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Abstract:
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Many diseases can be prevented or treated if they are detected early or signaled before their occurrence. Disease early detection and prevention (DEDAP) is thus important with a great impact on health improvement of our society. Traditionally, people are encouraged to check their health conditions regularly so that readings of relevant medical indices can be compared with certain threshold values. One limitation of such traditional DEDAP methods is that they focus mainly on the data collected at the current time point and historical data are not fully used. Consequently, irregular longitudinal pattern of the medical indices could be neglected and certain diseases could be left undetected. In this paper, we suggest a novel and effective method for DEDAP. To detect a disease, a patient's risk to the disease is first quantified at each time point, and then the longitudinal pattern of the risk is monitored sequentially over time. A signal will be triggered by a large cumulative difference between the risk pattern of the patient under monitoring and the risk pattern of a typical person without the disease in concern.
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