AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures
Both statistical and neural methods have been proposed in the literature to predict healthcare expenditures. However, less attention has been given to comparing predictions from both these methods as well as ensemble approaches in the healthcare domain. The primary objective of this paper was to eva...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-03-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fdata.2020.00004/full |
| _version_ | 1818302226017288192 |
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| author | Shruti Kaushik Abhinav Choudhury Pankaj Kumar Sheron Nataraj Dasgupta Sayee Natarajan Larry A. Pickett Varun Dutt |
| author_facet | Shruti Kaushik Abhinav Choudhury Pankaj Kumar Sheron Nataraj Dasgupta Sayee Natarajan Larry A. Pickett Varun Dutt |
| author_sort | Shruti Kaushik |
| collection | DOAJ |
| description | Both statistical and neural methods have been proposed in the literature to predict healthcare expenditures. However, less attention has been given to comparing predictions from both these methods as well as ensemble approaches in the healthcare domain. The primary objective of this paper was to evaluate different statistical, neural, and ensemble techniques in their ability to predict patients' weekly average expenditures on certain pain medications. Two statistical models, persistence (baseline) and autoregressive integrated moving average (ARIMA), a multilayer perceptron (MLP) model, a long short-term memory (LSTM) model, and an ensemble model combining predictions of the ARIMA, MLP, and LSTM models were calibrated to predict the expenditures on two different pain medications. In the MLP and LSTM models, we compared the influence of shuffling of training data and dropout of certain nodes in MLPs and nodes and recurrent connections in LSTMs in layers during training. Results revealed that the ensemble model outperformed the persistence, ARIMA, MLP, and LSTM models across both pain medications. In general, not shuffling the training data and adding the dropout helped the MLP models and shuffling the training data and not adding the dropout helped the LSTM models across both medications. We highlight the implications of using statistical, neural, and ensemble methods for time-series forecasting of outcomes in the healthcare domain. |
| first_indexed | 2024-12-13T05:35:32Z |
| format | Article |
| id | doaj.art-65a39b876d54471d9b88fa9d75652009 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-12-13T05:35:32Z |
| publishDate | 2020-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-65a39b876d54471d9b88fa9d756520092022-12-21T23:57:57ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2020-03-01310.3389/fdata.2020.00004475663AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble ArchitecturesShruti Kaushik0Abhinav Choudhury1Pankaj Kumar Sheron2Nataraj Dasgupta3Sayee Natarajan4Larry A. Pickett5Varun Dutt6Applied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Mandi, IndiaApplied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Mandi, IndiaApplied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Mandi, IndiaRxDataScience, Inc., Durham, NC, United StatesRxDataScience, Inc., Durham, NC, United StatesRxDataScience, Inc., Durham, NC, United StatesApplied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Mandi, IndiaBoth statistical and neural methods have been proposed in the literature to predict healthcare expenditures. However, less attention has been given to comparing predictions from both these methods as well as ensemble approaches in the healthcare domain. The primary objective of this paper was to evaluate different statistical, neural, and ensemble techniques in their ability to predict patients' weekly average expenditures on certain pain medications. Two statistical models, persistence (baseline) and autoregressive integrated moving average (ARIMA), a multilayer perceptron (MLP) model, a long short-term memory (LSTM) model, and an ensemble model combining predictions of the ARIMA, MLP, and LSTM models were calibrated to predict the expenditures on two different pain medications. In the MLP and LSTM models, we compared the influence of shuffling of training data and dropout of certain nodes in MLPs and nodes and recurrent connections in LSTMs in layers during training. Results revealed that the ensemble model outperformed the persistence, ARIMA, MLP, and LSTM models across both pain medications. In general, not shuffling the training data and adding the dropout helped the MLP models and shuffling the training data and not adding the dropout helped the LSTM models across both medications. We highlight the implications of using statistical, neural, and ensemble methods for time-series forecasting of outcomes in the healthcare domain.https://www.frontiersin.org/article/10.3389/fdata.2020.00004/fulltime-series forecastingpersistenceautoregressive integrated moving average (ARIMA)multilayer perceptron (MLP)long short-term memory (LSTM)ensemble |
| spellingShingle | Shruti Kaushik Abhinav Choudhury Pankaj Kumar Sheron Nataraj Dasgupta Sayee Natarajan Larry A. Pickett Varun Dutt AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures Frontiers in Big Data time-series forecasting persistence autoregressive integrated moving average (ARIMA) multilayer perceptron (MLP) long short-term memory (LSTM) ensemble |
| title | AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures |
| title_full | AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures |
| title_fullStr | AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures |
| title_full_unstemmed | AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures |
| title_short | AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures |
| title_sort | ai in healthcare time series forecasting using statistical neural and ensemble architectures |
| topic | time-series forecasting persistence autoregressive integrated moving average (ARIMA) multilayer perceptron (MLP) long short-term memory (LSTM) ensemble |
| url | https://www.frontiersin.org/article/10.3389/fdata.2020.00004/full |
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