New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows

Aladeemy, Mohammed; Adwan, Linda; Booth, Amy E.; Khasawneh, Mohammad T.; Poranki, Srikanth

doi:10.1016/j.asoc.2019.105866

Cited by 52 publications

(23 citation statements)

References 69 publications

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“…A very recent application of DTs to predict no-show is Aladeemy et al [ 51 ] in 2020. In this study, the authors considered different techniques such as DTs, random forest, k-nearest neighbors, support vector machines, boosting, naïve Bayes, and deep learning.…”

Section: Resultsmentioning

confidence: 99%

“…The most used technique within the wrapper methods was stepwise feature selection [ 7 , 32 , 33 , 34 , 38 ]. Other techniques are metaheuristics such as genetic algorithms [ 61 ] or Opposition-Based Self-Adaptive Cohort Intelligence [ 51 ]. Finally, embedded methods incorporate the selection of variables within the model itself.…”

Section: Discussionmentioning

confidence: 99%

“…This limitation makes it very difficult to predict missing attendance on the first visit. Different authors have addressed this problem by means of different techniques such as not including in the analysis patients who do not have a certain number of previous visits [ 5 , 28 ], not including the first appointment in the study [ 7 , 15 , 16 , 21 , 30 , 44 , 53 ], or including a variable that indicates whether the appointment corresponds to the first visit [ 14 , 17 , 20 , 23 , 27 , 29 , 32 , 34 , 40 , 41 , 42 , 43 , 48 , 50 , 51 , 52 , 56 , 58 , 59 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

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Patient No-Show Prediction: A Systematic Literature Review

Carreras-García

Delgado-Gómez

Llorente

et al. 2020

Entropy

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Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Patient No-Show Prediction: A Systematic Literature Review

Carreras-García

Delgado-Gómez

Llorente

et al. 2020

Entropy

View full text Add to dashboard Cite

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“…Therefore, integrating a stochastic model with the continuous genetic algorithm (CGA) is used in the current study. (23) where N is the number of samples, MSE is the mean square error and Comp. is the complexity of the model.…”

Section: Stochastic Modelingmentioning

confidence: 99%

A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events.

Bonakdari

Pelletier

Martel‐Pelletier

2020

Preprint

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Background. An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-intervention. We propose a novel continuous transitional data-driven hybrid methodology using a non-linear approach based on a combination of stochastic and artificial intelligence methods that facilitate the evaluation of ITS data without knowledge of lag time. Understanding the influence of implemented intervention on outcome(s) is imperative for decision makers in order to manage health systems accurately and in a timely manner. Methods. To validate a developed hybrid model, we used, as an example, a published dataset based on a real health problem on the effects of the Italian smoking ban in public spaces on hospital admissions for acute coronary events. We employed a continuous methodology based on data preprocessing to identify linear and nonlinear components in which autoregressive moving average and generalized structure group method of data handling were combined to model stochastic and nonlinear components of ITS. We analyzed the rate of admission for acute coronary events from January 2002 to November 2006 using this new data-driven hybrid methodology that allowed for long-term outcome prediction. Results. Our results showed the Pearson correlation coefficient of the proposed combined transitional data-driven model exhibited an average of 17.74% enhancement from the single stochastic model and 2.05% from the nonlinear model. In addition, data demonstrated that the developed model improved the mean absolute percentage error and correlation coefficient values for which 2.77% and 0.89 were found compared to 4.02% and 0.76, respectively. Importantly, this model does not use any predefined lag time between pre- and post-intervention.Conclusions. Most of the previous studies employed the linear regression and considered a lag time to interpret the impact of intervention on public health outcome. The proposed hybrid methodology improved ITS prediction from conventional methods and could be used as a reliable alternative in public health intervention evaluation.

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“…Over the past few years, soft computing methods have been employed across domains and have established reliable tools for modeling complex systems and predicting different phenomena in healthcare [18][19][20][21][22][23][24]. Among soft computing techniques, the Group Method of Data Handling (GMDH) is a common self-organizing heuristic model, which can be used for simulating complicated nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events

2020

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Background An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-intervention. We propose a novel continuous transitional data-driven hybrid methodology using a non-linear approach based on a combination of stochastic and artificial intelligence methods that facilitate the evaluation of ITS data without knowledge of lag time. Understanding the influence of implemented intervention on outcome(s) is imperative for decision makers in order to manage health systems accurately and in a timely manner. Methods To validate a developed hybrid model, we used, as an example, a published dataset based on a real health problem on the effects of the Italian smoking ban in public spaces on hospital admissions for acute coronary events. We employed a continuous methodology based on data preprocessing to identify linear and nonlinear components in which autoregressive moving average and generalized structure group method of data handling were combined to model stochastic and nonlinear components of ITS. We analyzed the rate of admission for acute coronary events from January 2002 to November 2006 using this new data-driven hybrid methodology that allowed for long-term outcome prediction. Results Our results showed the Pearson correlation coefficient of the proposed combined transitional data-driven model exhibited an average of 17.74% enhancement from the single stochastic model and 2.05% from the nonlinear model. In addition, data demonstrated that the developed model improved the mean absolute percentage error and correlation coefficient values for which 2.77% and 0.89 were found compared to 4.02% and 0.76, respectively. Importantly, this model does not use any predefined lag time between pre- and post-intervention. Conclusions Most of the previous studies employed the linear regression and considered a lag time to interpret the impact of intervention on public health outcome. The proposed hybrid methodology improved ITS prediction from conventional methods and could be used as a reliable alternative in public health intervention evaluation.

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New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows

Cited by 52 publications

References 69 publications

Patient No-Show Prediction: A Systematic Literature Review

Patient No-Show Prediction: A Systematic Literature Review

A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events.

A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events

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