A self-learning fuzzy discrete event system for HIV/AIDS treatment regimen selection

Supervised Learning of Multievent Transition Matrices in Fuzzy Discrete-Event Systems

In this article, supervised learning of fuzzy discrete-event systems (FDES) is investigated. A learning algorithm that performs supervised learning for multievent transition matrices of a sequence of fuzzy discrete events is derived. FDES can be used to describe a large class of practical systems that consist of fuzzy discrete states, fuzzy discrete events, and transitions among fuzzy discrete states via fuzzy discrete events. Because fuzzy discrete states, fuzzy discrete events, and fuzzy transitions are well defined in FDES, the FDES model is highly explainable, which is important in many applications, especially in biomedical applications. Based on this explainable model, the proposed learning algorithm can be used to learn events and event sequences in the model. Hence, it allows system developers to build an explainable model for a complex system based on the data available. Simulations using MATLAB are conducted to verify the effectiveness of the proposed algorithm.

Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

BACKGROUND

As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making.

PURPOSE

This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems.

METHODS

PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting.

RESULTS

The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts.

CONCLUSIONS

Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research.

OTHER

A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures.

Learning Fuzzy Automaton's Event Transition Matrix When Post-Event State Is Unknown

Compared to other system modeling techniques, the fuzzy discrete event systems (FDESs) methodology has the unique capability of modeling a class of event-driven systems as fuzzy automata with ambiguous state and event-invoked state transition. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton's event transition matrix using fuzzy states before and after the occurrence of fuzzy events. The post-event state was assumed to be readily available while the pre-event state was either directly available or estimatable through learning. This article is focused on algorithm development for learning the transition matrix in a different setting-when the pre-event state is available but the post-event state is not. We suppose the post-event state is described by a fuzzy set that is linked to a (physical) variable whose value is available. Stochastic-gradient-descent-based algorithms are developed that can learn the transition matrix plus the parameters of the fuzzy sets when the fuzzy sets are of the Gaussian type. Computer simulation results are presented to confirm the theoretical development.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

A Caputo-Fabrizio Fractional-Order Model of HIV/AIDS with a Treatment Compartment: Sensitivity Analysis and Optimal Control Strategies

Although most of the early research studies on fractional-order systems were based on the Caputo or Riemann–Liouville fractional-order derivatives, it has recently been proven that these methods have some drawbacks. For instance, kernels of these methods have a singularity that occurs at the endpoint of an interval of definition. Thus, to overcome this issue, several new definitions of fractional derivatives have been introduced. The Caputo–Fabrizio fractional order is one of these nonsingular definitions. This paper is concerned with the analyses and design of an optimal control strategy for a Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic. The Caputo–Fabrizio fractional-order model of HIV/AIDS is considered to prevent the singularity problem, which is a real concern in the modeling of real-world systems and phenomena. Firstly, in order to find out how the population of each compartment can be controlled, sensitivity analyses were conducted. Based on the sensitivity analyses, the most effective agents in disease transmission and prevalence were selected as control inputs. In this way, a modified Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic is proposed. By changing the contact rate of susceptible and infectious people, the atraumatic restorative treatment rate of the treated compartment individuals, and the sexual habits of susceptible people, optimal control was designed. Lastly, simulation results that demonstrate the appropriate performance of the Caputo–Fabrizio fractional-order model and proposed control scheme are illustrated.

Prediction and analysis of Corona Virus Disease 2019

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.

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

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.

Decision-making system for orthodontic treatment planning based on direct implementation of expertise knowledge

Development of the decision-making systems has been highly demanded to provide objective evidence for the decisions of experts, especially in medicine, and a variety of systems have been developed by means of the state-of-the-art technology. In orthodontics, there has been no objective criterion for the decisions of whether or not to perform one of the invasive treatments, tooth extraction. Therefore, the prediction system for the extraction-nonextraction decisions was developed by intuitive implementation of expertise knowledge in this study. The system was successfully optimized with respect to knowledge descriptions and an inference algorithm to provide the prediction accuracy of 90.5% and simulations of the decision-making process on the optimized model were performed to obtain the terse representation of the expertise knowledge elements that are assumed to affect the decision-making of experts.

State-feedback control of fuzzy discrete-event systems

In a 2002 paper, we combined fuzzy logic with discrete-event systems (DESs) and established an automaton model of fuzzy DESs (FDESs). The model can effectively represent deterministic uncertainties and vagueness, as well as human subjective observation and judgment inherent to many real-world problems, particularly those in biomedicine. We also investigated optimal control of FDESs and applied the results to optimize HIV/AIDS treatments for individual patients. Since then, other researchers have investigated supervisory control problems in FDESs, and several results have been obtained. These results are mostly derived by extending the traditional supervisory control of (crisp) DESs, which are string based. In this paper, we develop state-feedback control of FDESs that is different from the supervisory control extensions. We use state space to describe the system behaviors and use state feedback in control. Both disablement and enforcement are allowed. Furthermore, we study controllability based on the state space and prove that a controller exists if and only if the controlled system behavior is (state-based) controllable. We discuss various properties of the state-based controllability. Aside from novelty, the proposed new framework has the advantages of being able to address a wide range of practical problems that cannot be effectively dealt with by existing approaches. We use the diabetes treatment as an example to illustrate some key aspects of our theoretical results.