Suchergebnisse - ACM: C.: Computer Systems Organization/C.5: COMPUTER SYSTEM IMPLEMENTATION/C.5.3: Microcomputers

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    Autoren: Quattlebaum TG; Department of Pediatrics, Medical University of South Carolina, Charleston 29425.

    Quelle: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 1988 Jan-Feb; Vol. 26 (1), pp. 45-52.

    Publikationsart: Journal Article

    Info zur Zeitschrift: Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print Cited Medium: Print ISSN: 0169-2607 (Print) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE

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    1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., . & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., . Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras

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    Autoren: Grant A Delisle E Dubois S et al.

    Quelle: M.D. computing : computers in medical practice [MD Comput] 1995 Jan-Feb; Vol. 12 (1), pp. 45-9.

    Publikationsart: Journal Article; Research Support, Non-U.S. Gov't

    Info zur Zeitschrift: Publisher: Springer-Verlag New York Inc Country of Publication: United States NLM ID: 8408946 Publication Model: Print Cited Medium: Print ISSN: 0724-6811 (Print) Linking ISSN: 07246811 NLM ISO Abbreviation: MD Comput Subsets: MEDLINE

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    Autoren: Volkelt, Johannes Immanuel, 1848-1930, Auteur du texte

    Quelle: Bibliothèque nationale de France, département Philosophie, histoire, sciences de l'homme, 4-R-3004 (3), 1925-1927.

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    Autoren: Volkelt, Johannes Immanuel, 1848-1930, Auteur du texte

    Quelle: Bibliothèque nationale de France, département Philosophie, histoire, sciences de l'homme, 4-R-3004 (1), 1925-1927.

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    Autoren: Volkelt, Johannes Immanuel, 1848-1930, Auteur du texte

    Quelle: Bibliothèque nationale de France, département Philosophie, histoire, sciences de l'homme, 4-R-3004 (2), 1925-1927.

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    Autoren: Rickert, Heinrich, 1863-1936, Auteur du texte

    Quelle: Bibliothèque nationale de France, département Philosophie, histoire, sciences de l'homme, Z KOJEVE-3611, 1921.

    Relation: Notice du catalogue : http://catalogue.bnf.fr/ark:/12148/cb40940976k

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    Quelle: Communications of the ACM; Nov82, Vol. 25 Issue 11, p772-780, 9p, 7 Diagrams, 1 Chart