Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions
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- Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
- Sundmacher, Leonie & Fischbach, Diana & Schuettig, Wiebke & Naumann, Christoph & Augustin, Uta & Faisst, Cristina, 2015. "Which hospitalisations are ambulatory care-sensitive, to what degree, and how could the rates be reduced? Results of a group consensus study in Germany," Health Policy, Elsevier, vol. 119(11), pages 1415-1423.
- Jonas Krämer & Jonas Schreyögg & Reinhard Busse, 2019. "Classification of hospital admissions into emergency and elective care: a machine learning approach," Health Care Management Science, Springer, vol. 22(1), pages 85-105, March.
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Keywords
real-world evidence; prediction model; claims data; machine learning; integrated care; ambulatory care-sensitive conditions; hospitalization; prevention; population health;All these keywords.
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