第六章 結論與討論
6.2 未來研究方向
根據本研究之結果，已完成整個看診流程之前半段，圖6-1 之模式則作 為未來推展至整各看診時間預測之流程，其最後結果將以病患就診之屬性，
加上等候理論以決定病患之就診序號及預估看診時間。
圖6-1 病患就診預約時間預測模式 候線，在此根據等候管制規則（queue discipline），先後接受服務設施（service facility）所提供的服務，服務完畢後即離開等候系統(作業研究，2001)。以一
HIS Database
N N-1
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附 錄
附錄一
D63 醫師以 Index Mining 軟體所得到 EM 之結果以下為取D63 醫師以 Index Mining 軟體所得到 EM 之結果。
=== Run information ===
Scheme: EM -I 100 -N 5 -S 100 -M 1.0E-6 Relation: QueryResult
Instances: 1179 Attributes: 7 pt_type date shift dr_v_time age sex
disease_code
Test mode: evaluate on training data
=== Clustering model (full training set) ===
EM
==
Number of clusters: 5
Cluster: 0 Prior probability: 0.3387 Attribute: pt_type
Discrete Estimator. Counts = | 3: 207.73 | 2: 178.73 | 1: 11.86 (Total = 398.31) Attribute: date
Discrete Estimator. Counts = | 6: 0 | 3: 0 | 5: 0 | 1: 0 | 4: 175.38 | 2: 222.92 | 7: 0 (Total = 398.31)
Attribute: shift
Discrete Estimator. Counts = | 1: 0.01 | 2: 0.01 | 3: 398.3 (Total = 398.31) Attribute: dr_v_time
Normal Distribution. Mean = 3.3477 StdDev = 1.7989 Attribute: age
Normal Distribution. Mean = 30.1084 StdDev = 17.4799 Attribute: sex
Discrete Estimator. Counts = | 1: 211.39 | 0: 186.93 (Total = 398.31) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 32.99 | 6809: 15.45 | 87340: 26.37 | 6819:
Cluster: 1 Prior probability: 0.2901 Attribute: pt_type
Discrete Estimator. Counts = | 3: 162.59 | 2: 154.18 | 1: 24.93 (Total = 341.7) Attribute: date
Discrete Estimator. Counts = | 6: 152.19 | 3: 139.13 | 5: 9.73 | 1: 0 | 4: 0 | 2: 0 | 7:
40.66 (Total = 341.7) Attribute: shift
Discrete Estimator. Counts = | 1: 341.67 | 2: 0.03 | 3: 0.01 (Total = 341.7) Attribute: dr_v_time
Normal Distribution. Mean = 3.6181 StdDev = 1.8787 Attribute: age
Normal Distribution. Mean = 36.0586 StdDev = 20.1089
Attribute: sex
Discrete Estimator. Counts = | 1: 159.64 | 0: 182.07 (Total = 341.7) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 22.97 | 6809: 14.94 | 87340: 16.85 | 6819: 7 | Cluster: 2 Prior probability: 0.0381 Attribute: pt_type
Discrete Estimator. Counts = | 3: 21.77 | 2: 23.29 | 1: 1.74 (Total = 46.8) Attribute: date
Discrete Estimator. Counts = | 6: 0.19 | 3: 0.25 | 5: 28.21 | 1: 17.86 | 4: 0.02 | 2: 0.03
| 7: 0.24 (Total = 46.8) Attribute: shift
Discrete Estimator. Counts = | 1: 5.05 | 2: 41.7 | 3: 0.05 (Total = 46.8) Attribute: dr_v_time
Normal Distribution. Mean = 11.865 StdDev = 4.0115 Attribute: age
Normal Distribution. Mean = 35.4439 StdDev = 17.8517
Attribute: sex
Discrete Estimator. Counts = | 1: 29.63 | 0: 17.17 (Total = 46.8) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 2.78 | 6809: 1.08 | 87340: 0.11 | 6819: 0.32 |
Cluster: 3 Prior probability: 0.271 Attribute: pt_type
Discrete Estimator. Counts = | 3: 146.53 | 2: 155.03 | 1: 16.27 (Total = 317.83) Attribute: date
Discrete Estimator. Counts = | 6: 0 | 3: 0 | 5: 126.69 | 1: 191.12 | 4: 0 | 2: 0 | 7: 0 (Total = 317.83)
Attribute: shift
Discrete Estimator. Counts = | 1: 0.59 | 2: 317.24 | 3: 0.01 (Total = 317.83) Attribute: dr_v_time
Normal Distribution. Mean = 3.8263 StdDev = 2.1145 Attribute: age
Normal Distribution. Mean = 33.0233 StdDev = 20.4843 Attribute: sex
Discrete Estimator. Counts = | 1: 163.96 | 0: 153.87 (Total = 317.83) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 28.2 | 6809: 10.93 | 87340: 21.91 | 6819: 3.68 |
Cluster: 4 Prior probability: 0.0621 Attribute: pt_type
Discrete Estimator. Counts = | 3: 48.38 | 2: 23.77 | 1: 2.19 (Total = 74.35) Attribute: date
Discrete Estimator. Counts = | 6: 14.62 | 3: 18.61 | 5: 0.37 | 1: 0.01 | 4: 20.59 | 2:
18.05 | 7: 2.09 (Total = 74.35) Attribute: shift
Discrete Estimator. Counts = | 1: 35.68 | 2: 0.02 | 3: 38.64 (Total = 74.35) Attribute: dr_v_time
Normal Distribution. Mean = 11.4713 StdDev = 3.2281
