The　automatic　disease　diagnosis　utilizing　clinical　data　has　been　suffering　from　the　issues　of　feature　sparse　and　high　probability　of　missing　values.　Since　the　graph　neural　network　is　a　effective　tool　to　model　the　structural　information　and　infer　the　missing　values,　it　is　becoming　the　dominant　method　for　the　predictive　model　construction　from　electronic　medical　records.　Existing　graph　neural　network　based　solutions　usually　adopt　the　medical　concepts　(e.g.,　symptoms)　the　feature　representation　of　clinical　data　without　considering　their　underlying　semantic　relations.　The　limited　discriminative　capability　of　the　medical　concept　cannot　provide　sufficient　indicative　information　about　the　disease.　This　article　proposes　a　knowledge-guided　graph　attention　network　for　the　disease　prediction.　Beside　extracting　the　attribute-value　structure　as　a　large-size　medical　concept,　the　mutual　information　between　multiple　medical　concepts　mentioned　in　the　electronic　medical　records　are　taken　into　account　in　the　graph　construction.　Meanwhile,　the　defined　diseases　and　their　associations　with　the　medical　concepts　in　the　medical　knowledge　graph　are　incorporated　into　the　graph,　which　provides　the　potentials　to　enhance　the　indicative　impacts　of　the　medical　concepts　directly　related　to　a　target　disease.　Then,　the　spatial　and　attention　based　graph　encoders　are　employed　to　aggregate　information　from　directly　neighbor　nodes　to　generate　node　embeddings　as　the　compact　features　to　be　used　for　disease　diagnosis.　The　approach　itself　is　a　general　one　that　can　utilized　to　build　the　predictive　model　using　Chinese　EMRs　for　different　diseases.　The　empirical　experiments　for　its　performance　evaluation　are　conducted　on　the　real-world　COPD　EMR　dataset.　The　comparison　study　results　show　that　the　proposed　model　outperforms　baseline　methods,　which　illustrates　the　effectiveness　of　our　proposed　model.