NLPIR\/ICTCLA2018 ACADEMIC SEMINAR 6th ISSUE COMPLETED<\/span><\/span><\/b>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0
\nt\u000eC\u001b}\u0011Q\b[*K\u0012F<\/span><\/p>\n

\u00a0<\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0\u0014q$Z5M\fC\u000ey%\\\u000fq<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 Last week, Nada gave a presentation about the paper, Machine Learning Approaches for Early DRG Classification and Resource Allocation and shared some opinion on it.<\/span><\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0
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\nA\u000fo\u000fY\u000fB\u000f_,r\u0019R$S<\/span><\/p>\n

\u00a0<\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0\u000fO
\nK\u001dj,e\u0019@ `*P I&r$h<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 After the presentation, several questions were asked. The Q&As are listed as following:<\/span><\/span><\/span><\/p>\n

4?5G
\nt\u0007w\u001fK\u0014N0<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 Q:Do you think it is useful? Doctor may don’t believe the results.<\/span><\/span><\/span><\/p>\n

2P\u0004b&W4D2F\u0011O\u0002@\u0016i\u0004G0<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 A: it focuses on prediction of diagnosis-related groups (DRGs) and uses these predictions for allocating scarce hospital resources.<\/span><\/span><\/span><\/p>\n

\nF\u000fGH\u001eI\u0004O\u0004n;r4R3?:n,o0<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 Q:wha’s table 11 means?<\/span><\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0
\nF”a\u000eP\u0003r\u0018z\u0004\\,c<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 A:The results before admission are given in Table 11 and show that when using the DRG grouper, the \u00a0 \u00a0 \u00a0 \u00a0 deviations of the classified revenues are higher than using machine learning methods.<\/span><\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0*G\u0006M\fB\u001f}\u000fE<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 Q<\/span><\/span>\uff1a<\/span>can approches in this papper apply to chinese DRGs?<\/span><\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0\u0004^1L5x\u0013z.X
\nE1@<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 A: the DRGs in this paper is different from chinese DRGs, especially in ICD code. so it can’t apply to chineses DRGs.<\/span><\/span><\/span><\/p>\n

\u0005M\u0019B”S6e\u001cv7L0<\/span><\/p>\n

\u00a0<\/span><\/span><\/p>\n

\fn!v\u001aL\u0013r2z\u000fL\u001fK\u000fS L\u0004Q0<\/span><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 The powerpoint of the presentation can be download.<\/span><\/span><\/span><\/p>\n

\u001eE)V4\\\u0007H\u0001v#E\u0003L0<\/span><\/p>\n

\u00a0<\/span><\/span>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0$F6i#Q\u001fL\u0010[-O*{<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"