Difficulty　in　knowledge　validation　is　a　significant　hindrance　to　knowledge　discovery　via　data　mining,　especially　automatic　validation　without　artificial　participation.　In　the　field　of　medical　research,　medical　knowledge　discovery　from　electronic　medical　records　is　a　common　medical　data　mining　method,　but　it　is　difficult　to　validate　the　discovered　medical　knowledge　without　the　participation　of　medical　experts.　In　this　article,　we　propose　a　data-driven　medical　knowledge　discovery　closed-loop　pipeline　based　on　interpretable　machine　learning　and　deep　learning;　the　components　of　the　pipeline　include　Data　Generator,　Medical　Knowledge　Mining,　Medical　Knowledge　Evaluation,　and　Medical　Knowledge　Application.　In　addition　to　completing　the　discovery　of　medical　knowledge,　the　pipeline　can　also　automatically　validate　the　knowledge.　We　apply　our　pipeline＇s　discovered　medical　knowledge　to　a　traditional　prognostic　predictive　model　of　heart　failure　in　a　real-world　study,　demonstrating　that　the　incorporation　of　medical　knowledge　can　effectively　improve　the　performance　of　the　traditional　model.　We　also　construct　a　scale　model　based　on　the　discovered　medical　knowledge　and　demonstrate　that　it　achieves　good　performance.　To　guarantee　its　medical　effectiveness,　every　process　of　our　pipeline　involves　the　participation　of　medical　experts.