This paper is in the following e-collection/theme issue:

Artificial Intelligence (1402) Diabetes Self-Management (491) Machine Learning (1486) Clinical Information and Decision Making (1423)

Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37233, first published .
Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation

Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation

Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation

Authors of this article:

Luis Fernando Granda Morales1 Author Orcid Image ;   Priscila Valdiviezo-Diaz1 Author Orcid Image ;   Ruth Reátegui1 Author Orcid Image ;   Luis Barba-Guaman1 Author Orcid Image
Article Authors Cited by (22) Tweetations (4) Metrics

Journals

  1. Tan W, Gao Q, Oei R, Hsu W, Lee M, Tan N. Diabetes medication recommendation system using patient similarity analytics. Scientific Reports 2022;12(1) View
  2. Sae-Ang A, Chairat S, Tansuebchueasai N, Fumaneeshoat O, Ingviya T, Chaichulee S. Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches. International Journal of Environmental Research and Public Health 2022;20(1):309 View
  3. Howell P, Aryal A, Wu C. Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions. JMIR Research Protocols 2023;12:e43316 View
  4. Sun Y, Zhou J, Ji M, Pei L, Wang Z. Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping. Journal of Medical Internet Research 2023;25:e38184 View
  5. Zhang Y, Zhang D, Liu X, Peng W, Mu Y, Li Y, Qiu Q. A Practical Statin Recommendation System Based on Real-World Data to Improve LDL-C Management in Secondary Prevention. Journal of Cardiovascular Pharmacology 2023;81(5):373 View
  6. Liu P, Wang Z, Liu N, Peres M. A scoping review of the clinical application of machine learning in data-driven population segmentation analysis. Journal of the American Medical Informatics Association 2023;30(9):1573 View
  7. Amin A, Jahanshahi M, Meybodi M. Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN. Applied Sciences 2023;13(18):10073 View
  8. Ma H, Zhang J, Li C, Zou L. Discovery of anthraquinones as DPP-IV inhibitors: Structure-activity relationships and inhibitory mechanism. Fitoterapia 2023;168:105549 View
  9. Slade E, Rennick-Egglestone S, Ng F, Kotera Y, Llewellyn-Beardsley J, Newby C, Glover T, Keppens J, Slade M. The Implementation of Recommender Systems for Mental Health Recovery Narratives: Evaluation of Use and Performance. JMIR Mental Health 2024;11:e45754 View
  10. Singhal S, Pal K. State of art and emerging trends on group recommender system: a comprehensive review. International Journal of Multimedia Information Retrieval 2024;13(2) View
  11. Afanasieva T, Zamashkin Y. Opportunities of patient-oriented systems for digital prevention of chronic non-communicable diseases. Russian Journal of Preventive Medicine 2024;27(6):7 View
  12. Anbazhagan E, Sophiya E, Prasanna Kumar R. Sentiment-aware drug recommendations with a focus on symptom-condition mapping. International Journal of Information Technology 2024;16(8):5195 View
  13. Harikumar S, Jayamohan Pillai C, Vani Chithra V, Raman R, Kaimal M, Tang K, Nedungadi P. High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training Programs. IEEE Access 2024;12:171807 View

Books/Policy Documents

  1. Shah S, Naik V, Mukhopadhyay D, Roy S. Internet of Things. Advances in Information and Communication Technology. View
  2. Swathi Mirthika G, Sivakumar B. Intelligent Sustainable Systems. View