📄️ 11.1 Basics of Time Series in Healthcare
This chapter covers the basics of time series data in healthcare, including its characteristics, temporal patterns, and key concepts. It outlines the importance of time series analysis in healthcare applications and introduces techniques for analyzing time series data. Python libraries suitable for time series analysis are mentioned, along with a case study illustrating the application of time series analysis to patient temperature data. The conclusion emphasizes the value of time series analysis in healthcare for revealing insights and enhancing patient care.
📄️ 11.2 Time Series Decomposition
This section introduces the concept of time series decomposition and its importance in healthcare. It explains the components of decomposition (trend, seasonality, residual) and their significance. The methods of additive and multiplicative decomposition are discussed, along with their use cases. Python libraries suitable for time series decomposition are mentioned, and a case study involving hospital admissions is provided to illustrate the application of decomposition in healthcare. The conclusion highlights the benefits of time series decomposition in revealing insights and enhancing patient care.
📄️ 11.3 ARIMA Models for Patient Metrics
This section introduces ARIMA models and their significance in healthcare. It explains the components of ARIMA models (autoregressive, moving average, differencing) and their suitability for stationary time series data. The applications of ARIMA models in healthcare are highlighted, including demand forecasting, epidemic prediction, and resource allocation. The section also discusses Python libraries for implementing ARIMA models, and a case study involving patient admissions forecasting is provided to illustrate their application. The conclusion emphasizes the role of ARIMA models in enhancing patient care through data-driven decision-making.
📄️ 11.4 Prophet: Forecasting Time Series in Healthcare
This section introduces the Prophet forecasting tool and its applicability in healthcare. It explains the capabilities of Prophet, including handling seasonal effects and uncertainty in time series data. The applications of Prophet in healthcare, such as patient volume forecasting, disease trend prediction, and resource demand estimation, are highlighted. The implementation of Prophet in Python using the prophet library is discussed, and a case study involving patient appointment forecasting is presented. The conclusion emphasizes Prophet's role in enhancing patient care and resource allocation through accurate and interpretable time series forecasts.
📄️ Resources
- Pandas Time Series Documentation