Given　the　shortage　of　intensive　care　units　(ICUs)　due　to　the　coronavirus　disease　(COVID-19),　a　prediction　model　is　essential　in　ensuring　the　availability　of　ICU　beds.　However,　several　challenges,　such　as　the　importance　of　distinguishing　indicators,　efficiency　of　ICU　admission　records,　and　the　explainability　and　effectiveness　of　the　prediction　model,　hinder　the　effective　prediction　of　ICU　admissions.　To　mitigate　these　challenges,　an　explainable　decision　model　that　uses　the　extended　belief　rule-based　system　is　introduced　to　predict　ICU　admission.　First,　an　indicator　extraction　model　is　proposed　to　measure　the　importance　of　the　various　indicators　and　obtain　representative　indicators.　Second,　a　Charnes,　Cooper,　and　Rhodes　(CCR)　model　is　constructed　to　measure　the　efficiencies　of　the　belief　rules　to　achieve　the　compact　structure　of　an　extended　belief　rule　base.　Third,　a　new　extended　belief　rule-based　model,　optimized　by　parameter　optimization　and　domain　division-based　rule　reduction,　is　developed　to　predict　ICU　admission.　These　procedures　enable　the　explainable　decision　model　to　adapt　to　big　data　situation,　offer　explanations　and　realize　high　efficiency.　Finally,　a　case　study　of　ICU　admission　during　a　COVID-19　outbreak　is　conducted　to　demonstrate　the　implementation　and　effectiveness　of　the　proposed　model　in　a　comparative　analysis.