In　biomedical　research　the　analysis　of　disease　prevalence　is　of　critical　importance.　While　most　of　the　existing　prevalence　studies　focus　on　individual　diseases,　there　has　been　increasing　effort　that　jointly　examines　the　prevalence　values　and　their　trends　of　multiple　diseases.　Such　joint　analysis　can　provide　valuable　insights　not　shared　by　individual　-disease　analysis.　A　critical　limitation　of　the　existing　analysis　is　that　there　is　a　lack　of　attention　to　existing　information,　which　has　been　accumulated　through　a　large　number　of　studies　and　can　be　valuable　especially　when　there　are　a　large　number　of　diseases　but　the　number　of　prevalence　values　for　a　specific　disease　is　limited.　In　this　study　we　conduct　the　functional　clustering　analysis　of　prevalence　trends　for　a　large　number　of　diseases.　A　novel　approach　based　on　the　penalized　fusion　technique　is　developed　to　incorporate　information　mined　from　published　articles.　It　is　innovatively　designed　to　take　into　account　that　such　information　may　not　be　fully　relevant　or　correct.　Another　significant　development　is　that　statistical　properties　are　rigorously　established.　Simulation　is　conducted　and　demonstrates　its　competitive　performance.　In　the　analysis　of　data　from　Taiwan　NHIRD　(National　Health　Insurance　Research　Database),　new　and　interesting　findings　that　differ　from　the　existing　ones　are　made.