Home | Register | Login | Inquiries | Alerts | Sitemap |  


Advanced Search
JKM > Volume 41(2); 2020 > Article
Kim, Park, Choi, Lim, Ok, Noh, Song, Kang, Lee, and Kim: Application of Machine Learning to Predict Weight Loss in Overweight, and Obese Patients on Korean Medicine Weight Management Program

Abstract

Objectives

The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics.

Methods

From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting.

Results

Prediction accuracy was increased from 1st to 2nd and 3rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1st (54.69%), 2nd (73.52%), and 3rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1st (0.640), 2nd (0.816), and 3rd (0.939) prediction analysis based on reduced weight.

Conclusions

The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

Fig. 1
Flowchart of dataset for analysis
jkm-41-2-58f1.gif
Fig. 2
Data analytics lifecycle
jkm-41-2-58f2.gif
Fig. 3
A schematic diagram of prediction analyses of weight loss.
The analysis for predicting weight loss was divided into three parts, and the weight loss at each time point refers to the change from the initial point of treatment to the point of weight report.
jkm-41-2-58f3.gif
Fig. 4
Receiver operating characteristics (ROC) curves
jkm-41-2-58f4.gif
Table 1A
Independent Variables Used in the First Analysis (n=25,988)
Variables
Age (years) 36.34 ± 10.5

Weight (kg) 72.69 ± 10.50

BMI (kg/m2) 27.60 ± 3.04

Patients with medication dose change (n, %) Stable 21,590 (83.08)
Increased 4,398 (16.92)

Gender (n, %) Male 2,292 (8.82)
Female 23,696 (91.18)

Dietary habits (n, %)* Light eating 749 (2.88)
Binge eating 10,817 (41.62)
Nighttime eating 8,197 (31.54)

Weight loss experience (n, %) None 6,575 (25.3)
Diet, exercise only or weight loss drug for less than 3 months 10,247 (39.43)
Weight loss drug over 3 months 9,166 (35.27)

Diseases (n, %)* High blood pressure 1,790 (6.89)
Anemia 1,922 (7.4)
Diabetes 676 (2.6)
Hypothyroidism 598 (2.3)
Hyperthyroidism 293 (1.13)
Gastritis 846 (3.26)
Reflux esophagitis 490 (1.89)
Hyperlipidemia 610 (2.35)
Low back pain 794 (3.06)

* multiple choices allowed

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Table 1B
Independent Variables Used in the 2nd Analysis (n=6,304)
Variables Added Variables in 2nd Analysis

Variables

Age (years) 37.09±10.25 Weight2 (kg) 69.81±10.01
Weight (kg) 72.51±10.36 BMI2 (kg/m2) 26.55±2.94
BMI (kg/m2) 27.58±3.02 Prescription Period1–2 (days) 25.72±5.19

Patients with medication dose change (n, %) Stable 5217(82.76) (1st) Reduced BMI1–2 (kg/m2) 1.02±0.46
→3,847(61.02) (2nd)
Increased 1087(17.24) (1st) Reduced Rate1–2 (%) 3.71±1.6
→2,458(38.98) (2nd)

Gender (n, %) Male 527 (8.34) Reduced Weight1–2 (kg) 2.7±1.25

Female 5,777 (91.64) Symptoms of discomfort 1–2 (n, %)* Gastro-intestinal system 673 (10.68)

Dietary habits (n, %)* Light eating 198 (3.14) Central and peripheral nervous system 497 (7.88)
Binge eating 2,593 (41.13) Psychiatric Symptoms 204 (3.24)
Nighttime eating 1,888 (29.95) Autonomic nervous system 117 (1.86)

Weight loss experience (n, %) None 1,656 (26.27) Others 205 (3.25)

Diet, exercise only or weight loss drug for less than 3 months 2,449 (38.85) Satisfaction with weight loss 1–2 (n, %) Good 3,143 (49.86)
Weight loss drug over 3 months 2,199 (34.88) Fair 2,296 (36.42)

Diseases (n, %)* High blood pressure 487 (7.58) Poor 865 (13.72)

Anemia 445 (7.06) Satiety and appetite suppression 1–2 (n, %) Good 2,691 (42.69)
Diabetes 160 (2.54) Fair 1381 (21.91)
Hypothyroidism 148 (2.35) Poor 2232 (35.41)

