Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study’s objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection. Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases’ Features to improve the reliability and fault-tolerant ability of the system. Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities’ responses could be reduced to 7.0–10.0 days. Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors’ logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.
【저자키워드】 Surveillance, medical diagnosis, knowledge representation, reasoning model, medical expert systems, unknown disease outbreaks, 【초록키워드】 COVID-19, pandemic, reliability, feasibility, knowledge, Infection, diagnostic, Diagnosis, Local, Transmission, Infectious disease, MERS, Severe acute respiratory syndrome, Outbreaks, COVID-19 outbreak, Accuracy, outbreak, response, Wuhan, disease outbreaks, Pandemics, Emerging infectious disease, respiratory, disease outbreak, disease, Middle East respiratory syndrome, Middle East, capture, knowledge bases, Logistic, acute respiratory syndrome, Clinical data, median time, COVID-19 case, transfer, illness onset, early response, bases, offer, representation, initial, IMPROVE, defined, shown, detect, reduced, the median, 【제목키워드】 disease outbreaks, Model, Reasoning, Unknown,