The　outbreak　of　a　new　major　infectious　disease　often　has　a　severe　impact　on　human　health　and　society.　Developing　appropriate　treatments　is　a　societal　concern.　A　case-based　decision　making　model　can　potentially　generate　therapy　alternatives,　but　has　also　revealed　two　challenges,　specifically,　ensuring　precise　case　similarity　measurement　and　reasonable　selection　of　the　most　suitable　historical　cases.　Hence,　a　decision-making　model　with　similarity　measurement　for　case　selection　is　introduced　to　generate　treatments.　First,　case　information　is　extended　to　probabilistic　hesitant　fuzzy　sets　with　uncertainty,　and　case　similarity　measurement　based　on　an　interval　evidential　reasoning　approach　is　developed,　which　not　only　handles　heterogeneous　information　effectively　but　also　guarantees　accurate　retrieval　results.　Then,　an　attribute　determination　model　is　constructed　from　many　factors　to　improve　the　rationality　of　the　results　and　enable　interpretability.　Furthermore,　an　improved　gained　and　lost　dominance　score　method　is　used　to　determine　the　most　suitable　historical　case　and　an　alternative　target　case　is　produced,　which　ensures　the　fairness　and　rationality　of　the　decision　results.　Finally,　a　case　study　involving　generation　of　therapy　alternatives　for　patients　with　mild　COVID-19　is　provided　to　demonstrate　the　procedure　employed　in　the　proposed　method　and　comparative　analysis　is　conducted　to　verify　its　advantages.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2024.