Applying sequential pattern mining to investigate cerebrovascular health outpatients' re-visit patterns

Chao Ou-Yang, Chandrawati Putri Wulandari, Rizka Aisha Rahmi Hariadi, Han-Cheng Wang, Chiehfeng Chen

研究成果: 雜誌貢獻文章

摘要

Background and Objective: Increases in outpatients seeking medical check-ups are expanding the number of health examination data records, which can be utilized for medical strategic planning and other purposes. However, because hospital visits by outpatients seeking medical check-ups are unpredictable, those patients often cannot receive optimal service due to limited facilities of hospitals. To resolve this problem, this study attempted to predict re-visit patterns of outpatients.

Method: Two-phase sequential pattern mining (SPM) and an association mining method were chosen to predict patient returns using sequential data. The data were grouped according to the outpatients' personal information and evaluated by a discriminant analysis to check the significance of the grouping. Furthermore, SPM was employed to generate frequency patterns from each group and extract a general association pattern of return.

Results: Results of sequence patterns and association mining in this study provided valuable insights in terms of outpatients' re-visit behaviors for regular medical check-ups. Cosine and Jaccard are two symmetric measures which were used in this study to indicate the degree of association between two variables. For instance, Jaccard values of variable abnormal blood pressure associated with an abnormal body-mass index (BMI) and/or abnormal blood sugar were respectively 47.5% and 100%, for the two-visit and three-visit behavior patterns. These results indicated that the corresponding pair of variables was more reliable when covering the three-visit behavior pattern than the two-visit behavior. Thus, appropriate preventive measures or suggestions for other medical treatments can be prepared for outpatients that have this pattern on their third visit. The higher degree of association implies that the corresponding behavior pattern might influence outpatients' intentions to regularly seek medical check-ups concerning the risk of stroke. Furthermore, a radiology diagnosis (i.e., magnetic resonance imaging or neck vascular ultrasound) plays an important role in the association with a re-visit behavior pattern with respective 50% and 70% Cosine and Jaccard values in general behavior {f11}∧{f01}. These findings can serve as valuable information to increase the quality of medical services and marketing, by suggesting appropriate treatment for the subsequent visit after learning the behavior patterns.

Conclusions: The proposed method can provide valuable information related to outpatients' re-visit behavior patterns based on hidden knowledge generated from sequential patterns and association mining results. For marketing purposes, medical practitioners can take behavior patterns studied in this paper into account to raise patients' awareness of several possible medical conditions that might arise on subsequent visits and encourage them to take preventive measures or suggest other medical treatments.
原文英語
頁(從 - 到)e5183
期刊PeerJ
6
DOIs
出版狀態已發佈 - 2018

指紋

Marketing
Outpatients
Health
Radiology
Strategic planning
Blood pressure
Discriminant analysis
Magnetic resonance
Blood Glucose
Ultrasonics
Imaging techniques
medical treatment
marketing
radiology
Discriminant Analysis
stroke
physicians
magnetic resonance imaging
blood vessels
Blood Vessels

引用此文

Applying sequential pattern mining to investigate cerebrovascular health outpatients' re-visit patterns. / Ou-Yang, Chao; Wulandari, Chandrawati Putri; Hariadi, Rizka Aisha Rahmi; Wang, Han-Cheng; Chen, Chiehfeng.

於: PeerJ, 卷 6, 2018, p. e5183.

研究成果: 雜誌貢獻文章

Ou-Yang, Chao ; Wulandari, Chandrawati Putri ; Hariadi, Rizka Aisha Rahmi ; Wang, Han-Cheng ; Chen, Chiehfeng. / Applying sequential pattern mining to investigate cerebrovascular health outpatients' re-visit patterns. 於: PeerJ. 2018 ; 卷 6. 頁 e5183.
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abstract = "Background and Objective: Increases in outpatients seeking medical check-ups are expanding the number of health examination data records, which can be utilized for medical strategic planning and other purposes. However, because hospital visits by outpatients seeking medical check-ups are unpredictable, those patients often cannot receive optimal service due to limited facilities of hospitals. To resolve this problem, this study attempted to predict re-visit patterns of outpatients.Method: Two-phase sequential pattern mining (SPM) and an association mining method were chosen to predict patient returns using sequential data. The data were grouped according to the outpatients' personal information and evaluated by a discriminant analysis to check the significance of the grouping. Furthermore, SPM was employed to generate frequency patterns from each group and extract a general association pattern of return.Results: Results of sequence patterns and association mining in this study provided valuable insights in terms of outpatients' re-visit behaviors for regular medical check-ups. Cosine and Jaccard are two symmetric measures which were used in this study to indicate the degree of association between two variables. For instance, Jaccard values of variable abnormal blood pressure associated with an abnormal body-mass index (BMI) and/or abnormal blood sugar were respectively 47.5{\%} and 100{\%}, for the two-visit and three-visit behavior patterns. These results indicated that the corresponding pair of variables was more reliable when covering the three-visit behavior pattern than the two-visit behavior. Thus, appropriate preventive measures or suggestions for other medical treatments can be prepared for outpatients that have this pattern on their third visit. The higher degree of association implies that the corresponding behavior pattern might influence outpatients' intentions to regularly seek medical check-ups concerning the risk of stroke. Furthermore, a radiology diagnosis (i.e., magnetic resonance imaging or neck vascular ultrasound) plays an important role in the association with a re-visit behavior pattern with respective 50{\%} and 70{\%} Cosine and Jaccard values in general behavior {f11}∧{f01}. These findings can serve as valuable information to increase the quality of medical services and marketing, by suggesting appropriate treatment for the subsequent visit after learning the behavior patterns.Conclusions: The proposed method can provide valuable information related to outpatients' re-visit behavior patterns based on hidden knowledge generated from sequential patterns and association mining results. For marketing purposes, medical practitioners can take behavior patterns studied in this paper into account to raise patients' awareness of several possible medical conditions that might arise on subsequent visits and encourage them to take preventive measures or suggest other medical treatments.",
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AU - Wang, Han-Cheng