Attribute: age
Normal Distribution. Mean = 41.66 StdDev = 21.1914 Attribute: sex
Discrete Estimator. Counts = | 1: 49.38 | 0: 24.96 (Total = 74.35) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 1.06 | 6809: 3.61 | 87340: 3.76 | 6819: 0.06 |
=== Evaluation on training set ===
EM
==
Number of clusters: 5
Cluster: 0 Prior probability: 0.3387 Attribute: pt_type
Discrete Estimator. Counts = | 3: 207.73 | 2: 178.73 | 1: 11.86 (Total = 398.31) Attribute: date
Discrete Estimator. Counts = | 6: 0 | 3: 0 | 5: 0 | 1: 0 | 4: 175.38 | 2: 222.92 | 7: 0
(Total = 398.31) Attribute: shift
Discrete Estimator. Counts = | 1: 0.01 | 2: 0.01 | 3: 398.3 (Total = 398.31) Attribute: dr_v_time
Normal Distribution. Mean = 3.3477 StdDev = 1.7989 Attribute: age
Normal Distribution. Mean = 30.1084 StdDev = 17.4799 Attribute: sex
Discrete Estimator. Counts = | 1: 211.39 | 0: 186.93 (Total = 398.31) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 32.99 | 6809: 15.45 | 87340: 26.37 | 6819:
Cluster: 1 Prior probability: 0.2901 Attribute: pt_type
Discrete Estimator. Counts = | 3: 162.59 | 2: 154.18 | 1: 24.93 (Total = 341.7) Attribute: date
Discrete Estimator. Counts = | 6: 152.19 | 3: 139.13 | 5: 9.73 | 1: 0 | 4: 0 | 2: 0 | 7:
40.66 (Total = 341.7)
Attribute: shift
Discrete Estimator. Counts = | 1: 341.67 | 2: 0.03 | 3: 0.01 (Total = 341.7) Attribute: dr_v_time
Normal Distribution. Mean = 3.6181 StdDev = 1.8787 Attribute: age
Normal Distribution. Mean = 36.0586 StdDev = 20.1089 Attribute: sex
Discrete Estimator. Counts = | 1: 159.64 | 0: 182.07 (Total = 341.7) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 22.97 | 6809: 14.94 | 87340: 16.85 | 6819: 7 | Cluster: 2 Prior probability: 0.0381 Attribute: pt_type
Discrete Estimator. Counts = | 3: 21.77 | 2: 23.29 | 1: 1.74 (Total = 46.8) Attribute: date
Discrete Estimator. Counts = | 6: 0.19 | 3: 0.25 | 5: 28.21 | 1: 17.86 | 4: 0.02 | 2: 0.03
| 7: 0.24 (Total = 46.8)
Attribute: shift
Discrete Estimator. Counts = | 1: 5.05 | 2: 41.7 | 3: 0.05 (Total = 46.8) Attribute: dr_v_time
Normal Distribution. Mean = 11.865 StdDev = 4.0115 Attribute: age
Normal Distribution. Mean = 35.4439 StdDev = 17.8517 Attribute: sex
Discrete Estimator. Counts = | 1: 29.63 | 0: 17.17 (Total = 46.8) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 2.78 | 6809: 1.08 | 87340: 0.11 | 6819: 0.32 |
Cluster: 3 Prior probability: 0.271 Attribute: pt_type
Discrete Estimator. Counts = | 3: 146.53 | 2: 155.03 | 1: 16.27 (Total = 317.83) Attribute: date
Discrete Estimator. Counts = | 6: 0 | 3: 0 | 5: 126.69 | 1: 191.12 | 4: 0 | 2: 0 | 7: 0
(Total = 317.83) Attribute: shift
Discrete Estimator. Counts = | 1: 0.59 | 2: 317.24 | 3: 0.01 (Total = 317.83) Attribute: dr_v_time
Normal Distribution. Mean = 3.8263 StdDev = 2.1145 Attribute: age
Normal Distribution. Mean = 33.0233 StdDev = 20.4843 Attribute: sex
Discrete Estimator. Counts = | 1: 163.96 | 0: 153.87 (Total = 317.83) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 28.2 | 6809: 10.93 | 87340: 21.91 | 6819: 3.68 |
Cluster: 4 Prior probability: 0.0621 Attribute: pt_type
Discrete Estimator. Counts = | 3: 48.38 | 2: 23.77 | 1: 2.19 (Total = 74.35) Attribute: date
Discrete Estimator. Counts = | 6: 14.62 | 3: 18.61 | 5: 0.37 | 1: 0.01 | 4: 20.59 | 2:
18.05 | 7: 2.09 (Total = 74.35)
Attribute: shift
Discrete Estimator. Counts = | 1: 35.68 | 2: 0.02 | 3: 38.64 (Total = 74.35) Attribute: dr_v_time
Normal Distribution. Mean = 11.4713 StdDev = 3.2281 Attribute: age
Normal Distribution. Mean = 41.66 StdDev = 21.1914 Attribute: sex
Discrete Estimator. Counts = | 1: 49.38 | 0: 24.96 (Total = 74.35) Attribute: disease_code
Discrete Estimator. Counts = | 8730: 1.06 | 6809: 3.61 | 87340: 3.76 | 6819: 0.06 | Clustered Instances
0 437 ( 37%) 1 382 ( 32%) 2 360 ( 31%) Log likelihood: -8.44279