The invention relates to the technical field of computers, discloses an internet-of-things-driven AIDS patient remote follow-up monitoring method and
system, and aims to solve the problems that in the prior art, multi-
source data fusion is insufficient, risk prediction precision is low, a model lacks self-adaptive ability, and intervention is disjointed from clinic. The method comprises the following steps: collecting physiological, medication and
environmental data in real time through an
Internet of Things terminal; cleaning, alignment and
feature extraction are carried out through an edge gateway; the method comprises the following steps: fusing an
electronic medical record at a cloud end, performing dynamic modeling by utilizing a
hybrid model of a
time sequence convolutional network and a long-short-
term memory network, and generating an individualized
health risk score through a cross-
modal attention mechanism; and matching the three-level intervention rule base based on the
score, and dynamically pushing follow-up or medical instructions. According to the technical scheme, high-precision risk early warning, minute-level
continuous monitoring, low-
delay response and personalized closed-loop intervention can be achieved, and the follow-up compliance rate and the clinical intervention efficiency are remarkably improved.