Overview

Dataset statistics

Number of variables9
Number of observations33
Missing cells29
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory82.0 B

Variable types

Categorical3
Numeric6

Dataset

Description2022년 한국보훈복지의료공단 부산보훈병원 만성질환자 연령별 분포 데이터 ※ 59세 이하 / 60대 / 70대 / 80대 / 90세 이상
URLhttps://www.data.go.kr/data/15102307/fileData.do

Alerts

상병코드 is highly overall correlated with 상병명칭High correlation
상병명칭 is highly overall correlated with 상병코드High correlation
59세이하 is highly overall correlated with 60세-64세 and 4 other fieldsHigh correlation
60세-64세 is highly overall correlated with 59세이하 and 4 other fieldsHigh correlation
65세-69세 is highly overall correlated with 59세이하 and 4 other fieldsHigh correlation
70세-79세 is highly overall correlated with 59세이하 and 4 other fieldsHigh correlation
80세-89세 is highly overall correlated with 59세이하 and 4 other fieldsHigh correlation
90세이상 is highly overall correlated with 59세이하 and 4 other fieldsHigh correlation
59세이하 has 6 (18.2%) missing valuesMissing
60세-64세 has 6 (18.2%) missing valuesMissing
65세-69세 has 6 (18.2%) missing valuesMissing
80세-89세 has 2 (6.1%) missing valuesMissing
90세이상 has 9 (27.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:26:09.401643
Analysis finished2023-12-12 22:26:12.965642
Duration3.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
입원(실인원)
11 
입원(연인원)
11 
외래
11 

Length

Max length7
Median length7
Mean length5.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row입원(실인원)
2nd row입원(실인원)
3rd row입원(실인원)
4th row입원(실인원)
5th row입원(실인원)

Common Values

ValueCountFrequency (%)
입원(실인원) 11
33.3%
입원(연인원) 11
33.3%
외래 11
33.3%

Length

2023-12-13T07:26:13.024974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:26:13.112294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
입원(실인원 11
33.3%
입원(연인원 11
33.3%
외래 11
33.3%

상병코드
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size396.0 B
I10~I13, I15
3 
E10~E14
3 
F00~F99, G40~G41
3 
A15, A16, A19
3 
I05~I09, I20~I27, I30~I52
3 
Other values (6)
18 

Length

Max length25
Median length17
Mean length12.636364
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI10~I13, I15
2nd rowE10~E14
3rd rowF00~F99, G40~G41
4th rowA15, A16, A19
5th rowI05~I09, I20~I27, I30~I52

Common Values

ValueCountFrequency (%)
I10~I13, I15 3
9.1%
E10~E14 3
9.1%
F00~F99, G40~G41 3
9.1%
A15, A16, A19 3
9.1%
I05~I09, I20~I27, I30~I52 3
9.1%
I60~I69 3
9.1%
G00~G37, G43~G83 3
9.1%
C00~C97, D00~D09 3
9.1%
E00~E07 3
9.1%
B18, B19, K70~K77 3
9.1%

Length

2023-12-13T07:26:13.205392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i10~i13 3
 
4.8%
i60~i69 3
 
4.8%
k70~k77 3
 
4.8%
b19 3
 
4.8%
b18 3
 
4.8%
e00~e07 3
 
4.8%
d00~d09 3
 
4.8%
c00~c97 3
 
4.8%
g43~g83 3
 
4.8%
g00~g37 3
 
4.8%
Other values (11) 33
52.4%

상병명칭
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size396.0 B
고혈압
3 
당뇨병
3 
정신 및 행동장애
3 
호흡기결핵
3 
심장질환
3 
Other values (6)
18 

Length

Max length9
Median length7
Mean length5.2727273
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고혈압
2nd row당뇨병
3rd row정신 및 행동장애
4th row호흡기결핵
5th row심장질환

Common Values

ValueCountFrequency (%)
고혈압 3
9.1%
당뇨병 3
9.1%
정신 및 행동장애 3
9.1%
호흡기결핵 3
9.1%
심장질환 3
9.1%
대뇌혈관질환 3
9.1%
신경계질환 3
9.1%
악성신생물 3
9.1%
갑상선의 장애 3
9.1%
간의 질환 3
9.1%

Length

2023-12-13T07:26:13.322615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고혈압 3
 
