Microfluidic　technology　facilitates　high-throughput　generation　of　time　series　data　for　biological　and　medical　studies.　Deep　learning　enables　accurate,　predictive　analysis　and　proactive　decision-making　based　on　autonomous　recognition　of　intricate　pattern　hidden　in　series.　In　this　work,　we　first　devised　a　paper-based　microfluidic　system　for　portable　nucleic　acid　amplification　test　with　economic　energy　consumption.　Then,　we　employed　Graph　Neural　Network　(GNN),　distinguished　by　its　non-Euclidean　data　structure　tailored　for　deep　learning,　with　spatiotemporal　attention　mechanism　to　perform　near-sensor　predictive　analysis　of　the　on-chip　reaction.　Our　findings　demonstrated　that　the　novel　GNN　model　can　provide　accurate　predictions　of　positive　outcomes　at　the　early　stages　of　the　reaction　using　less　than　one-third　of　the　total　reaction　time.　Then,　the　deep　learning　model　trained　by　onchip　data　was　subsequently　applied　to　more　than　900　clinical　plots.　Generalization　of　the　GNN　model　was　successfully　validated　across　different　detection　methods,　diverse　types　of　datasets　and　time　series　with　variable　length.　Accuracy,　sensitivity　and　specificity　of　the　predictive　approach　were　96.5　%,　94.3　%　and　99.0　%　by　utilizing　the　early　half　of　reaction　information.　Finally,　we　compared　the　GNN　model　with　various　deep　learning　models.　Despite　differences　in　the　prediction　of　negative　samples　among　various　models　were　minute,　GNN　obviously　offered　overall　superior　performance.　This　work　ignites　a　cutting-edge　application　of　deep　learning　in　point-of-care　and　near-sensor　tests.　By　harnessing　the　power　of　body　area　networks　and　edge/fog　computing,　our　approach　unlocks　promising　possibilities　in　diverse　fields　like　healthcare　and　instrument　science.