Sodium　homeostasis　disorder　is　one　of　the　most　common　abnormal　symptoms　of　elderly　patients　in　intensive　care　unit　(ICU),　which　may　lead　to　physiological　disorders　of　many　organs.　The　current　prediction　of　serum　sodium　in　ICU　is　mainly　based　on　the　subjective　judgment　of　doc-tors　＇　experience.　This　study　aims　at　this　problem　by　studying　the　clinical　retrospective　electronic　medical　record　data　of　ICU　to　establish　a　machine　learning　model　to　predict　the　short-term　serum　sodium　value　of　ICU　patients.　The　data　set　used　in　this　study　is　the　open-source　intensive　care　medical　information　set　Medical　Information　Mart　for　Intensive　Care　(MIMIC)-IV.　The　time　point　of　serum　sodium　detection　was　selected　from　the　ICU　clinical　records,　and　the　ICU　records　of　25　risk　factors　related　to　serum　sodium　were　extracted　from　the　patients　within　the　first　12　h　for　sta-tistical　analysis.　A　prediction　model　of　serum　sodium　value　within　48　h　was　established　using　a　feedforward　neural　network,　and　compared　with　previous　methods.　Our　research　results　show　that　the　neural　network　learning　model　can　predict　the　development　of　serum　sodium　in　patients　using　physiological　indicators　recorded　in　clinical　electronic　medical　records　within　12　h,　and　has　better　prediction　effect　than　the　serum　sodium　formula　and　other　machine　learning　models.