generalized and transferable patient language representation for phenotyping with limited data. J Biomed Inform 2021;116:103726. [PMID: 33711541 DOI: 10.1016/j.jbi.2021.103726] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Tanabe S. How can artificial intelligence and humans work together to fight against cancer? Artif Intell Cancer 2020; 1(3): 45-50 [DOI: 10.35713/aic.v1.i3.45] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
5 Hurvitz N, Azmanov H, Kesler A, Ilan Y. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet 2021. [PMID: 34276056 DOI: 10.1038/s41431-021-00928-4] [Reference Citation Analysis]
6 Pantelis AG, Stravodimos GK, Lapatsanis DP. A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives. Obes Surg 2021. [PMID: 34264433 DOI: 10.1007/s11695-021-05548-x] [Reference Citation Analysis]
7 Rao RS, Shivanna DB, Mahadevpur KS, Shivaramegowda SG, Prakash S, Lakshminarayana S, Patil S. Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies. Diagnostics 2021;11:2184. [DOI: 10.3390/diagnostics11122184] [Reference Citation Analysis]
8 Kim BW, Choi MC, Kim MK, Lee JW, Kim MT, Noh JJ, Park H, Jung SG, Joo WD, Song SH, Lee C. Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers. Cancers (Basel) 2021;13:5670. [PMID: 34830824 DOI: 10.3390/cancers13225670] [Reference Citation Analysis]
9 Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, Cato K, Hardiker N, Junger A, Michalowski M, Nyrup R, Rahimi S, Reed DN, Salakoski T, Salanterä S, Walton N, Weber P, Wiegand T, Topaz M. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs 2021;77:3707-17. [PMID: 34003504 DOI: 10.1111/jan.14855] [Reference Citation Analysis]
10 Zerka F, Urovi V, Bottari F, Leijenaar RTH, Walsh S, Gabrani-Juma H, Gueuning M, Vaidyanathan A, Vos W, Occhipinti M, Woodruff HC, Dumontier M, Lambin P. Privacy preserving distributed learning classifiers - Sequential learning with small sets of data. Comput Biol Med 2021;136:104716. [PMID: 34364262 DOI: 10.1016/j.compbiomed.2021.104716] [Reference Citation Analysis]
11 Duong D, Waikel RL, Hu P, Tekendo-Ngongang C, Solomon BD. Neural network classifiers for images of genetic conditions with cutaneous manifestations. HGG Adv 2022;3:100053. [PMID: 35047844 DOI: 10.1016/j.xhgg.2021.100053] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
12 Tabaie A, Orenstein EW, Nemati S, Basu RK, Clifford GD, Kamaleswaran R. Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines. Front Pediatr 2021;9:726870. [PMID: 34604142 DOI: 10.3389/fped.2021.726870] [Reference Citation Analysis]
13 Decherchi S, Pedrini E, Mordenti M, Cavalli A, Sangiorgi L. Opportunities and Challenges for Machine Learning in Rare Diseases. Front Med (Lausanne) 2021;8:747612. [PMID: 34676229 DOI: 10.3389/fmed.2021.747612] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Hasani N, Farhadi F, Morris MA, Nikpanah M, Rhamim A, Xu Y, Pariser A, Collins MT, Summers RM, Jones E, Siegel E, Saboury B. Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities. PET Clin 2022;17:13-29. [PMID: 34809862 DOI: 10.1016/j.cpet.2021.09.009] [Reference Citation Analysis]