6.7%
당뇨병 3
 
6.7%
정신 3
 
6.7%
및 3
 
6.7%
행동장애 3
 
6.7%
호흡기결핵 3
 
6.7%
심장질환 3
 
6.7%
대뇌혈관질환 3
 
6.7%
신경계질환 3
 
6.7%
악성신생물 3
 
6.7%
Other values (5) 15
33.3%

59세이하
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)96.3%
Missing6
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean294.92593
Minimum1
Maximum1431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T07:26:13.428572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q115.5
median187
Q3459
95-th percentile850.7
Maximum1431
Range1430
Interquartile range (IQR)443.5

Descriptive statistics

Standard deviation352.99956
Coefficient of variation (CV)1.1969092
Kurtosis2.7196014
Mean294.92593
Median Absolute Deviation (MAD)182
Skewness1.535626
Sum7963
Variance124608.69
MonotonicityNot monotonic
2023-12-13T07:26:13.534389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 2
 
6.1%
18 1
 
3.0%
738 1
 
3.0%
716 1
 
3.0%
474 1
 
3.0%
253 1
 
3.0%
187 1
 
3.0%
566 1
 
3.0%
534 1
 
3.0%
361 1
 
3.0%
Other values (16) 16
48.5%
(Missing) 6
 
18.2%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
4 1
3.0%
5 2
6.1%
6 1
3.0%
15 1
3.0%
16 1
3.0%
18 1
3.0%
28 1
3.0%
53 1
3.0%
ValueCountFrequency (%)
1431 1
3.0%
899 1
3.0%
738 1
3.0%
716 1
3.0%
566 1
3.0%
534 1
3.0%
474 1
3.0%
444 1
3.0%
439 1
3.0%
405 1
3.0%

60세-64세
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)85.2%
Missing6
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean180.77778
Minimum1
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T07:26:13.670085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median40
Q3304.5
95-th percentile578.8
Maximum850
Range849
Interquartile range (IQR)296.5

Descriptive statistics

Standard deviation232.63827
Coefficient of variation (CV)1.2868743
Kurtosis1.1853899
Mean180.77778
Median Absolute Deviation (MAD)38
Skewness1.3487717
Sum4881
Variance54120.564
MonotonicityNot monotonic
2023-12-13T07:26:13.771503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 4
 
12.1%
13 2
 
6.1%
11 1
 
3.0%
850 1
 
3.0%
466 1
 
3.0%
250 1
 
3.0%
177 1
 
3.0%
144 1
 
3.0%
359 1
 
3.0%
379 1
 
3.0%
Other values (13) 13
39.4%
(Missing) 6
18.2%
ValueCountFrequency (%)
1 1
 
3.0%
2 4
12.1%
4 1
 
3.0%
5 1
 
3.0%
11 1
 
3.0%
13 2
6.1%
14 1
 
3.0%
18 1
 
3.0%
34 1
 
3.0%
40 1
 
3.0%
ValueCountFrequency (%)
850 1
3.0%
580 1
3.0%
576 1
3.0%
466 1
3.0%
438 1
3.0%
379 1
3.0%
359 1
3.0%
250 1
3.0%
229 1
3.0%
205 1
3.0%

65세-69세
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)88.9%
Missing6
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean310.77778
Minimum1
Maximum1507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T07:26:13.874515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q110
median158
Q3397.5
95-th percentile1102.6
Maximum1507
Range1506
Interquartile range (IQR)387.5

Descriptive statistics

Standard deviation410.93293
Coefficient of variation (CV)1.3222726
Kurtosis1.919129
Mean310.77778
Median Absolute Deviation (MAD)155
Skewness1.5950081
Sum8391
Variance168865.87
MonotonicityNot monotonic
2023-12-13T07:26:13.987157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 2
 
6.1%
368 2
 
6.1%
1 2
 
6.1%
1108 1
 
3.0%
589 1
 
3.0%
279 1
 
3.0%
427 1
 
3.0%
360 1
 
3.0%
971 1
 
3.0%
14 1
 
3.0%
Other values (14) 14
42.4%
(Missing) 6
18.2%
ValueCountFrequency (%)
1 2
6.1%
2 1
3.0%
3 2
6.1%
6 1
3.0%
7 1
3.0%
13 1
3.0%
14 1
3.0%
15 1
3.0%
17 1
3.0%
38 1
3.0%
ValueCountFrequency (%)
1507 1
3.0%
1108 1
3.0%
1090 1
3.0%
971 1
3.0%
589 1
3.0%
446 1
3.0%
427 1
3.0%
368 2
6.1%
360 1
3.0%
279 1
3.0%

