In　the　past　decades,　ontology-based　query　expansion　has　been　studied　to　improve　health　and　biomedical　information　retrieval　by　many　researchers,　but　the　results　of　previous　works　are　inconsistent.　Query　expansion　with　domain　ontologies　could　introduce　noise　that　degrades　the　retrieval　performance,　therefore　noise　control　is　the　key　to　its　success.　In　this　paper,　we　explore　three　noise　control　strategies　for　UMLS-based　query　expansion.　The　first　strategy　is　the　adoption　of　a　word-phrase　hybrid　retrieval　model,　and　the　other　two　strategies　explored　are　expansion　term　weighting　and　term　filtering.　All　the　three　strategies　are　implemented　based　on　the　Indri　search　engine　and　evaluated　on　two　standard　datasets,　OHSUMED　and　TREC　Genomic　Track　2006.　The　experimental　results　indicate　that　the　word-phrase　hybrid　retrieval　model　is　superior　to　the　word-based　model　and　the　pure　phrase-based　model,　and　beneficial　to　not　only　baseline　retrieval　but　also　query　expansion.　Expansion　term　weighting　is　an　effective　strategy　to　suppress　term　noise　and　improve　retrieval　performance.　And　expansion　term　filtering　can　also　give　some　positive　effects　in　most　cases　but　is　not　as　effective　as　the　other　two　strategies.　By　combining　the　three　strategies　together,　the　best　retrieval　performances　can　be　achieved　on　both　datasets.　©　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2018.