NDLI: Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

Content Provider	Springer Nature : BioMed Central
Author	Li, Min Wang, Qunwei Shen, Yinzhong
Abstract	Background Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. Methods We cleaned 257,305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16,440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. Results The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10,614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. Conclusions This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.
Related Links	https://aidsrestherapy.biomedcentral.com/counter/pdf/10.1186/s12981-020-00326-8.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	17426405
DOI	10.1186/s12981-020-00326-8
Journal	AIDS Research and Therapy
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2021-01-28
Access Restriction	Open
Subject Keyword	Infectious Diseases Virology Antiretroviral therapy Adherence prediction RFM AIDS
Content Type	Text
Resource Type	Article
Subject	Molecular Medicine Virology Pharmacology (medical)
Journal Impact Factor	2.1/2023
5-Year Journal Impact Factor	2.4/2023

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