@article{10.11648/j.ajns.20251402.12,
  author = {Xian Huang and Yan Zhang and Yanru Wang and Huamei Lin and Yuzhu Yang},
  title = {Predictive Model for Nonadherence to Biologic Therapy in Ulcerative Colitis Patients Based on Machine Learning
},
  journal = {American Journal of Nursing Science},
  volume = {14},
  number = {2},
  pages = {30-36},
  doi = {10.11648/j.ajns.20251402.12},
  url = {https://doi.org/10.11648/j.ajns.20251402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajns.20251402.12},
  abstract = {Objective: To develop a clinical prediction model for nonadherence to biologic therapy in patients with ulcerative colitis (UC) using machine learning techniques, and to assess its predictive accuracy to guide clinical interventions. Methods: A total of 221 UC patients who initiated biologic therapy between December 2023 and February 2025 at our hospital were included in this study. Data on 15 variables, such as age, sex, disease duration, and other clinical factors, were collected. Medication adherence was measured using the proportion of days covered (PDC), with a PDC >80% considered indicative of good adherence. The Support Vector Machine (SVM) and Boruta algorithms were employed to identify key predictors. A multivariate logistic regression model was developed using the intersection of factors identified by both algorithms. Model performance was evaluated using the C-index, ROC curve, calibration curve, decision curve analysis, and validation with the K-nearest neighbor (KNN) algorithm. Results: Of the 221 patients, 82 (37.1%) were categorized into the nonadherence group. Following factor selection, insurance status, depression level, education level, disease activity, and age were identified as significant predictors of nonadherence. The model demonstrated a C-index of 0.779, and the ROC curve showed an area under the curve (AUC) of 0.78. Calibration curve analysis revealed good model consistency, and KNN validation yielded high precision with an AUC of 0.9978 and a PR AUC of 0.9963. Conclusion: The developed prediction model for medication nonadherence in UC patients demonstrates robust predictive and calibration capabilities. This model may aid healthcare professionals in identifying high-risk patients and supporting timely clinical interventions.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Predictive Model for Nonadherence to Biologic Therapy in Ulcerative Colitis Patients Based on Machine Learning

AU  - Xian Huang
AU  - Yan Zhang
AU  - Yanru Wang
AU  - Huamei Lin
AU  - Yuzhu Yang
Y1  - 2025/05/22
PY  - 2025
N1  - https://doi.org/10.11648/j.ajns.20251402.12
DO  - 10.11648/j.ajns.20251402.12
T2  - American Journal of Nursing Science
JF  - American Journal of Nursing Science
JO  - American Journal of Nursing Science
SP  - 30
EP  - 36
PB  - Science Publishing Group
SN  - 2328-5753
UR  - https://doi.org/10.11648/j.ajns.20251402.12
AB  - Objective: To develop a clinical prediction model for nonadherence to biologic therapy in patients with ulcerative colitis (UC) using machine learning techniques, and to assess its predictive accuracy to guide clinical interventions. Methods: A total of 221 UC patients who initiated biologic therapy between December 2023 and February 2025 at our hospital were included in this study. Data on 15 variables, such as age, sex, disease duration, and other clinical factors, were collected. Medication adherence was measured using the proportion of days covered (PDC), with a PDC >80% considered indicative of good adherence. The Support Vector Machine (SVM) and Boruta algorithms were employed to identify key predictors. A multivariate logistic regression model was developed using the intersection of factors identified by both algorithms. Model performance was evaluated using the C-index, ROC curve, calibration curve, decision curve analysis, and validation with the K-nearest neighbor (KNN) algorithm. Results: Of the 221 patients, 82 (37.1%) were categorized into the nonadherence group. Following factor selection, insurance status, depression level, education level, disease activity, and age were identified as significant predictors of nonadherence. The model demonstrated a C-index of 0.779, and the ROC curve showed an area under the curve (AUC) of 0.78. Calibration curve analysis revealed good model consistency, and KNN validation yielded high precision with an AUC of 0.9978 and a PR AUC of 0.9963. Conclusion: The developed prediction model for medication nonadherence in UC patients demonstrates robust predictive and calibration capabilities. This model may aid healthcare professionals in identifying high-risk patients and supporting timely clinical interventions.

VL  - 14
IS  - 2
ER  - 

Copy | Download