Hyperthyroidism 88 (1.4) Attendance2 (n, %) 3,793 (60.17)
Gastritis 141 (2.24)
Reflux esophagitis 67 (1.06)
Hyperlipidemia 162 (2.57)
Low back pain 478 (3.16)

* multiple choices allowed

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Table 1C
Independent Variables Used in the 3rd Analysis (n=833)
Variables
Age (years) 38.19 ± 10.42
Weight (kg) 73.96 ± 10.26
BMI (kg/m2) 28.3 ± 3.14
Patients with medication dose change (n, %) Stable 648(77.79) (1st)→481(57.74) (2nd)→324(38.90) (3rd)
Increased 185(22.21) (1st)→352(42.26) (2nd)→509(61.10) (3rd)
Gender (n, %) Male 51 (8.82)
Female 782 (91.18)
Dietary habits (n, %)* Light eating 23 (2.76)
Binge eating 349 (41.9)
Nighttime eating 242 (29.05)
Weight loss experience (n, %) None 215 (25.81)
Diet, exercise only or weight loss drug for less than 3 months 305 (36.61)
Weight loss drug over 3 months 313 (37.58)
Diseases (n, %)* High blood pressure 72 (8.64)
Anemia 49 (5.88)
Diabetes 26 (3.12)
Hypothyroidism 30 (3.6)
Hyperthyroidism 8 (0.96)
Gastritis 18 (2.16)
Reflux esophagitis 10 (1.2)
Hyperlipidemia 27 (3.24)
Low back pain 28 (3.36)

Added Variables in the 2nd Analysis Added Variables in the 3rd Analysis

Variables Variables

Weight2 (kg) 71.21 ± 9.81 Weight3 (kg) 68.97 ± 9.51
BMI2 (kg/m2) 27.25 ± 3.02 BMI3 (kg/m2) 26.39 ± 2.94
Prescription Period1–2 (days) 25.49 ± 5.24 Prescription Period1–3 (days) 58.57 ± 8.17
Prescription Period2–3 (days) 33.09 ± 6.53
Reduced BMI1–2 (kg/m2) 1.05 ± 0.46 Reduced BMI1–3 (kg/m2) 1.91 ± 0.71
Reduced BMI2–3 (kg/m2) 0.86 ± 0.48
Reduced Rate1–2 (%) 3.70 ± 1.54 Reduced Rate1–3 (%) 6.71 ± 2.31
Reduced Rate2–3 (%) 3.13 ± 1.74
Reduced Weight1–2 (kg) 2.75 ± 1.25 Reduced Weight1–3 (kg) 4.99 ± 1.93
Reduced Weight2–3 (kg) 2.24 ± 1.28
Symptoms of discomfort 1–2 (n, %)* Gastro-intestinal system 79 (9.48) Symptoms of discomfort 2–3 (n, %)* Gastro-intestinal system 72 (8.64)
Central and peripheral nervous system 80 (9.6) Central and peripheral nervous system 38 (4.56)
Psychiatric Symptoms 27 (3.24) Psychiatric Symptoms 24 (2.88)
Autonomic nervous system 14 (1.68) Autonomic nervous system 14 (1.68)
Others 42 (5.04) Others 29 (3.48)
Satisfaction with weight loss 1–2 (n, %) Good 463 (55.58) Satisfaction with weight loss 2–3 (n, %) Good 472 (56.66)
Fair 284 (34.09) Fair 264 (31.69)
Poor 86 (10.32) Poor 97 (11.64)
Satiety and appetite suppression 1–2 (n, %) Good 377 (45.26) Satiety and appetite suppression 2–3 (n, %) Good 343 (41.18)
Fair 186 (22.33) Fair 181 (21.73)
Poor 270 (32.41) Poor 309 (37.09)
Attendance2 (n, %) 586 (70.35) Attendance3 (n, %) 616 (73.95)

* multiple choices allowed

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Table 2
Model Performance for Quartile Classification Bench Mark
Reduced Rate Reduced Weight

