A causal model for short‐term time series analysis to predict incoming Medicare workload

Mizan, Tasquia; Taghipour, Sharareh

doi:10.1002/for.2717

Cited by 5 publications

(6 citation statements)

References 41 publications

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“…Li et al [14] have proposed a dynamically updated ensemble for learning imbalanced data streams with concept drift. A rank-based ensemble is proposed [16] to predict short-term online data which allows lower runtime of the ensemble model. Similar to the ranked-based approach, a heterogeneous dynamic weighted majority [20] method is also applied to the ensemble modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Mizan,

Taghipour

2022

Scientia Iranica

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Real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. The erroneous data causes inaccurate forecasting which leads to business loss. Moreover, the concept drift problem is a known problem in time series forecasting that also results in poor forecasting accuracy. The execution time of a machine learning model is also crucial when it is deployed in a real-time environment. This work presents an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the timevariant windowing technique. A data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. A meta-model is developed in the online forecasting phase which ensures faster execution for incoming data. The model is implemented for the medical workload prediction after testing and comparing with a few other heterogeneous ensemble models. The comparison results show in terms of the root mean squared error, the proposed model performs at least 65.7% better than the heterogeneous stacked ensemble models on the experimental dataset. Moreover, in comparison to the other standalone models considered in this experiment, the ABRE model reduces the prediction error by approximately 73.6%.

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Section: Literature Reviewmentioning

confidence: 99%

“…In the ML domain, the problem of changing relationships over time is known as concept drift [14]. Prediction on online time-series data requires faster execution time [16]. Models with faster execution time may result in poor prediction accuracy [17].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Mizan,

Taghipour

2022

Scientia Iranica

Self Cite

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“…In making use of forecasting in business and management research, key issues to be considered are the variable(s) to be predicted, the accuracy required, the horizon and timing of the forecast and, more importantly, the data on which the forecast is based (Makridakis and Wheelwright, 1977;Ren et al, 2020). Accuracy is a key dimension of a forecast; an inaccurate prediction can easily result in losses for a business (Mizan and Taghipour, 2021), and this may also lead to research lacking in 540 D. Thomakos et al rigour. In turn, the accuracy of quantitative business forecasts depends on the availability of relevant data.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in such instances, each data point becomes much more important than if the data were more comprehensive, which can lead to biased forecasting results (Weigand, Lange and Rauschenberger, 2021). In some cases, there may be no clear trend shown by a short time series, whereas a pattern might be revealed by more comprehensive data (Mizan and Taghipour, 2021).…”

Section: Introductionmentioning

confidence: 99%

ShoTS Forecasting: Short Time Series Forecasting for Management Research

Thomakos

Wood

Ioakimidis

et al. 2022

British J of Management

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We present a novel method for forecasting with limited information, that is for forecasting short time series. Our method is simple and intuitive; it relates to the most fundamental forecasting benchmark and is straightforward to implement. We present the technical details of the method and explain the nuances of how it works via two illustrative examples, with the use of employment-related data. We find that our new method outperforms standard forecasting methods and thus offers considerable utility in applied management research. The implications of our findings suggest that forecasting short time series, of which one can find many examples in business and management, is viable and can be of considerable practical help for both research and practice -even when the information available to analysts and decision-makers is limited.

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“…have proposed a dynamically updated ensemble for learning imbalanced data streams with concept drift. A rank-based ensemble is proposed(Mizan and Taghipour, 2021) to predict short-term online data which allows lower runtime of the ensemble model. Similar to the ranked-based approach, a Heterogeneous Dynamic Weighted Majority (HDWM)(Idrees et al, 2020) method is also applied to the ensemble modeling Ancy and Paulraj (2020).…”

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confidence: 99%

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

Mizan

2024

Preprint

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The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.

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A causal model for short‐term time series analysis to predict incoming Medicare workload

Cited by 5 publications

References 41 publications

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

ShoTS Forecasting: Short Time Series Forecasting for Management Research

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

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