AU - Chen, Chiehfeng

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N2 - Background and Objective: Increases in outpatients seeking medical check-ups are expanding the number of health examination data records, which can be utilized for medical strategic planning and other purposes. However, because hospital visits by outpatients seeking medical check-ups are unpredictable, those patients often cannot receive optimal service due to limited facilities of hospitals. To resolve this problem, this study attempted to predict re-visit patterns of outpatients.Method: Two-phase sequential pattern mining (SPM) and an association mining method were chosen to predict patient returns using sequential data. The data were grouped according to the outpatients' personal information and evaluated by a discriminant analysis to check the significance of the grouping. Furthermore, SPM was employed to generate frequency patterns from each group and extract a general association pattern of return.Results: Results of sequence patterns and association mining in this study provided valuable insights in terms of outpatients' re-visit behaviors for regular medical check-ups. Cosine and Jaccard are two symmetric measures which were used in this study to indicate the degree of association between two variables. For instance, Jaccard values of variable abnormal blood pressure associated with an abnormal body-mass index (BMI) and/or abnormal blood sugar were respectively 47.5% and 100%, for the two-visit and three-visit behavior patterns. These results indicated that the corresponding pair of variables was more reliable when covering the three-visit behavior pattern than the two-visit behavior. Thus, appropriate preventive measures or suggestions for other medical treatments can be prepared for outpatients that have this pattern on their third visit. The higher degree of association implies that the corresponding behavior pattern might influence outpatients' intentions to regularly seek medical check-ups concerning the risk of stroke. Furthermore, a radiology diagnosis (i.e., magnetic resonance imaging or neck vascular ultrasound) plays an important role in the association with a re-visit behavior pattern with respective 50% and 70% Cosine and Jaccard values in general behavior {f11}∧{f01}. These findings can serve as valuable information to increase the quality of medical services and marketing, by suggesting appropriate treatment for the subsequent visit after learning the behavior patterns.Conclusions: The proposed method can provide valuable information related to outpatients' re-visit behavior patterns based on hidden knowledge generated from sequential patterns and association mining results. For marketing purposes, medical practitioners can take behavior patterns studied in this paper into account to raise patients' awareness of several possible medical conditions that might arise on subsequent visits and encourage them to take preventive measures or suggest other medical treatments.

AB - Background and Objective: Increases in outpatients seeking medical check-ups are expanding the number of health examination data records, which can be utilized for medical strategic planning and other purposes. However, because hospital visits by outpatients seeking medical check-ups are unpredictable, those patients often cannot receive optimal service due to limited facilities of hospitals. To resolve this problem, this study attempted to predict re-visit patterns of outpatients.Method: Two-phase sequential pattern mining (SPM) and an association mining method were chosen to predict patient returns using sequential data. The data were grouped according to the outpatients' personal information and evaluated by a discriminant analysis to check the significance of the grouping. Furthermore, SPM was employed to generate frequency patterns from each group and extract a general association pattern of return.Results: Results of sequence patterns and association mining in this study provided valuable insights in terms of outpatients' re-visit behaviors for regular medical check-ups. Cosine and Jaccard are two symmetric measures which were used in this study to indicate the degree of association between two variables. For instance, Jaccard values of variable abnormal blood pressure associated with an abnormal body-mass index (BMI) and/or abnormal blood sugar were respectively 47.5% and 100%, for the two-visit and three-visit behavior patterns. These results indicated that the corresponding pair of variables was more reliable when covering the three-visit behavior pattern than the two-visit behavior. Thus, appropriate preventive measures or suggestions for other medical treatments can be prepared for outpatients that have this pattern on their third visit. The higher degree of association implies that the corresponding behavior pattern might influence outpatients' intentions to regularly seek medical check-ups concerning the risk of stroke. Furthermore, a radiology diagnosis (i.e., magnetic resonance imaging or neck vascular ultrasound) plays an important role in the association with a re-visit behavior pattern with respective 50% and 70% Cosine and Jaccard values in general behavior {f11}∧{f01}. These findings can serve as valuable information to increase the quality of medical services and marketing, by suggesting appropriate treatment for the subsequent visit after learning the behavior patterns.Conclusions: The proposed method can provide valuable information related to outpatients' re-visit behavior patterns based on hidden knowledge generated from sequential patterns and association mining results. For marketing purposes, medical practitioners can take behavior patterns studied in this paper into account to raise patients' awareness of several possible medical conditions that might arise on subsequent visits and encourage them to take preventive measures or suggest other medical treatments.

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