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Singh Y, Mars M. HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation. JMIR Res Protoc. 2012;1(1):e3doi: 10.2196/resprot.1930.
Singh, Y., & Mars, M. (2012). HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation. JMIR research protocols, 1(1), e3. https://doi.org/10.2196/resprot.1930
Singh, Yashik, and Mars, Maurice. "HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation." JMIR research protocols vol. 1,1 (2012): e3. doi: https://doi.org/10.2196/resprot.1930
Singh Y, Mars M. HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation. JMIR Res Protoc. 2012 Jun 07;1(1):e3. doi: 10.2196/resprot.1930. PMID: 23611761; PMCID: PMC3626142.
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JMIR Res Protoc. 2012 Jun 07;1(1):e3. doi: 10.2196/resprot.1930.

HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation.

JMIR research protocols

Yashik Singh, Maurice Mars

Affiliations

  1. Department of TeleHealth, Nelson R Mandela school of Medicine, University of KwaZulu-Natal, Durban, South Africa. [email protected].

PMID: 23611761 PMCID: PMC3626142 DOI: 10.2196/resprot.1930

Abstract

INTRODUCTION: The science of information systems, management, and interpretation plays an important part in the continuity of care of patients. This is becoming more evident in the treatment of human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS), the leading cause of death in sub-Saharan Africa. The high replication rates, selective pressure, and initial infection by resistant strains of HIV infer that drug resistance will inevitably become an important health care concern. This paper describes proposed research with the aim of developing a physician-administered, artificial intelligence-based decision support system tool to facilitate the management of patients on antiretroviral therapy.

METHODS: This tool will consist of (1) an artificial intelligence computer program that will determine HIV drug resistance information from genomic analysis; (2) a machine-learning algorithm that can predict future CD4 count information given a genomic sequence; and (3) the integration of these tools into an electronic medical record for storage and management.

CONCLUSION: The aim of the project is to create an electronic tool that assists clinicians in managing and interpreting patient information in order to determine the optimal therapy for drug-resistant HIV patients.

Keywords: Bioinformatics; HIV drug resistance; Machine Learning; Medical Informatics

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