Overview

Dataset statistics

Number of variables9
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory82.0 B

Variable types

Categorical3
Numeric6

Dataset

Description대전보훈병원에서 개방하는 진료정보 데이터로, 대전보훈병원 만성질환 환자 연령별 현황에 대한 내용이 포함된 공공데이터입니다.
URLhttps://www.data.go.kr/data/15102125/fileData.do

Alerts

상병코드 is highly overall correlated with 상병명칭High correlation
상병명칭 is highly overall correlated with 상병코드High correlation
59이하 is highly overall correlated with 60-64 and 4 other fieldsHigh correlation
60-64 is highly overall correlated with 59이하 and 4 other fieldsHigh correlation
65-69 is highly overall correlated with 59이하 and 4 other fieldsHigh correlation
70-79 is highly overall correlated with 59이하 and 4 other fieldsHigh correlation
80-89 is highly overall correlated with 59이하 and 4 other fieldsHigh correlation
90이상 is highly overall correlated with 59이하 and 4 other fieldsHigh correlation
59이하 has 7 (21.2%) zerosZeros
60-64 has 6 (18.2%) zerosZeros
65-69 has 6 (18.2%) zerosZeros
70-79 has 2 (6.1%) zerosZeros
80-89 has 2 (6.1%) zerosZeros
90이상 has 4 (12.1%) zerosZeros

Reproduction

Analysis started2023-12-12 16:55:29.990462
Analysis finished2023-12-12 16:55:33.639959
Duration3.65 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

2023-12-13T01:55:33.731628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:55:33.854928image/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

2023-12-13T01:55:34.007561image/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

2023-12-13T01:55:34.202404image/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%

59이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.54545
Minimum0
Maximum1090
Zeros7
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T01:55:34.355968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median22
Q3399
95-th percentile974.4
Maximum1090
Range1090
Interquartile range (IQR)398

Descriptive statistics

Standard deviation378.11135
Coefficient of variation (CV)1.3772268
Kurtosis-0.52580816
Mean274.54545
Median Absolute Deviation (MAD)22
Skewness1.0536009
Sum9060
Variance142968.19
MonotonicityNot monotonic
2023-12-13T01:55:34.487108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 7
21.2%
1 2
 
6.1%
2 2
 
6.1%
22 1
 
3.0%
794 1
 
3.0%
746 1
 
3.0%
293 1
 
3.0%
399 1
 
3.0%
819 1
 
3.0%
379 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
0 7
21.2%
1 2
 
6.1%
2 2
 
6.1%
3 1
 
3.0%
4 1
 
3.0%
7 1
 
3.0%
10 1
 
3.0%
12 1
 
3.0%
22 1
 
3.0%
24 1
 
3.0%
ValueCountFrequency (%)
1090 1
3.0%
990 1
3.0%
964 1
3.0%
934 1
3.0%
819 1
3.0%
802 1
3.0%
794 1
3.0%
746 1
3.0%
399 1
3.0%
379 1
3.0%

60-64
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.15152
Minimum0
Maximum1039
Zeros6
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T01:55:34.624184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q3173
95-th percentile659.6
Maximum1039
Range1039
Interquartile range (IQR)171

Descriptive statistics

Standard deviation248.20243
Coefficient of variation (CV)1.7584114
Kurtosis5.1405307
Mean141.15152
Median Absolute Deviation (MAD)4
Skewness2.2648382
Sum4658
Variance61604.445
MonotonicityNot monotonic
2023-12-13T01:55:34.757429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
18.2%
3 6
18.2%
1 2
 
6.1%
2 2
 
6.1%
382 1
 
3.0%
600 1
 
3.0%
240 1
 
3.0%
130 1
 
3.0%
166 1
 
3.0%
173 1
 
3.0%
Other values (11) 11
33.3%
ValueCountFrequency (%)
0 6
18.2%
1 2
 
6.1%
2 2
 
6.1%
3 6
18.2%
4 1
 
3.0%
18 1
 
3.0%
21 1
 
3.0%
35 1
 
3.0%
44 1
 
3.0%
72 1
 
3.0%
ValueCountFrequency (%)
1039 1
3.0%
749 1
3.0%
600 1
3.0%
422 1
3.0%
382 1
3.0%
364 1
3.0%
240 1
3.0%
175 1
3.0%
173 1
3.0%
166 1
3.0%

65-69
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.69697
Minimum0
Maximum1515
Zeros6
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T01:55:34.893701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q3336
95-th percentile1051.2
Maximum1515
Range1515
Interquartile range (IQR)335