1st Bench mark DT1 RF1 LR1 ANN1 1st Bench mark DT1 RF1 LR1 ANN1
Upper 25% 4.87% 74.91% 74.91% 74.91% 74.78% Upper 25% 3.54kg 75.68% 75.70% 75.70% 76.12%
50% 3.75% 53.41% 54.73% 54.26% 55.01% 50% 2.68kg 59.47% 60.18% 59.05% 60.02%
Lower 25% 2.66% 74.80% 74.80% 74.70% 75.08% Lower 25% 1.90kg 75.27% 75.27% 75.29% 75.41%

2nd Bench mark DT2 RF2 LR2 ANN2 2nd Bench mark DT2 RF2 LR2 ANN2

Upper 25% 8.21% 78.81% 80.50% 80.39% 80.17% Upper 25% 5.94kg 82.51% 82.51% 81.13% 82.46%
50% 6.53% 71.04% 71.51% 71.67% 73.59% 50% 4.64kg 72.15% 73.36% 73.89% 75.79%
Lower 25% 4.87% 79.81% 79.39% 80.39% 80.96% Lower 25% 3.44kg 80.13% 80.34% 80.13% 81.22%

3rd Bench mark DT3 RF3 LR3 ANN3 3rd Bench mark DT3 RF3 LR3 ANN3

Upper 25% 10.66% 86.00% 90.00% 86.80% 86.31% Upper 25% 8.02kg 89.20% 90.00% 88.00% 87.51%
50% 8.68% 80.00% 83.20% 84.80% 83.91% 50% 6.22kg 86.00% 85.20% 84.40% 85.23%
Lower 25% 6.68% 84.00% 84.40% 85.60% 86.67% Lower 25% 4.72kg 87.60% 86.00% 86.40% 86.55%

DT: Decision Tree; RF: Random Forest; LR: Logistic Regression; ANN: Artificial Neural Network

Reduced rate of 1st Bench mark = (initial weight – weight at 2nd prescription)/ initial weight *100

Reduced rate of 2nd Bench mark = (initial weight – weight at 3rd prescription)/ initial weight *100

Reduced rate of 3rd Bench mark = (initial weight – weight at last weight report)/ initial weight *100

Table 3
Features of First and Fourth Quartile based on First Prediction Analysis
Reduced Rate Reduced Weight

More Than Upper 25% (n=6,554) Less Than Lower 25% (n=6,527) More Than Upper 25% (n=6,499) Less Than Lower 25% (n=6,507)
Age (years) 34.92 ± 9.58 37.52 ± 10.59 34.59 ± 9.37 37.84 ± 10.67
Gender (n, %) Female 5,930 (90) Female 5,918 (91) Female 5,400 (83) Female 6,118 (94)
Male 624 (10) Male 609 (9) Male 1,099 (17) Male 389 (6)
Height (cm) 162.28 ± 6.67 161.95 ± 6.71 164.22 ± 7.32 160.94 ± 6.26
Weight (kg) 72.76 ± 10.78 72.74 ± 10.5 77.68 ± 12.01 70.06 ± 9.26
BMI (kg/m2) 27.56 ± 3.01 27.68 ± 3.07 28.71 ± 3.23 27.01 ± 2.81
Diet 1 (n, %) 1,908 (29) 1,426 (22) 1,902 (29) 1,393 (21)
Diet 2 (n, %) 2,689 (41) 2,436 (37) 2,593 (40) 2,482 (38)
Diet 3 (n, %) 1,957 (30) 2,665 (41) 2,004 (31) 2,632 (40)
RR (%) 5.89 ± 0.82 1.67 ± 0.79 5.76 ± 0.98 1.71 ± 0.84
RW (kg) 4.29 ± 0.87 1.22 ± 0.61 4.42 ± 0.77 1.18 ± 0.56

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Diet 1: Weight Loss Experience_None; Diet 2: Diet, exercise only or weight loss drug for less than 3 months; Diet 3: Weight Loss Experience_Weight Loss Drug over 3 Months; RR: Reduced Rate; RW: Reduced Weight

Table 4
Model Performance according to Variables Ranking Based on Feature Importance
Reduced Rate in 1st Analysis Reduced Weight in 1st Analysis

Ranking Variables DT (%) RF (%) LR (%) ANN (%) Variables DT (%) RF (%) LR (%) ANN (%)
7 Diet 1 52.19 52.19 52.19 52.44
6 MD_S 54.51 54.51 54.51 54.71
5 Diet 3 54.51 54.51 54.51 54.71
4 Gender 55.07 55.07 55.07 55.22
3 Diet3 52.39 52.39 52.39 53.44 Age 55.91 55.61 55.46 56.48
2 Weight 52.53 52.64 52.39 53.44 BMI 58.61 59.20 58.56 58.90
1 Age 54.06 54.05 53.79 54.69 Weight 58.78 60.06 59.05 59.95

Reduced Rate in 2nd Analysis Reduced Weight in 2nd Analysis

Ranking Variables DT (%) RF (%) LR (%) ANN (%) Variables DT (%) RF (%) LR (%) ANN (%)

8 Gender 54.65 54.65 54.65 53.44
7 SAS 1–2_G 57.14 57.14 57.14 57.63
6 SAS1–2 G 54.44 54.44 54.44 55.50 MD_S 57.77 57.77 57.77 58.60
5 SWL1–2 B 56.40 56.40 56.40 56.76 SWL1–2_B 59.73 59.73 59.83 60.01
4 Weight 57.24 56.50 56.50 57.12 Age 58.56 58.67 60.15 61.02
3 Age 55.97 57.03 57.40 59.18 SWL1–2_G 61.31 62.21 62.58 63.52
2 SWL1–2_G 60.94 61.52 62.42 62.21 Weight 64.16 64.64 64.11 66.23
1 RR 1–2 72.04 71.83 70.51 73.52 RR 1–2 73.41 73.15 72.20 75.33

Reduced Rate in 3rd Analysis Reduced Weight in 3rd Analysis

Ranking Variables DT (%) RF (%) LR (%) ANN (%) Variables DT (%) RF (%) LR (%) ANN (%)

4 Age 54.80 52.80 53.60 58.95 Age 59.20 59.20 58.00 61.95
3 Weight3 52.80 56.80 50.40 57.63 Weight 59.20 64.80 60.80 67.71
2 RR 1–2 70.80 73.60 69.20 71.55 RW 2–3 71.60 76.80 72.00 76.11
1 RR 1–3 80.00 81.20 81.60 81.88 RW 1–3 86.00 86.00 82.00 83.67

DT: Decision Tree; RF: Random Forest; LR: Logistic Regression; ANN: Artificial Neural Network; Diet 3: Weight Loss Experience_Weight Loss Drug over 3 Months; MD_S: Patients with Medication Dose Change_Stable; Diet 1: Weight Loss Experience_None; SAS 1–2_G: Satiety and Appetite Suppression 1–2_Good; SWL1–2_B: Satisfaction with Weight Loss 1–2_Bad; SWL1–2_G: Satisfaction with Weight Loss 1–2_Good; RR: Reduced Rate; RW: Reduced Weight

Table 5
Prediction Model Performance Results based on AUC
Algorithm Sensitivity Specificity AUC
1st Analysis Reduced Rate DT 0.557 0.524 0.551
RF 0.591 0.489 0.557
LR 0.576 0.499 0.546
ANN 0.574 0.497 0.550

Reduced weight DT 0.709 0.465 0.630
RF 0.609 0.591 0.640
LR 0.582 0.602 0.631
ANN 0.426 0.748 0.620

2nd Analysis Reduced Rate DT 0.722 0.719 0.791
RF 0.748 0.690 0.789
LR 0.734 0.677 0.785
ANN 0.805 0.606 0.785

Reduced weight DT 0.784 0.687 0.802
RF 0.764 0.700 0.816
LR 0.761 0.685 0.801
ANN 0.880 0.557 0.798

3rd Analysis Reduced Rate DT 0.762 0.836 0.890
RF 0.787 0.836 0.890
LR 0.852 0.781 0.897
ANN 0.828 0.789 0.880

Reduced weight DT 0.873 0.848 0.937
RF 0.873 0.848 0.939
LR 0.847 0.795 0.905
ANN 0.788 0.856 0.920

DT: Decision Tree; RF: Random Forest; LR: Logistic Regression; ANN: Artificial Neural Network; AUC: Area Under the Curve (0.90 – 1.00: excellent, 0.80 – 0.90: good, 0.70 – 0.80: fair, 0.60 – 0.70: poor, 0.50 – 0.60: fail)

참고문헌

1 Hill JO, Wyatt HR, Peters JC. Energy balance and obesity. Circulation. 2012; 126:1. 126–32.
crossref pmid pmc

2 Haidar YM, Cosman BC. Obesity epidemiology. Clin Colon Rectal Surg. 2011; 24:4. 205–10.
crossref pmid pmc

3 Wall KC, Politzer CS, Chahla J, Garrigues GE. Obesity is associated with an increased prevalence of glenohumeral osteoarthritis and arthroplasty: A cohort study. Orthop Clin N Am. 2020; 51:2. 259–264.
crossref

4 Kolb R, Sutterwala FS, Zhang W. Obesity and cancer: inflammation bridges the two. Curr Opin Pharmacol. 2016; 29:77–89.
crossref pmid pmc

5 Handjieva-Darlenska T, Handjiev S, Larsen TM, Baak MA, Jebb S, Papadaki A, et al. Initial weight loss on an 800-kcal diet as a predictor of weight loss success after 8 weeks: the Diogenes study. Eur J Clin Nutr. 2010; 64:9. 994–9.
crossref pmid

6 Hollis JF, Gullion CM, Stevens VJ, Brantley PJ, Appel LJ, Ard JD, et al. Weight loss during the intensive intervention phase of the weight-loss maintenance trial. Am J Prev Med. 2008; 35:2. 118–26.
crossref pmid pmc

7 Reed JR, Yates BC, Houfek J, Briner W, Schmid KK, Pullen CH. Motivational Factors Predict Weight Loss in Rural Adults. Public Health Nurs. 2016; 33:3. 232–241.
crossref pmid

8 Annesi JJ, Whitaker AC. Psychological factors discriminating between successful and unsuccessful weight loss in a behavioral exercise and nutrition education treatment. Int J Behav Med. 2010; 17:3. 168–75.
crossref pmid

9 Fabricatore AN, Wadden TA, Moore RH, Butryn ML, Heymsfield SB, Nguyen AM. Predictors of attrition and weight loss success: Results from a randomized controlled trial. Behav Res Ther. 2009; 47:8. 685–91.
crossref

10 Hadziabdic MO, Mucalo I, Hrabac P, Matic T, Rahelic D, Bozikov V. Factors predictive of drop-out and weight loss success in weight management of obese patients. J Hum Nutr Diet. 2015; 28:2. 24–32.
crossref pmid

11 Batterham M, Tapsell LC, Charlton KE. Predicting dropout in dietary weight loss trials using demographic and early weight change characteristics: Implications for trial design. Obes Res Clin Pract. 2016; 10:2. 189–96.
crossref pmid

12 Kang EY, Park YB, Kim MY, Park YJ. A Study on Factors Associated with Weight Loss by ‘Gamitaeeumjowee-Tang’ . J Korean Med Obes Res. 2017; 17:2. 68–72.
crossref

13 Batterham M, Tapsell L, Charlton K, O’Shea J, Thorne R. Using data mining to predict success in a weight loss trial. J Hum Nutr Diet. 2017; 30:4. 471–478.
crossref pmid

14 Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019; 380:1347–1358.
crossref pmid

15 Kim H, Yang SB, Kang Y, Park YB, Kim JH. Machine learning approach to blood stasis pattern identification based on self-reported symptoms. Korean J Acupunct. 2016; 33:3. 102–113.
crossref

16 Sharma K, Kaur A, Gujral S. Brain tumor detection based on machine learning algorithms. Int J Comput Appl. 2014; 103:1. 7–11.
crossref

17 Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015; 13:8–17.
crossref pmid

18 Wu CC, Hsu WD, Islam MM, Poly TN, Yang HC, Nguyen PA, et al. An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain. Comput Methods Programs Biomed. 2019; 173:109–117.
crossref pmid

19 Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012; 16:5. 933–51.
crossref pmid pmc

20 Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine Learning Techniques for Prediction of Early Childhood Obesity. Appl Clin Inform. 2015; 6:3. 506–520.
crossref pmid pmc

21 Hammond R, Athanasiadou R, Curado S, Aphinyanaphongs Y, Abrams C, Messito MJ, et al. Predicting childhood obesity using electronic health records and publicly available data. PLoS ONE. 14:4. e0215571 https://doi.org/10.1371/journal.pone.0215571
crossref

22 Aswani A, Kaminsky P, Mintz Y, Flowers E, Fukuoka Y. Behavioral Modeling in Weight Loss Interventions. Eur J Oper Res. 2019; 272:3. 1058–1072.
crossref pmid

23 Kim YM, Cho DG, Kang SH. Analysis of Factors associated with Geographic Variations in the Prevalence of Adult Obesity using Decision Tree. Health Soc Sci. 2014; 36:1. 157–181.


