Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing

Tran, Nam K.; Albahra, Samer; May, Larissa; Waldman, Sarah; Crabtree, Scott; Bainbridge, Scott; Rashidi, Hooman H.

doi:10.1093/clinchem/hvab239

Cited by 42 publications

(35 citation statements)

References 56 publications

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“…Other papers are concerned with several infectious diseases, but these reviews specified applications of digital technologies, artificial intelligence, and machine-learning in infectious disease laboratory testing to overcome human analytical limitations [13,14]. Other reviews focused on all kinds of infections and noninfectious diseases [15,16].…”

Section: Related Workmentioning

confidence: 99%

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

Alqaissi

Alotaibi

Ramzan

2022

Computational and Mathematical Methods in Medicine

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Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal. Moreover, heterogeneous data could ensure the model’s generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.

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Section: Related Workmentioning

confidence: 99%

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

Alqaissi

Alotaibi

Ramzan

2022

Computational and Mathematical Methods in Medicine

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“…In the process of rural informatization, a speech service system for agricultural information based on keyword speech recognition technology provides a convenient way to access agricultural information. Machine learning (ML) is currently the most popular artificial intelligence technique [ 42 ], aiming to improve the performance of its models by building learning behavior in machines through a software model that is trained over a large number of repetitions on sample data. It can often be used for prediction, classification, and clustering, and has applications in speech recognition, computer vision, and robot control, they can be applied at all stages of food production and supply.…”

Section: Background Of Food Safetymentioning

confidence: 99%

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Lei

Liu

et al. 2022

Foods

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Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made.

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“…Health, in particular, has benefited greatly from the adoption of AI-driven solutions [46] in many areas of clinical decision making [47]- [49] and infectious disease [50]. Despite some recent and promising results in hospital settings [3], [35], antibiotic prescribing and management is the exception, it can be argued that more work is needed in this area to apply the recent advances in machine learning and deep learning to tackle the AMR problem.…”

Section: Machine Learning and Amrmentioning

confidence: 99%

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

et al. 2022

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Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), where antibiotics use and prescription are poorly managed, is considered one of the main reasons for this problem. It is projected that the COVID-19 pandemic will accelerate the threat of AMR due to the increasing use of antibiotics across the world, and especially in countries with limited resources. In recent years, machine learning-based methods showed promising results and proved capable of providing the necessary tools to inform antimicrobial prescription and combat AMR. This timely paper provides a critical and technical review of existing machine learning-based methods for addressing AMR. First, an overview of the AMR problem as a global threat to public health, and its impact on countries with limited resources (LMICs) are presented. Then, a technical review and evaluation of existing literature that utilises machine learning to tackle AMR are provided with emphasis on methods that use readily available demographic and clinical data as well as microbial culture and sensitivity laboratory data of clinical isolates associated with multi-drug resistant infections. This is followed by a discussion of challenges and limitations that are considered barriers to scaling up the use of machine learning to address AMR. Finally, a framework for accelerating the use of AMR data-driven framework, and building a feasible solution that can be realistically implemented in LMICs is presented with a discussion of future directions and recommendations.

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Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing

Cited by 42 publications

References 56 publications

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

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