70세-79세
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3821.0303
Minimum1
Maximum17862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T07:26:14.099663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q170
median999
Q36361
95-th percentile13816.6
Maximum17862
Range17861
Interquartile range (IQR)6291

Descriptive statistics

Standard deviation5063.3965
Coefficient of variation (CV)1.3251391
Kurtosis0.78171644
Mean3821.0303
Median Absolute Deviation (MAD)996
Skewness1.3046892
Sum126094
Variance25637984
MonotonicityNot monotonic
2023-12-13T07:26:14.217281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 2
 
6.1%
99 2
 
6.1%
1 2
 
6.1%
31 1
 
3.0%
8946 1
 
3.0%
10978 1
 
3.0%
17862 1
 
3.0%
13003 1
 
3.0%
78 1
 
3.0%
15037 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
1 2
6.1%
3 2
6.1%
31 1
3.0%
34 1
3.0%
37 1
3.0%
43 1
3.0%
70 1
3.0%
78 1
3.0%
99 2
6.1%
113 1
3.0%
ValueCountFrequency (%)
17862 1
3.0%
15037 1
3.0%
13003 1
3.0%
10978 1
3.0%
10550 1
3.0%
8946 1
3.0%
8872 1
3.0%
7441 1
3.0%
6361 1
3.0%
5506 1
3.0%

80세-89세
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)96.8%
Missing2
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean652.54839
Minimum1
Maximum2789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T07:26:14.354363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q115.5
median151
Q31261
95-th percentile2336.5
Maximum2789
Range2788
Interquartile range (IQR)1245.5

Descriptive statistics

Standard deviation855.53281
Coefficient of variation (CV)1.3110642
Kurtosis0.068562961
Mean652.54839
Median Absolute Deviation (MAD)145
Skewness1.1760496
Sum20229
Variance731936.39
MonotonicityNot monotonic
2023-12-13T07:26:14.457464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6 2
 
6.1%
14 1
 
3.0%
1635 1
 
3.0%
269 1
 
3.0%
230 1
 
3.0%
1481 1
 
3.0%
642 1
 
3.0%
1382 1
 
3.0%
2334 1
 
3.0%
17 1
 
3.0%
Other values (20) 20
60.6%
(Missing) 2
 
6.1%
ValueCountFrequency (%)
1 1
3.0%
3 1
3.0%
4 1
3.0%
6 2
6.1%
8 1
3.0%
12 1
3.0%
14 1
3.0%
17 1
3.0%
18 1
3.0%
74 1
3.0%
ValueCountFrequency (%)
2789 1
3.0%
2339 1
3.0%
2334 1
3.0%
1877 1
3.0%
1742 1
3.0%
1635 1
3.0%
1481 1
3.0%
1382 1
3.0%
1140 1
3.0%
1023 1
3.0%

90세이상
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)83.3%
Missing9
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean202.75
Minimum1
Maximum782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T07:26:14.570088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median76
Q3370
95-th percentile645.3
Maximum782
Range781
Interquartile range (IQR)365

Descriptive statistics

Standard deviation243.33717
Coefficient of variation (CV)1.2001833
Kurtosis-0.1110348
Mean202.75
Median Absolute Deviation (MAD)75
Skewness1.0527831
Sum4866
Variance59212.978
MonotonicityNot monotonic
2023-12-13T07:26:14.679861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 4
 
12.1%
5 2
 
6.1%
4 1
 
3.0%
782 1
 
3.0%
534 1
 
3.0%
19 1
 
3.0%
44 1
 
3.0%
241 1
 
3.0%
127 1
 
3.0%
309 1
 
3.0%
Other values (10) 10
30.3%
(Missing) 9
27.3%
ValueCountFrequency (%)
1 4
12.1%
4 1
 
3.0%
5 2
6.1%
19 1
 
3.0%
24 1
 
3.0%
41 1
 
3.0%
44 1
 
3.0%
64 1
 
3.0%
88 1
 
3.0%
127 1
 
3.0%
ValueCountFrequency (%)
782 1
3.0%
660 1
3.0%
562 1
3.0%
534 1
3.0%
449 1
3.0%
406 1
3.0%
358 1
3.0%
309 1
3.0%
241 1
3.0%
140 1
3.0%

