Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis

Abstract

Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice. We conducted a literature search on PubMed, Google Scholar, and ScienceDirect, including papers published in English between January 2020 and April 2024. Search keywords included AI, ML, public health, surveillance, diagnosis, prognosis, and infectious disease, to identify published studies using AI and ML in infectious disease management. Studies without public datasets or lacking descriptions of the ML models were excluded. This review included a total of 77 studies applied in surveillance, prognosis, and diagnosis. Different types of input data from infectious disease surveillance, clinical diagnosis, and prognosis required different ML and AI models to achieve the maximum performance in infectious disease management. Our findings highlight the potential of Explainable AI and ensemble learning models to be more broadly applicable in different aspects of infectious disease management, which can be integrated in clinical workflows to improve infectious disease surveillance, diagnosis, and prognosis. Explainable AI and ensemble learning models can be suitably used to achieve high accuracy in prediction. However, as most of the studies have not been validated in different cohorts, it remains unclear whether these ML models can be broadly applicable to different populations. Nonetheless, the findings encourage deploying ML and AI to complement clinicians and augment clinical decision-making.

Keywords: artificial intelligence; diagnosis; infectious diseases management; machine learning; prognosis; surveillance.

Figure 1

Flow chart of search strategy employed to identify machine learning models used in infectious disease management. AI, Artificial Intelligence; ML, machine learning.

Figure 2

Overview of AI and ML techniques used in infectious disease management, classified into supervised learning, unsupervised learning, reinforcement learning, and Explainable AI.

Figure 3

Flowchart demonstrating the patient trajectory of a typical infectious disease profile. ML can be potentially employed in surveillance (process 1), diagnosis (process 2), and prognosis (process 3) to facilitate and accelerate these processes, which will be critical particularly during a virus pandemic or epidemic. Solid lines represent the shortened patient trajectory in response to an infectious disease pandemic, which can be facilitated with the implementation of ML or AI; dotted lines represent an ideal patient trajectory, and host of potential parameters can be leveraged by ML; red lines represent potential treatment burdens on the hospital infrastructure during a pandemic. FN, False Negative; TN, True Negative; FP, False Positive; TP, True Positive; ML, machine learning; CC, Chief Complaint; PCR, Polymerase Chain Reaction; EIDs, Emerging Infectious Diseases.

Figure 4

Summary of the most suitable ML models that can be used for infectious disease surveillance, diagnosis, and prognosis based on our literature review. Ensemble ML models demonstrate promise in multiple applications of infectious disease management. RNN-LSTM, Recurrent Neural Network–Long Short-Term Memory; SVM, Support Vector Machine; GBMs, Gradient Boosting Machines; XGBoost, eXtreme Gradient Boosting; RF, Random Forest; LR, Logistic Regression; DT, Decision Tree; KNN, k-Nearest Neighbor; SHAP, Shapley Additive exPlanations.

Figure 5

A possible workflow for clinical machine learning models in infectious diseases using information from this review.