A transfer learning approach to drug resistance classification in mixed HIV dataset

Funding: This research is funded by the Tertiary Education Trust Fund (TETFund), Nigeria.As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.Publisher PDFPeer reviewe

Processed HIV prognostic dataset for control experiments

This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription