Overview

Dataset statistics

Number of variables19
Number of observations3407
Missing cells19914
Missing cells (%)30.8%
Duplicate rows13
Duplicate rows (%)0.4%
Total size in memory532.5 KiB
Average record size in memory160.0 B

Variable types

DateTime2
Categorical6
Boolean2
Text2
Numeric7

Dataset

Description인천보훈병원에서 제공하는 영양검색을 통해서 환자의 올바른 식사 처방에 필요한 단백질, 콜레스테롤, BMI, TLC, 진단명 등 구성되어 있습니다.
URLhttps://www.data.go.kr/data/15117998/fileData.do

Alerts

Dataset has 13 (0.4%) duplicate rowsDuplicates
단백질 is highly overall correlated with 표준체중율 and 2 other fieldsHigh correlation
신장 is highly overall correlated with 표준체중 and 1 other fieldsHigh correlation
현재체중 is highly overall correlated with 표준체중율 and 3 other fieldsHigh correlation
표준체중 is highly overall correlated with 신장 and 1 other fieldsHigh correlation
표준체중율 is highly overall correlated with 단백질 and 4 other fieldsHigh correlation
열량 is highly overall correlated with 신장 and 1 other fieldsHigh correlation
열량계산 is highly overall correlated with 현재체중 and 3 other fieldsHigh correlation
진료과코드 is highly overall correlated with 열량체중구분 and 1 other fieldsHigh correlation
열량체중구분 is highly overall correlated with 단백질 and 4 other fieldsHigh correlation
단백질체중구분 is highly overall correlated with 단백질 and 4 other fieldsHigh correlation
결과구분 is highly imbalanced (56.1%)Imbalance
체중변화여부 is highly imbalanced (75.4%)Imbalance
단백질계산 is highly imbalanced (91.0%)Imbalance
현재식사처방 is highly imbalanced (92.9%)Imbalance
열량체중구분 is highly imbalanced (88.1%)Imbalance
단백질체중구분 is highly imbalanced (87.9%)Imbalance
소화장애여부 has 82 (2.4%) missing valuesMissing
신장 has 3303 (96.9%) missing valuesMissing
현재체중 has 3304 (97.0%) missing valuesMissing
표준체중 has 3303 (96.9%) missing valuesMissing
표준체중율 has 3304 (97.0%) missing valuesMissing
열량 has 3308 (97.1%) missing valuesMissing
열량계산 has 3310 (97.2%) missing valuesMissing
단백질 has 253 (7.4%) zerosZeros

Reproduction

Analysis started2023-12-12 06:05:46.313081
Analysis finished2023-12-12 06:05:53.355367
Duration7.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

입원일자
Date

Distinct585
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
Minimum2018-12-06 00:00:00
Maximum2022-12-29 00:00:00
2023-12-12T15:05:53.435166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:53.601465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

결과구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
양호
2503 
저위험군
779 
중위험군
 
104
고위험군
 
11
<NA>
 
10

Length

Max length4
Median length2
Mean length2.5306721
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양호
2nd row양호
3rd row저위험군
4th row양호
5th row양호

Common Values

ValueCountFrequency (%)
양호 2503
73.5%
저위험군 779
 
22.9%
중위험군 104
 
3.1%
고위험군 11
 
0.3%
<NA> 10
 
0.3%

Length

2023-12-12T15:05:53.742877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:05:53.858564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양호 2503
73.5%
저위험군 779
 
22.9%
중위험군 104
 
3.1%
고위험군 11
 
0.3%
na 10
 
0.3%

검색일자
Date

Distinct585
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
Minimum2018-12-07 00:00:00
Maximum2022-12-30 00:00:00
2023-12-12T15:05:53.975354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:54.135373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

체중변화여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
False
3268 
True
 
139
ValueCountFrequency (%)
False 3268
95.9%
True 139
 
4.1%
2023-12-12T15:05:54.242089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

소화장애여부
Boolean

MISSING 

Distinct2
Distinct (%)0.1%
Missing82
Missing (%)2.4%
Memory size6.8 KiB
False
2927 
True
398 
(Missing)
 
82
ValueCountFrequency (%)
False 2927
85.9%
True 398
 
11.7%
(Missing) 82
 
2.4%
2023-12-12T15:05:54.319825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

진료과코드
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
내과
741 
비뇨의학과
658 
정형외과
500 
안과
375 
신경외과
335 
Other values (7)
798 

Length

Max length5
Median length4
Mean length3.582624
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row내과
2nd row내과
3rd row신경외과
4th row치과
5th row정형외과

Common Values

ValueCountFrequency (%)
내과 741
21.7%
비뇨의학과 658
19.3%
정형외과 500
14.7%
안과 375
11.0%
신경외과 335
9.8%
소화기내과 269
 
7.9%
외과 136
 
4.0%
가정의학과 116
 
3.4%
재활의학과 102
 
3.0%
이비인후과 90
 
2.6%
Other values (2) 85
 
2.5%

Length

2023-12-12T15:05:54.414375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
내과 741
21.7%
비뇨의학과 658
19.3%
정형외과 500
14.7%
안과 375
11.0%
신경외과 335
9.8%
소화기내과 269
 
7.9%
외과 136
 
4.0%
가정의학과 116
 
3.4%
재활의학과 102
 
3.0%
이비인후과 90
 
2.6%
Other values (2) 85
 
2.5%

질병코드
Text

Distinct525
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
2023-12-12T15:05:55.019878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.2318756
Min length3

Characters and Unicode

Total characters14418
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)6.5%

Sample

1st rowK859
2nd rowK859
3rd rowS3350
4th rowK0532
5th rowM8199
ValueCountFrequency (%)
h0411 241
 
7.1%
e168 160
 
4.7%
k869 151
 
4.4%
z269 144
 
4.2%
n408 120
 
3.5%
r521 115
 
3.4%
k210 101
 
3.0%
e118 85
 
2.5%
k0532 78
 
2.3%
k859 77
 
2.3%
Other values (515) 2135
62.7%
2023-12-12T15:05:55.689686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1860
12.9%
0 1820
12.6%
9 1560
10.8%
2 1125
 
7.8%
8 1110
 
7.7%
6 932
 
6.5%
4 889
 
6.2%
5 750
 
5.2%
K 747
 
5.2%
3 585
 
4.1%
Other values (19) 3040
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11011
76.4%
Uppercase Letter 3407
 
23.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 747
21.9%
E 360
10.6%
H 359
10.5%
M 297
 
8.7%
R 264
 
7.7%
N 238
 
7.0%
I 199
 
5.8%
J 196
 
5.8%
Z 158
 
4.6%
G 104
 
3.1%
Other values (9) 485
14.2%
Decimal Number
ValueCountFrequency (%)
1 1860
16.9%
0 1820
16.5%
9 1560
14.2%
2 1125
10.2%
8 1110
10.1%
6 932
8.5%
4 889
8.1%
5 750
6.8%
3 585
 
5.3%
7 380
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11011
76.4%
Latin 3407
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 747
21.9%
E 360
10.6%
H 359
10.5%
M 297
 
8.7%
R 264
 
7.7%
N 238
 
7.0%
I 199
 
5.8%
J 196
 
5.8%
Z 158
 
4.6%
G 104
 
3.1%
Other values (9) 485
14.2%
Common
ValueCountFrequency (%)
1 1860
16.9%
0 1820
16.5%
9 1560
14.2%
2 1125
10.2%
8 1110
10.1%
6 932
8.5%
4 889
8.1%
5 750
6.8%
3 585
 
5.3%
7 380
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1860
12.9%
0 1820
12.6%
9 1560
10.8%
2 1125
 
7.8%
8 1110
 
7.7%
6 932
 
6.5%
4 889
 
6.2%
5 750
 
5.2%
K 747
 
5.2%
3 585
 
4.1%
Other values (19) 3040
21.1%

진단명
Text

Distinct635
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
2023-12-12T15:05:56.096537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length96
Median length78
Mean length28.098914
Min length2

Characters and Unicode

Total characters95733
Distinct characters172
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique301 ?
Unique (%)8.8%

Sample

1st rowpancreatitis
2nd rowpancreatitis
3rd rowSprain and strain of lumbar spine
4th rowchronic periodontitis
5th rowOsteoporosis, unspecified site unspecified
ValueCountFrequency (%)
unspecified 677
 
5.6%
other 605
 
5.0%
of 584
 
4.9%
disease 359
 
3.0%
pancreatic 302
 
2.5%
chronic 301
 
2.5%
syndrome 292
 
2.4%
and 284
 
2.4%
with 277
 
2.3%
eye 239
 
2.0%
Other values (760) 8068
67.3%
2023-12-12T15:05:56.651134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9712
 
10.1%
i 9673
 
10.1%
8582
 
9.0%
s 6512
 
6.8%
r 6421
 
6.7%
a 6296
 
6.6%
n 5964
 
6.2%
t 5812
 
6.1%
o 5396
 
5.6%
c 4460
 
4.7%
Other values (162) 26905
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81769
85.4%
Space Separator 8582
 
9.0%
Uppercase Letter 2771
 
2.9%
Other Letter 1126
 
1.2%
Other Punctuation 759
 
0.8%
Decimal Number 411
 
0.4%
Dash Punctuation 114
 
0.1%
Open Punctuation 96
 
0.1%
Close Punctuation 96
 
0.1%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
명 66
 
5.9%
상 57
 
5.1%
세 55
 
4.9%
불 54
 
4.8%
기 45
 
4.0%
타 43
 
3.8%
도 39
 
3.5%
성 37
 
3.3%
절 31
 
2.8%
염 30
 
2.7%
Other values (94) 669
59.4%
Lowercase Letter
ValueCountFrequency (%)
e 9712
11.9%
i 9673
11.8%
s 6512
 
8.0%
r 6421
 
7.9%
a 6296
 
7.7%
n 5964
 
7.3%
t 5812
 
7.1%
o 5396
 
6.6%
c 4460
 
5.5%
p 3517
 
4.3%
Other values (16) 18006
22.0%
Uppercase Letter
ValueCountFrequency (%)
D 464
16.7%
O 430
15.5%
C 324
11.7%
H 190
6.9%
M 177
 
6.4%
A 174
 
6.3%
G 173
 
6.2%
S 169
 
6.1%
W 122
 
4.4%
I 84
 
3.0%
Other values (10) 464
16.7%
Decimal Number
ValueCountFrequency (%)
2 154
37.5%
1 82
20.0%
0 71
17.3%
9 61
 
14.8%
5 19
 
4.6%
3 9
 
2.2%
6 6
 
1.5%
7 5
 
1.2%
4 4
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 689
90.8%
' 36
 
4.7%
. 24
 
3.2%
* 10
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 81
84.4%
[ 13
 
13.5%
［ 2
 
2.1%
Close Punctuation
ValueCountFrequency (%)
) 81
84.4%
] 13
 
13.5%
］ 2
 
2.1%
Space Separator
ValueCountFrequency (%)
8582
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84540
88.3%
Common 10067
 
10.5%
Hangul 1126
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
명 66
 
5.9%
상 57
 
5.1%
세 55
 
4.9%
불 54
 
4.8%
기 45
 
4.0%
타 43
 
3.8%
도 39
 
3.5%
성 37
 
3.3%
절 31
 
2.8%
염 30
 
2.7%
Other values (94) 669
59.4%
Latin
ValueCountFrequency (%)
e 9712
11.5%
i 9673
11.4%
s 6512
 
7.7%
r 6421
 
7.6%
a 6296
 
7.4%
n 5964
 
7.1%
t 5812
 
6.9%
o 5396
 
6.4%
c 4460
 
5.3%
p 3517
 
4.2%
Other values (36) 20777
24.6%
Common
ValueCountFrequency (%)
8582
85.2%
, 689
 
6.8%
2 154
 
1.5%
- 114
 
1.1%
1 82
 
0.8%
( 81
 
0.8%
) 81
 
0.8%
0 71
 
0.7%
9 61
 
0.6%
' 36
 
0.4%
Other values (12) 116
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94603
98.8%
Hangul 1126
 
1.2%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9712
 
10.3%
i 9673
 
10.2%
8582
 
9.1%
s 6512
 
6.9%
r 6421
 
6.8%
a 6296
 
6.7%
n 5964
 
6.3%
t 5812
 
6.1%
o 5396
 
5.7%
c 4460
 
4.7%
Other values (56) 25775
27.2%
Hangul
ValueCountFrequency (%)
명 66
 
5.9%
상 57
 
5.1%
세 55
 
4.9%
불 54
 
4.8%
기 45
 
4.0%
타 43
 
3.8%
도 39
 
3.5%
성 37
 
3.3%
절 31
 
2.8%
염 30
 
2.7%
Other values (94) 669
59.4%
None
ValueCountFrequency (%)
］ 2
50.0%
［ 2
50.0%

단백질
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9420605
Minimum0
Maximum5.4
Zeros253
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:56.799683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.9
median4.3
Q34.5
95-th percentile4.9
Maximum5.4
Range5.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.2020953
Coefficient of variation (CV)0.30494086
Kurtosis5.7059168
Mean3.9420605
Median Absolute Deviation (MAD)0.3
Skewness-2.5508378
Sum13430.6
Variance1.4450331
MonotonicityNot monotonic
2023-12-12T15:05:56.943020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4.5 332
 
9.7%
4.4 325
 
9.5%
4.3 323
 
9.5%
4.2 294
 
8.6%
4.6 286
 
8.4%
0.0 253
 
7.4%
4.1 216
 
6.3%
4.7 207
 
6.1%
4.0 160
 
4.7%
3.8 145
 
4.3%
Other values (28) 866
25.4%
ValueCountFrequency (%)
0.0 253
7.4%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2.0 1
 
< 0.1%
2.1 1
 
< 0.1%
2.2 1
 
< 0.1%
2.3 1
 
< 0.1%
2.4 2
 
0.1%
2.5 4
 
0.1%
2.6 5
 
0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.3 4
 
0.1%
5.2 7
 
0.2%
5.1 29
 
0.9%
5.0 55
 
1.6%
4.9 106
 
3.1%
4.8 145
4.3%
4.7 207
6.1%
4.6 286
8.4%
4.5 332
9.7%

신장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)58.7%
Missing3303
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean161.91154
Minimum140.5
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:57.074937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum140.5
5-th percentile148.93
Q1158
median163.55
Q3167
95-th percentile171
Maximum180
Range39.5
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.5309369
Coefficient of variation (CV)0.046512664
Kurtosis0.12425246
Mean161.91154
Median Absolute Deviation (MAD)3.75
Skewness-0.61071021
Sum16838.8
Variance56.715011
MonotonicityNot monotonic
2023-12-12T15:05:57.245889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160.0 5
 
0.1%
170.0 5
 
0.1%
164.0 5
 
0.1%
162.0 5
 
0.1%
152.0 4
 
0.1%
167.0 4
 
0.1%
163.4 3
 
0.1%
165.6 3
 
0.1%
156.0 3
 
0.1%
166.0 3
 
0.1%
Other values (51) 64
 
1.9%
(Missing) 3303
96.9%
ValueCountFrequency (%)
140.5 1
 
< 0.1%
144.6 3
0.1%
148.0 1
 
< 0.1%
148.9 1
 
< 0.1%
149.1 1
 
< 0.1%
150.0 2
0.1%
150.4 2
0.1%
150.6 1
 
< 0.1%
151.4 1
 
< 0.1%
152.0 4
0.1%
ValueCountFrequency (%)
180.0 1
 
< 0.1%
177.0 1
 
< 0.1%
173.3 1
 
< 0.1%
173.2 1
 
< 0.1%
172.0 1
 
< 0.1%
171.0 2
 
0.1%
170.0 5
0.1%
169.7 1
 
< 0.1%
169.0 2
 
0.1%
168.7 1
 
< 0.1%

현재체중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)76.7%
Missing3304
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean49.38301
Minimum30.6
Maximum87.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:57.382525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.6
5-th percentile32.9
Q142.4
median47.9
Q353
95-th percentile74.06
Maximum87.4
Range56.8
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation11.457076
Coefficient of variation (CV)0.23200441
Kurtosis1.3019703
Mean49.38301
Median Absolute Deviation (MAD)5.25
Skewness1.0418511
Sum5086.45
Variance131.26459
MonotonicityNot monotonic
2023-12-12T15:05:57.538359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0 5
 
0.1%
51.0 5
 
0.1%
41.8 3
 
0.1%
48.2 3
 
0.1%
43.0 3
 
0.1%
32.9 2
 
0.1%
30.6 2
 
0.1%
48.0 2
 
0.1%
45.4 2
 
0.1%
40.0 2
 
0.1%
Other values (69) 74
 
2.2%
(Missing) 3304
97.0%
ValueCountFrequency (%)
30.6 2
0.1%
30.9 1
< 0.1%
31.0 1
< 0.1%
31.8 1
< 0.1%
32.9 2
0.1%
33.0 1
< 0.1%
34.0 1
< 0.1%
36.3 1
< 0.1%
37.0 1
< 0.1%
37.9 1
< 0.1%
ValueCountFrequency (%)
87.4 1
< 0.1%
83.0 1
< 0.1%
77.6 1
< 0.1%
76.0 1
< 0.1%
75.6 1
< 0.1%
74.4 1
< 0.1%
71.0 1
< 0.1%
68.5 1
< 0.1%
67.8 1
< 0.1%
67.4 1
< 0.1%

표준체중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)55.8%
Missing3303
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean57.441346
Minimum41.5
Maximum71.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:57.709851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.5
5-th percentile46.69
Q154.9
median58.75
Q361.4
95-th percentile64.3
Maximum71.3
Range29.8
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation5.8546904
Coefficient of variation (CV)0.10192467
Kurtosis0.1354231
Mean57.441346
Median Absolute Deviation (MAD)2.8
Skewness-0.68006173
Sum5973.9
Variance34.2774
MonotonicityNot monotonic
2023-12-12T15:05:57.850376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.4 6
 
0.2%
57.7 5
 
0.1%
56.3 5
 
0.1%
63.6 5
 
0.1%
59.2 4
 
0.1%
60.6 4
 
0.1%
43.9 3
 
0.1%
58.7 3
 
0.1%
61.1 3
 
0.1%
60.3 3
 
0.1%
Other values (48) 63
 
1.8%
(Missing) 3303
96.9%
ValueCountFrequency (%)
41.5 1
 
< 0.1%
43.9 3
0.1%
46.0 1
 
< 0.1%
46.6 1
 
< 0.1%
47.2 2
0.1%
47.5 2
0.1%
48.5 2
0.1%
48.9 1
 
< 0.1%
49.2 2
0.1%
49.9 1
 
< 0.1%
ValueCountFrequency (%)
71.3 1
 
< 0.1%
68.9 1
 
< 0.1%
66.1 1
 
< 0.1%
66.0 1
 
< 0.1%
65.1 1
 
< 0.1%
64.3 2
 
0.1%
63.6 5
0.1%
63.4 1
 
< 0.1%
62.8 2
 
0.1%
62.6 1
 
< 0.1%

표준체중율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)88.3%
Missing3304
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean87.104854
Minimum59.6
Maximum209.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:58.009488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.6
5-th percentile64.83
Q174.85
median80.2
Q394.4
95-th percentile127.01
Maximum209.9
Range150.3
Interquartile range (IQR)19.55

Descriptive statistics

Standard deviation21.782955
Coefficient of variation (CV)0.2500774
Kurtosis9.6404309
Mean87.104854
Median Absolute Deviation (MAD)8
Skewness2.4729728
Sum8971.8
Variance474.49713
MonotonicityNot monotonic
2023-12-12T15:05:58.173795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.8 2
 
0.1%
69.8 2
 
0.1%
77.3 2
 
0.1%
78.2 2
 
0.1%
73.3 2
 
0.1%
76.1 2
 
0.1%
74.9 2
 
0.1%
64.8 2
 
0.1%
78.9 2
 
0.1%
88.2 2
 
0.1%
Other values (81) 83
 
2.4%
(Missing) 3304
97.0%
ValueCountFrequency (%)
59.6 1
< 0.1%
62.5 1
< 0.1%
63.0 1
< 0.1%
63.2 1
< 0.1%
64.8 2
0.1%
65.1 1
< 0.1%
65.5 1
< 0.1%
67.1 1
< 0.1%
68.7 1
< 0.1%
69.3 1
< 0.1%
ValueCountFrequency (%)
209.9 1
< 0.1%
146.2 1
< 0.1%
144.6 1
< 0.1%
143.8 1
< 0.1%
129.0 1
< 0.1%
127.8 1
< 0.1%
119.9 1
< 0.1%
117.6 1
< 0.1%
116.7 1
< 0.1%
115.1 1
< 0.1%

열량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)74.7%
Missing3308
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean1726.8687
Minimum1198
Maximum2210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:58.329853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1198
5-th percentile1359.1
Q11607.5
median1776
Q31860
95-th percentile1955.7
Maximum2210
Range1012
Interquartile range (IQR)252.5

Descriptive statistics

Standard deviation201.44542
Coefficient of variation (CV)0.11665358
Kurtosis-0.080239967
Mean1726.8687
Median Absolute Deviation (MAD)118
Skewness-0.50586649
Sum170960
Variance40580.258
MonotonicityNot monotonic
2023-12-12T15:05:58.484468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1842 6
 
0.2%
1689 4
 
0.1%
1894 3
 
0.1%
1361 3
 
0.1%
1701 2
 
0.1%
1789 2
 
0.1%
1524 2
 
0.1%
1835 2
 
0.1%
1605 2
 
0.1%
1929 2
 
0.1%
Other values (64) 71
 
2.1%
(Missing) 3308
97.1%
ValueCountFrequency (%)
1198 1
 
< 0.1%
1286 1
 
< 0.1%
1305 1
 
< 0.1%
1342 2
0.1%
1361 3
0.1%
1380 1
 
< 0.1%
1406 1
 
< 0.1%
1425 2
0.1%
1455 1
 
< 0.1%
1463 1
 
< 0.1%
ValueCountFrequency (%)
2210 1
< 0.1%
2121 1
< 0.1%
2110 1
< 0.1%
2049 1
< 0.1%
1980 1
< 0.1%
1953 1
< 0.1%
1947 1
< 0.1%
1946 1
< 0.1%
1941 1
< 0.1%
1931 1
< 0.1%

열량계산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)8.2%
Missing3310
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean30.247423
Minimum20
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2023-12-12T15:05:58.613615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile28
Q130
median30
Q331
95-th percentile31.2
Maximum35
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6772431
Coefficient of variation (CV)0.055450776
Kurtosis15.658111
Mean30.247423
Median Absolute Deviation (MAD)1
Skewness-1.6859643
Sum2934
Variance2.8131443
MonotonicityNot monotonic
2023-12-12T15:05:58.713223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
30 48
 
1.4%
31 32
 
0.9%
28 7
 
0.2%
35 4
 
0.1%
29 3
 
0.1%
20 1
 
< 0.1%
32 1
 
< 0.1%
27 1
 
< 0.1%
(Missing) 3310
97.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
27 1
 
< 0.1%
28 7
 
0.2%
29 3
 
0.1%
30 48
1.4%
31 32
0.9%
32 1
 
< 0.1%
35 4
 
0.1%
ValueCountFrequency (%)
35 4
 
0.1%
32 1
 
< 0.1%
31 32
0.9%
30 48
1.4%
29 3
 
0.1%
28 7
 
0.2%
27 1
 
< 0.1%
20 1
 
< 0.1%

단백질계산
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
<NA>
3308 
1.2
 
67
1.0
 
16
0.8
 
6
0.9
 
6

Length

Max length4
Median length4
Mean length3.9709422
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3308
97.1%
1.2 67
 
2.0%
1.0 16
 
0.5%
0.8 6
 
0.2%
0.9 6
 
0.2%
1.1 4
 
0.1%

Length

2023-12-12T15:05:58.850999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:05:59.006193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3308
97.1%
1.2 67
 
2.0%
1.0 16
 
0.5%
0.8 6
 
0.2%
0.9 6
 
0.2%
1.1 4
 
0.1%

현재식사처방
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
<NA>
3299 
일반상식
 
54
일반연식
 
24
금식
 
8
당뇨연식1800
 
6
Other values (10)
 
16

Length

Max length12
Median length4
Mean length4.0220135
Min length2

Unique

Unique7 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3299
96.8%
일반상식 54
 
1.6%
일반연식 24
 
0.7%
금식 8
 
0.2%
당뇨연식1800 6
 
0.2%
당뇨상식1800 5
 
0.1%
당뇨상식1600 2
 
0.1%
일반미음 2
 
0.1%
당뇨저염연식1800 1
 
< 0.1%
일반뉴케어RTH 400 1
 
< 0.1%
Other values (5) 5
 
0.1%

Length

2023-12-12T15:05:59.171861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3299
96.7%
일반상식 54
 
1.6%
일반연식 24
 
0.7%
금식 8
 
0.2%
당뇨연식1800 6
 
0.2%
당뇨상식1800 5
 
0.1%
2캔 3
 
0.1%
당뇨상식1600 2
 
0.1%
일반미음 2
 
0.1%
일반관급식용 1
 
< 0.1%
Other values (7) 7
 
0.2%

열량체중구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
<NA>
3310 
IBW(표준체중)
 
96
ABW(조정체중)
 
1

Length

Max length9
Median length4
Mean length4.142354
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3310
97.2%
IBW(표준체중) 96
 
2.8%
ABW(조정체중) 1
 
< 0.1%

Length

2023-12-12T15:05:59.298990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:05:59.425412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3310
97.2%
ibw(표준체중 96
 
2.8%
abw(조정체중 1
 
< 0.1%

단백질체중구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
<NA>
3308 
IBW(표준체중)
 
98
ABW(조정체중)
 
1

Length

Max length9
Median length4
Mean length4.1452891
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3308
97.1%
IBW(표준체중) 98
 
2.9%
ABW(조정체중) 1
 
< 0.1%

Length

2023-12-12T15:05:59.575368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:05:59.709916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3308
97.1%
ibw(표준체중 98
 
2.9%
abw(조정체중 1
 
< 0.1%

Interactions

2023-12-12T15:05:51.958280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:47.483621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.164437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.839408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.779664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.453640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.208115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:52.051053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:47.609615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.292964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.924115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.884946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.562106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.321324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:52.164699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:47.730226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.387464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.024449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.991087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.655290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.437632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:52.265736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:47.825939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.472473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.115297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.082419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.767500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.529079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:52.356299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:47.901308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.568638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.233544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.177180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.863889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.636220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:52.453505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:47.988622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.656925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.341801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.271268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.999397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.744119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:52.535434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.071661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:48.739048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:49.422644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:50.357541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.096339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:51.844232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:05:59.797049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결과구분체중변화여부소화장애여부진료과코드단백질신장현재체중표준체중표준체중율열량열량계산단백질계산현재식사처방열량체중구분단백질체중구분
결과구분1.0000.0930.2000.2490.5570.0000.2590.0000.2910.2670.2820.0000.0000.0000.000
체중변화여부0.0931.0000.2640.2180.0660.0000.0000.2490.0000.0000.0000.0000.3760.0000.000
소화장애여부0.2000.2641.0000.3510.1160.5120.0000.6170.0240.5750.0600.0790.1760.0000.000
진료과코드0.2490.2180.3511.0000.5720.1880.4300.0000.4240.0000.5140.0000.0000.8540.855
단백질0.5570.0660.1160.5721.0000.3190.6370.3770.6880.0000.5030.1940.5450.4950.496
신장0.0000.0000.5120.1880.3191.0000.5670.9690.1660.9260.0000.6160.0000.0000.000
현재체중0.2590.0000.0000.4300.6370.5671.0000.7540.8120.5360.7910.5510.0000.8320.833
표준체중0.0000.2490.6170.0000.3770.9690.7541.0000.2610.9250.0000.6750.0000.0000.000
표준체중율0.2910.0000.0240.4240.6880.1660.8120.2611.0000.1410.5140.2850.0000.4940.495
열량0.2670.0000.5750.0000.0000.9260.5360.9250.1411.0000.6080.3180.0000.0000.000
열량계산0.2820.0000.0600.5140.5030.0000.7910.0000.5140.6081.0000.1120.7281.0001.000
단백질계산0.0000.0000.0790.0000.1940.6160.5510.6750.2850.3180.1121.0000.3970.0000.000
현재식사처방0.0000.3760.1760.0000.5450.0000.0000.0000.0000.0000.7280.3971.0000.4100.398
열량체중구분0.0000.0000.0000.8540.4950.0000.8320.0000.4940.0001.0000.0000.4101.0000.692
단백질체중구분0.0000.0000.0000.8550.4960.0000.8330.0000.4950.0001.0000.0000.3980.6921.000
2023-12-12T15:05:59.975994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체중변화여부진료과코드단백질계산현재식사처방단백질체중구분열량체중구분결과구분소화장애여부
체중변화여부1.0000.1690.0000.2750.0000.0000.0620.170
진료과코드0.1691.0000.0000.0000.6540.6530.1170.272
단백질계산0.0000.0001.0000.2150.0000.0000.0000.093
현재식사처방0.2750.0000.2151.0000.3480.3730.0000.125
단백질체중구분0.0000.6540.0000.3481.0000.4870.0000.000
열량체중구분0.0000.6530.0000.3730.4871.0000.0000.000
결과구분0.0620.1170.0000.0000.0000.0001.0000.133
소화장애여부0.1700.2720.0930.1250.0000.0000.1331.000
2023-12-12T15:06:00.143581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단백질신장현재체중표준체중표준체중율열량열량계산결과구분체중변화여부소화장애여부진료과코드단백질계산현재식사처방열량체중구분단백질체중구분
단백질1.0000.043-0.4180.048-0.5040.1700.2460.2770.0490.0870.2830.1180.2460.5160.518
신장0.0431.0000.4210.9960.0210.764-0.1310.0000.0000.4890.0280.3940.0000.0000.000
현재체중-0.4180.4211.0000.4110.8700.165-0.5810.1510.0000.0000.2170.2510.0000.6360.637
표준체중0.0480.9960.4111.0000.0090.769-0.1200.0000.1810.4580.0000.3340.0000.0000.000
표준체중율-0.5040.0210.8700.0091.000-0.210-0.5720.1990.0000.0050.2410.1830.0000.5160.517
열량0.1700.7640.1650.769-0.2101.0000.2830.1610.0000.4290.0000.1370.1970.0000.000
열량계산0.246-0.131-0.581-0.120-0.5720.2831.0000.1090.0000.0000.3240.0000.4590.9790.979
결과구분0.2770.0000.1510.0000.1990.1610.1091.0000.0620.1330.1170.0000.0000.0000.000
체중변화여부0.0490.0000.0000.1810.0000.0000.0000.0621.0000.1700.1690.0000.2750.0000.000
소화장애여부0.0870.4890.0000.4580.0050.4290.0000.1330.1701.0000.2720.0930.1250.0000.000
진료과코드0.2830.0280.2170.0000.2410.0000.3240.1170.1690.2721.0000.0000.0000.6530.654
단백질계산0.1180.3940.2510.3340.1830.1370.0000.0000.0000.0930.0001.0000.2150.0000.000
현재식사처방0.2460.0000.0000.0000.0000.1970.4590.0000.2750.1250.0000.2151.0000.3730.348
열량체중구분0.5160.0000.6360.0000.5160.0000.9790.0000.0000.0000.6530.0000.3731.0000.487
단백질체중구분0.5180.0000.6370.0000.5170.0000.9790.0000.0000.0000.6540.0000.3480.4871.000

