AimsAlthough　there　are　various　model-based　approaches　to　individualized　vancomycin　(VCM)　administration,　few　have　been　reported　for　adult　patients　with　periprosthetic　joint　infection　(PJI).　This　work　attempted　to　develop　a　machine　learning　(ML)-based　model　for　predicting　VCM　trough　concentration　in　adult　PJI　patients.MethodsThe　dataset　of　287　VCM　trough　concentrations　from　130　adult　PJI　patients　was　split　into　a　training　set　(229)　and　a　testing　set　(58)　at　a　ratio　of　8:2,　and　an　independent　external　32　concentrations　were　collected　as　a　validation　set.　A　total　of　13　covariates　and　the　target　variable　(VCM　trough　concentration)　were　included　in　the　dataset.　A　covariate　model　was　respectively　constructed　by　support　vector　regression,　random　forest　regression　and　gradient　boosted　regression　trees　and　interpreted　by　SHapley　Additive　exPlanation　(SHAP).ResultsThe　SHAP　plots　visualized　the　weight　of　the　covariates　in　the　models,　with　estimated　glomerular　filtration　rate　and　VCM　daily　dose　as　the　2　most　important　factors,　which　were　adopted　for　the　model　construction.　Random　forest　regression　was　the　optimal　ML　algorithm　with　a　relative　accuracy　of　82.8%　and　absolute　accuracy　of　67.2%　(R2　=.61,　mean　absolute　error　=　2.4,　mean　square　error　=　10.1),　and　its　prediction　performance　was　verified　in　the　validation　set.ConclusionThe　proposed　ML-based　model　can　satisfactorily　predict　the　VCM　trough　concentration　in　adult　PJI　patients.　Its　construction　can　be　facilitated　with　only　2　clinical　parameters　(estimated　glomerular　filtration　rate　and　VCM　daily　dose),　and　prediction　accuracy　can　be　rationalized　by　SHAP　values,　which　highlights　a　profound　practical　value　for　clinical　dosing　guidance　and　timely　treatment.