Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy

Abstract

Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

“…The scoping review identified many studies that developed or validated models to predict therapeutic response for which prevention of ADEs in patients not expected to benefit from treatment was stated as a motivation for model development. 18 (27%) of 67 studies addressed this use case [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] and focused on antineoplastics to treat patients with cancer (four [22%] of 18 studies), or antivirals with or without immuno modulators to treat patients with HIV or hepatitis C (five [28%]). Eight (44%) of 18 studies evaluated a single AI model.…”

“…Eight (44%) of 18 studies evaluated a single AI model. 47,49,51,[56][57][58]60,61 The remaining studies compared multiple models; use of support vector machines or tree-based algorithms generally resulted in the most favourable performance.…”

Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol

“…However, these integrated data sources are characterized by high volume and variation, and there are several data analytic challenges in the integrated data structure, including mismatched time scales and multilevel risk predictors. The recent developments in Big Data analytics, such as artificial neural network [ 50 , 51 ], LSTM Neural Network, random forest [ 51 , 52 ], support vector machine [ 51 ], and deep learning approach such as CNN [ 53 ], make it feasible to address these methodological challenges and predict virologic outcomes using data from multiple domains.…”

La inteligencia artificial y sus aplicaciones en medicina II: importancia actual y aplicaciones prácticas

“… Muchas investigaciones biomédicas se ven claramente beneficiadas por la IA, ya que su aplicación genera una reducción en los costes y facilita la obtención y gestión de datos a través de modelos semánticos y relaciones entre variables desde una perspectiva diferente a la de la estadística clásica. La lógica difusa se está aplicando a investigaciones dentro del ámbito de la salud y está aportando nuevas visiones en el campo de creación de modelos de expansión de enfermedades infecciosas 45 , sistemas expertos para el diagnóstico 46 o respuestas a tratamientos 47 . …”