New approach in modelling of the patients behavior in Primary Health Care (PHC)
DOI:
https://doi.org/10.7494/automat.2015.19.1.17Keywords:
PHC, stochastic process modelling, population modellingAbstract
The purpose of this paper is to show the concept of simulation of Primary Health Care (PHC) patients behavior. There are two reasons for interest in this area. The first of them is the need for support in decision making regarding financing and management of PHC facilities on levels from national (Natonal Health Fund) to local (PHC provider). The second one is the lack of effective methods for such population modeling. In this paper author describe an idea of probability distribution modeling and present preliminary results of patient visits simulation over a period of a year.
Celem artykułu jest pokazanie koncepcji symulacji zachowań pacjentów Podstawowej Opieki Zdrowotnej (POZ). Temat ten jest ważny z dwóch powodów. Po pierwsze model taki mógłby posłużyć w podejmowaniu decyzji w rozdzielaniu funduszy pomiędzy jednostkami POZ przez Narodowy Fundusz Zdrowia. Drugim z powodów jest obecnie brak efektywnych metod modelowania populacji POZ. W niniejszej pracy autor opisuje ideę modelowania populacji przy użyciu rozkładu prawdopodobieństwa i przedstawia wstępne wyniki symulacji dla wizyt pacjentów w ciągu roku.
References
Brochu E., Brochu T., de Freitas N., A Bayesian Interactive Optimization Approach to Procedural Animation Design, Symposium on Computer Animation, 2010, pp. 103–12
Kringos D.S., Boerma W.G.W., Bourgueil Y., Cartier T., Hasvold T., Hutchinson A., Lember M., Oleszczyk M., Pavlic D.R., Svab I., Tedeschi P., Wilson A., Windak A., Dedeu T., Wilm S., The European primaryc a remonit or: structure, process an do utcome indicators, BMC Family Practice 2010, Vol. 11, pp. 81
Schäfer W.L., Boerma W.G.W., Kringos D.S., De Ryck E., Gre ß S., Heinemann S., Murante A.M., Rotar-Pavlic D., Schellevis F.G., Seghieri C., Van den Berg M. J ., Westert G.P., Willems S., Groenewegen P.P., Measures of quality, costs and equity in primary health care instruments developed to analyse and compare primary care in 35 countries, Quality in Primary Care 2013, Vol. 21, pp. 67–79
Jaynes E.T., Probability Theory: The Logic of Science, Cambridge University Press 2003
Sivia D., Skilling J ., Data Analysis: A Bayesian Tutorial, Oxford University Press, Oxford 2006
Lizotte D., Wang T., Bowling M., Schuurmans D., Automatic Gait Optimization with Gaussian Process Regression, 2007, pp. 944–949
Martinez-Cantin R., de Freitas N., Brochu E., Castellanos J ., Doucet A., A Bayesian Exploration - Exploitation Approach for Optimal Online Sensing and Planning with a Visually Guided Mobile Robot, Autonomous Robots 2009, Vol. 27, No. 3, pp. 93–103
Srinivas N., Krause A., Kakade S.M., Seeger M.W., Information - theoretic regret bounds for Gaussian process optimization in the band it setting, IEEE Transactions on Information Theory 2012, Vol. 58, No. 5, pp. 3250–3265
Garnett R., Osborne M.A., Roberts S. J., Bayes ian optimization for sensor set selection, International Conference on Information Processing in Sensor Networks – IPSN 2010, pp. 209–219
Bergstra J ., Yamins D., Cox D.D., Hyperopt: A Python Library for optimizing the hyper parameters of machine lerning algorithms, Python in Science Conference, 2013, pp. 13–20
Thornton C., Hutter F., Hoos H.H., Leyton-Brown K., Auto-WEK A: Combined Selection and Hyperparameter Optimization of Classification Algorithms, in: Proceedings of the 19 th ACM S IGKDD International Conference on Kn owledge Discovery and Data Mining, ACM, New York 2013, pp. 847–855
Snoek J ., Larochelle H., Adams R., Practical Bayesian Optimization of Machine Learning Algorithms, Neural Information Processing Systems Conference, 2012, pp. 1–12
Baranowski J ., Windak A., Optymalizacja polskiego systemu finansowania podstawowej opieki zdrowotnej, Technical Report, Ernst & Young Usługi Finansowe Audyt sp. z o.o. Polska sp.k., Warszawa 2012
