Patient Satisfaction Through Interpretable Machine Learning Approach
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3932-9_5
Reference4 articles.
1. Yu J, Xing L, Tan X, Ren T, Li Z (2019) Doctor-patient combined matching problem and its solving algorithms. IEEE Access 7:177723–177733
2. Alsaqri S (2016) Patient satisfaction with quality of nursing care at governmental hospitals, Ha’il City, Saudi Arabia. J Biol Agric Healthc 6(10):128–142
3. Tsang G, Zhou SM, Xie X (2020) Modeling large sparse data for feature selection: hospital admission predictions of the dementia patients using primary care electronic health records. IEEE J Transl Eng Health Med 9:1–13
4. Sabarmathi G., Chinnaiyan R (2019) Reliable machine learning approach to predict patient satisfaction for optimal decision making and quality health care. In: Proceedings of the fourth international conference on communication and electronics systems (ICCES 2019). IEEE Xplore. ISBN 978-1-7281-1261-9
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