Descriptive statistics

Standard deviation387.17732
Coefficient of variation (CV)1.7079069
Kurtosis4.6320635
Mean226.69697
Median Absolute Deviation (MAD)11
Skewness2.1942716
Sum7481
Variance149906.28
MonotonicityNot monotonic
2023-12-13T01:55:35.066346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6
 
18.2%
1 3
 
9.1%
3 2
 
6.1%
10 1
 
3.0%
260 1
 
3.0%
386 1
 
3.0%
248 1
 
3.0%
336 1
 
3.0%
464 1
 
3.0%
840 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
0 6
18.2%
1 3
9.1%
2 1
 
3.0%
3 2
 
6.1%
4 1
 
3.0%
8 1
 
3.0%
9 1
 
3.0%
10 1
 
3.0%
11 1
 
3.0%
12 1
 
3.0%
ValueCountFrequency (%)
1515 1
3.0%
1368 1
3.0%
840 1
3.0%
760 1
3.0%
464 1
3.0%
443 1
3.0%
437 1
3.0%
386 1
3.0%
336 1
3.0%
260 1
3.0%

70-79
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2487.2121
Minimum0
Maximum14335
Zeros2
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T01:55:35.224214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q142
median334
Q33490
95-th percentile9938
Maximum14335
Range14335
Interquartile range (IQR)3448

Descriptive statistics

Standard deviation3664.8533
Coefficient of variation (CV)1.4734784
Kurtosis2.6074059
Mean2487.2121
Median Absolute Deviation (MAD)334
Skewness1.7436367
Sum82078
Variance13431149
MonotonicityNot monotonic
2023-12-13T01:55:35.378218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 2
 
6.1%
42 2
 
6.1%
1183 2
 
6.1%
39 1
 
3.0%
3490 1
 
3.0%
4510 1
 
3.0%
1698 1
 
3.0%
4186 1
 
3.0%
5638 1
 
3.0%
10085 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
0 2
6.1%
1 1
3.0%
3 1
3.0%
7 1
3.0%
27 1
3.0%
39 1
3.0%
42 2
6.1%
73 1
3.0%
82 1
3.0%
90 1
3.0%
ValueCountFrequency (%)
14335 1
3.0%
10085 1
3.0%
9840 1
3.0%
8567 1
3.0%
6409 1
3.0%
5638 1
3.0%
4510 1
3.0%
4186 1
3.0%
3490 1
3.0%
3153 1
3.0%

80-89
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean560.69697
Minimum0
Maximum2816
Zeros2
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T01:55:35.508573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q112
median88
Q31165
95-th percentile2020.8
Maximum2816
Range2816
Interquartile range (IQR)1153

Descriptive statistics

Standard deviation799.81215
Coefficient of variation (CV)1.4264606
Kurtosis0.69110108
Mean560.69697
Median Absolute Deviation (MAD)87
Skewness1.3409015
Sum18503
Variance639699.47
MonotonicityNot monotonic
2023-12-13T01:55:35.649772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 2
 
6.1%
0 2
 
6.1%
23 2
 
6.1%
4 1
 
3.0%
1165 1
 
3.0%
1768 1
 
3.0%
1869 1
 
3.0%
2816 1
 
3.0%
11 1
 
3.0%
788 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
0 2
6.1%
1 2
6.1%
4 1
3.0%
5 1
3.0%
7 1
3.0%
11 1
3.0%
12 1
3.0%
18 1
3.0%
22 1
3.0%
23 2
6.1%
ValueCountFrequency (%)
2816 1
3.0%
2112 1
3.0%
1960 1
3.0%
1869 1
3.0%
1768 1
3.0%
1409 1
3.0%
1351 1
3.0%
1225 1
3.0%
1165 1
3.0%
788 1
3.0%

90이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.66667
Minimum0
Maximum1034
Zeros4
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T01:55:36.090465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median20
Q3376
95-th percentile732
Maximum1034
Range1034
Interquartile range (IQR)374

Descriptive statistics

Standard deviation280.03054
Coefficient of variation (CV)1.4534457
Kurtosis1.2904779
Mean192.66667
Median Absolute Deviation (MAD)20
Skewness1.4335651
Sum6358
Variance78417.104
MonotonicityNot monotonic
2023-12-13T01:55:36.231412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 4
 
