With　the　development　of　the　Mobile　Healthcare　Monitoring　Network　(MHMN),　patients＇　data　collected　by　body　sensors　not　only　allows　patients　to　monitor　their　health　or　make　online　pre-diagnosis　but　also　enables　clinicians　to　make　proper　decisions　by　utilizing　data　mining　techniques.　In　MHMN,　patients＇　personal　data　are　collected　by　sensors　per　second　and　uploaded　to　the　cloud　server　as　multi-dimension　vectors,　cloud　server　stores　the　personal　data　as　well　as　sends　monitoring　information　to　the　hospital　when　the　real-time　data　is　abnormal.　Hospital　users　(i.e.,　doctors,　etc.)　may　query　some　samples　which　contain　certain　textual　keywords　or　digital　keywords　in　certain　ranges　for　disease　diagnosis　or　medical　research.　For　example,　a　certain　hospital　user　may　query　all　samples　with　textual　keywords　＇cancer;　diabetes＇　and　digital　vectors　＇age＇　∈　[30,　50],　＇blood　sugar＇　∈　[4,　8],　＇heart　rhythm＇　∈　[70,　80].　Besides,　the　potential　value　of　massive　medical　data　has　attracted　considerable　interests　recently,　for　example,　valuable　results　in　diagnosis　model　can　be　yield　with　large-scale　aggregation　analysis　of　personal　medical　data.　The　cloud　server　can　build　a　diagnosis　model　using　data　mining　technology　over　massive　data,　so　that　hospital　users　or　pre-diagnosis　users　upload　medical　data　(i.e.,　age,　blood　pressure,　blood　sugar,　etc.)　to　the　cloud　for　diagnosis.　©　2021　IEEE.