Missing values

2023-12-12T15:05:52.696470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:05:52.999302image/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.
2023-12-12T15:05:53.213475image/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

입원일자결과구분검색일자체중변화여부소화장애여부진료과코드질병코드진단명단백질신장현재체중표준체중표준체중율열량열량계산단백질계산현재식사처방열량체중구분단백질체중구분
02020-07-10양호2020-07-11NN내과K859pancreatitis4.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12020-07-10양호2020-07-11NY내과K859pancreatitis2.8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22020-07-10저위험군2020-07-11NN신경외과S3350Sprain and strain of lumbar spine4.2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32020-07-10양호2020-07-11NN치과K0532chronic periodontitis0.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
42020-07-10양호2020-07-11NN정형외과M8199Osteoporosis, unspecified site unspecified4.3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
52020-07-10양호2020-07-11NN신경외과M961postlaminectomy syndrome4.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
62020-07-10양호2020-07-11NN비뇨의학과N319Neuromuscular dysfunction of bladder, unspecified4.1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
72020-07-13양호2020-07-14NN소화기내과K859pancreatitis3.5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
82020-07-13양호2020-07-14NN내과E785Hyperlipidemia, unspecified4.4<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
92020-07-13양호2020-07-14NN외과K6423도 치핵4.4<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
입원일자결과구분검색일자체중변화여부소화장애여부진료과코드질병코드진단명단백질신장현재체중표준체중표준체중율열량열량계산단백질계산현재식사처방열량체중구분단백질체중구분
33972022-11-28양호2022-11-29NN외과B353Tinea pedis4.8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33982022-11-28양호2022-11-29NN비뇨의학과M06960경도 상세불명의 류마티스관절염, 아래다리3.8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33992022-11-29양호2022-11-30NN내과R558syncope4.8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34002022-11-29저위험군2022-11-30NN비뇨의학과N408Hyperplasia of prostate With other complication4.3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34012022-11-29양호2022-11-30NN신경외과M4806spinal stenosis, lumbar region4.4<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34022022-12-02중위험군2022-12-03NN비뇨의학과N200renal stone3.1156.053.553.5100.01605300.9일반상식IBW(표준체중)IBW(표준체중)
34032022-12-06양호2022-12-07NN비뇨의학과S134cervical sprain4.1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34042022-12-19양호2022-12-20YN외과L989scalp mass4.5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34052022-12-21양호2022-12-22NN내과I109Other and unspecified primary hypertension4.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34062022-12-21양호2022-12-22NN가정의학과E1448diabetic polyneuropathy4.5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

입원일자결과구분검색일자체중변화여부소화장애여부진료과코드질병코드진단명단백질신장현재체중표준체중표준체중율열량열량계산단백질계산현재식사처방열량체중구분단백질체중구분# duplicates
02020-06-24양호2020-06-25NN정형외과E168Other specified disorders of pancreatic internal secretion4.3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
12020-07-17양호2020-07-18NN치과K0532chronic periodontitis0.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
22020-08-21양호2020-08-22NN치과K0532chronic periodontitis0.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
32020-11-09양호2020-11-10NN안과H0411Dry eye syndrome4.6<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
42021-01-25양호2021-01-26NN안과H0411Dry eye syndrome4.3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
52021-04-26양호2021-04-27NN안과H0411Dry eye syndrome4.6<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
62021-07-07양호2021-07-08NN내과K869pancreatic disease4.2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
72021-07-19양호2021-07-20NN정형외과R521Chronic intractable pain4.2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
82021-07-28양호2021-07-29NN정형외과E168Other specified disorders of pancreatic internal secretion4.4<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
92021-08-18양호2021-08-19NN정형외과R521Chronic intractable pain3.8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2