24 Nam SH, Kim SY, Lim YW, Park YB. Review on predictors of weight loss in obesity treatment. J Korean Med Obes Res. 2018; 18:2. 115–127.
crossref

25 Yoon NR, Yoo YJ, Kim MJ, Kim SY, Lim YW, Lim HH, et al. Analysis of adverse events in weight loss program in combination with ‘Gamitaeeumjowee-Tang’ and low-calorie diet. J Korean Med Obes Res. 2018; 18:1. 1–9.
crossref

26 Kurs MB, Rudnicki WR. Feature Selection with the Boruta Package. J Stat Softw. 2010; 36:11. 1–13.


27 Jung D, Kim G, Park J, Lee H, Kim H, Choi H, et al. Prediction of rehospitalization of patients and finding causes of it with data mashup and bigdata analysis. Entrue J Inf Technol. 2015; 14:3. 133–149.


28 Díaz-Uriarte R, Andrés SA. Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006; 7:3 https://doi.org/10.1186/1471-2105-7-3
crossref pmid pmc

29 Ortega Hinojosa AM, MacLeod KE, Balmes J, Jerrett M. Influence of school environments on childhood obesity in California. Environ Res. 2018; 166:100–107.
crossref pmid

30 Munger E, Choi H, Dey AK, Elnabawi YA, Groenendyk JW, Rodante J, et al. Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study”. J Am Acad Dermatol. 2019; Article in presshttps://doi.org/10.1016/j.jaad.2019.10.060
crossref

31 Scheinker D, Valencia A, Rodriguez F. Identification of factors associated with variation in US county-level obesity prevalence rates using epidemiologic vs machine learning models. JAMA Netw Open. 2019; 2:4. e192884
crossref pmid pmc

32 Forman EM, Kerrigan SG, Butryn ML, Juarascio AS, Manasse SM, Ontañón S, et al. Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss? J Behav Med. 2019; 42:2. 276–290.
crossref

33 Hong N, Wen A, Stone DJ, Tsuji S, Kingsbury PR, Rasmussen LV, et al. Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries. J Biomed Inform. 2019; 99:103310https://doi.org/10.1016/j.jbi.2019.103310
crossref

34 Han JY, Park YJ. Analysis of factors influencing obesity treatment according to initial condition and compliance with medication. J Korean Med Obes Res. 2019; 19:1. 31–41.
crossref

35 Magkos F, Fraterrigo G, Yoshino J, Luecking C, Kirbach K, Kelly SC. Effects of Moderate and Subsequent Progressive Weight Loss on Metabolic Function and Adipose Tissue Biology in Humans with Obesity. Cell Metab. 2016; 23:4. 591–601.
crossref pmid pmc

36 Jo GW, Ok JM, Kim SY, Lim YW. Review on the Efficacy and Safety of Mahuang and Ephedrine in the Treatment of Obesity -Focused on RCT-. J Korean Med. 2017; 38:3. 170–184.
crossref

37 Disse E, Ledoux S, Bétry C, Caussy C, Maitrepierre C, Coupaye M, et al. An artificial neural network to predict resting energy expenditure in obesity. Clin Nutr. 2018; 37:5. 1661–1669.
crossref pmid

TOOLS
PDF Links  PDF Links
Full text via DOI  Full text via DOI
PubReader  PubReader
Download Citation  Download Citation
  E-Mail
  Print
Share:      
METRICS
0
Crossref
131
View
12
Download
Editorial office contact information
3F, #26-27 Gayang-dong, Gangseo-gu Seoul, 157-200 Seoul, Korea
The Society of Korean Medicine
Tel : +82-2-2658-2658-3627   Fax : +82-2-2658-3631   E-mail : skom1953@daum.net
About |  Browse Articles |  Current Issue |  For Authors and Reviewers
powerd by m2community