Interactions

2023-12-13T07:26:12.074891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:09.724886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.419001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.810826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.224118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.661104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:12.166484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:09.804709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.482552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.879900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.295236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.727161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:12.237875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:09.878655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.537104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.943700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.358310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.784966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:12.344484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:09.966408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.602702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.016497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.425107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.850709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:12.438471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.051951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.684385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.087992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.504465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.921515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:12.514787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.121196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:10.744426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.150904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.581805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:11.991460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:26:14.783542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상병코드상병명칭59세이하60세-64세65세-69세70세-79세80세-89세90세이상
구분1.0000.0000.0000.4540.5760.6150.6190.6280.606
상병코드0.0001.0001.0000.5950.5100.0310.0000.3380.000
상병명칭0.0001.0001.0000.5950.5100.0310.0000.3380.000
59세이하0.4540.5950.5951.0000.8670.9130.9180.8330.899
60세-64세0.5760.5100.5100.8671.0000.9660.9030.7760.843
65세-69세0.6150.0310.0310.9130.9661.0000.9250.7770.899
70세-79세0.6190.0000.0000.9180.9030.9251.0000.9080.947
80세-89세0.6280.3380.3380.8330.7760.7770.9081.0000.945
90세이상0.6060.0000.0000.8990.8430.8990.9470.9451.000
2023-12-13T07:26:15.171573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상병코드구분상병명칭
상병코드1.0000.0001.000
구분0.0001.0000.000
상병명칭1.0000.0001.000
2023-12-13T07:26:15.283015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
59세이하60세-64세65세-69세70세-79세80세-89세90세이상구분상병코드상병명칭
59세이하1.0000.9610.9150.9000.9010.7250.2710.2750.275
60세-64세0.9611.0000.9510.9280.9300.7910.4170.2160.216
65세-69세0.9150.9511.0000.9050.9100.7950.4590.0000.000
70세-79세0.9000.9280.9051.0000.9690.8930.3970.0000.000
80세-89세0.9010.9300.9100.9691.0000.9170.2940.0950.095
90세이상0.7250.7910.7950.8930.9171.0000.2520.0000.000
구분0.2710.4170.4590.3970.2940.2521.0000.0000.000
상병코드0.2750.2160.0000.0000.0950.0000.0001.0001.000
상병명칭0.2750.2160.0000.0000.0950.0000.0001.0001.000

Missing values

2023-12-13T07:26:12.667795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:26:12.789444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T07:26:12.897207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분상병코드상병명칭59세이하60세-64세65세-69세70세-79세80세-89세90세이상
0입원(실인원)I10~I13, I15고혈압52231121
1입원(실인원)E10~E14당뇨병221708<NA>
2입원(실인원)F00~F99, G40~G41정신 및 행동장애<NA><NA><NA>331
3입원(실인원)A15, A16, A19호흡기결핵<NA>1<NA>3<NA><NA>
4입원(실인원)I05~I09, I20~I27, I30~I52심장질환18113866910741
5입원(실인원)I60~I69대뇌혈관질환55399184
6입원(실인원)G00~G37, G43~G83신경계질환6279961
7입원(실인원)C00~C97, D00~D09악성신생물2814172887424
8입원(실인원)E00~E07갑상선의 장애<NA><NA><NA>11<NA>
9입원(실인원)B18, B19, K70~K77간의 질환426374<NA>
구분상병코드상병명칭59세이하60세-64세65세-69세70세-79세80세-89세90세이상
23외래E10~E14당뇨병8998501507178622339358
24외래F00~F99, G40~G41정신 및 행동장애14315801090130032789782
25외래A15, A16, A19호흡기결핵1513147817<NA>
26외래I05~I09, I20~I27, I30~I52심장질환361438971150372334562
27외래I60~I69대뇌혈관질환53437936089461382309
28외래G00~G37, G43~G83신경계질환5663594276361642127
29외래C00~C97, D00~D09악성신생물187144368105501481241
30외래E00~E07갑상선의 장애253177279182323044
31외래B18, B19, K70~K77간의 질환474250368238626919
32외래N18만성신부전증71646658988721635534