12.1%
2 3
 
9.1%
1 2
 
6.1%
6 2
 
6.1%
20 2
 
6.1%
3 1
 
3.0%
726 1
 
3.0%
575 1
 
3.0%
45 1
 
3.0%
376 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
0 4
12.1%
1 2
6.1%
2 3
9.1%
3 1
 
3.0%
6 2
6.1%
8 1
 
3.0%
10 1
 
3.0%
15 1
 
3.0%
18 1
 
3.0%
20 2
6.1%
ValueCountFrequency (%)
1034 1
3.0%
741 1
3.0%
726 1
3.0%
575 1
3.0%
562 1
3.0%
470 1
3.0%
447 1
3.0%
402 1
3.0%
376 1
3.0%
320 1
3.0%

Interactions

2023-12-13T01:55:32.807296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.288044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.719985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.144695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.668504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.270879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.891041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.351726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.786584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.228927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.746293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.366103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.971651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.415878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.853651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.305609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.828238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.463336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:33.108393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.493860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.924222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.411622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.918759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.561379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:33.211143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.581372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.998956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.507190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.091111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.649625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:33.299250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:30.655261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.075786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:31.595157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.178355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:32.730663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:55:36.332643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상병코드상병명칭59이하60-6465-6970-7980-8990이상
구분1.0000.0000.0000.5260.6330.8080.7710.3810.357
상병코드0.0001.0001.0000.4800.0000.0280.2520.0000.000
상병명칭0.0001.0001.0000.4800.0000.0280.2520.0000.000
59이하0.5260.4800.4801.0000.9420.7780.9020.8770.778
60-640.6330.0000.0000.9421.0000.8600.9470.9040.821
65-690.8080.0280.0280.7780.8601.0000.9220.8330.752
70-790.7710.2520.2520.9020.9470.9221.0000.8490.858
80-890.3810.0000.0000.8770.9040.8330.8491.0000.899
90이상0.3570.0000.0000.7780.8210.7520.8580.8991.000
2023-12-13T01:55:36.450577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상병코드상병명칭구분
상병코드1.0001.0000.000
상병명칭1.0001.0000.000
구분0.0000.0001.000
2023-12-13T01:55:36.548608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
59이하60-6465-6970-7980-8990이상구분상병코드상병명칭
59이하1.0000.9450.9330.8930.8880.7570.3500.2090.209
60-640.9451.0000.9540.9390.9280.8300.4550.0000.000
65-690.9330.9541.0000.9330.9030.7920.4620.0000.000
70-790.8930.9390.9331.0000.9740.8990.4180.0000.000
80-890.8880.9280.9030.9741.0000.9160.2250.0000.000
90이상0.7570.8300.7920.8990.9161.0000.2270.0000.000
구분0.3500.4550.4620.4180.2250.2271.0000.0000.000
상병코드0.2090.0000.0000.0000.0000.0000.0001.0001.000
상병명칭0.2090.0000.0000.0000.0000.0000.0001.0001.000

Missing values

2023-12-13T01:55:33.437835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:55:33.584223image/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.

Sample

구분상병코드상병명칭59이하60-6465-6970-7980-8990이상
0입원(실인원)I10~I13, I15고혈압1113943
1입원(실인원)E10~E14당뇨병10311172232
2입원(실인원)F00~F99, G40~G41정신 및 행동장애000311
3입원(실인원)A15, A16, A19호흡기결핵000000
4입원(실인원)I05~I09, I20~I27, I30~I52심장질환738110246
5입원(실인원)I60~I69대뇌혈관질환343732210
6입원(실인원)G00~G37, G43~G83신경계질환12224271
7입원(실인원)C00~C97, D00~D09악성신생물4721231807920
8입원(실인원)E00~E07갑상선의 장애000110
9입원(실인원)B18, B19, K70~K77간의 질환4242752
구분상병코드상병명칭59이하60-6465-6970-7980-8990이상
23외래E10~E14당뇨병109010391515143351869470
24외래F00~F99, G40~G41정신 및 행동장애990382760856728161034
25외래A15, A16, A19호흡기결핵01342112
26외래I05~I09, I20~I27, I30~I52심장질환19117544364091165402
27외래I60~I69대뇌혈관질환379422840100852112562
28외래G00~G37, G43~G83신경계질환8191734645638788251
29외래C00~C97, D00~D09악성신생물39916633641861409376
30외래E00~E07갑상선의 장애293130248118314720
31외래B18, B19, K70~K77간의 질환746240386169821445
32외래N18만성신부전증79460026045101351575