Journal of Traditional Chinese Medicine ›› 2025, Vol. 45 ›› Issue (1): 192-200.DOI: 10.19852/j.cnki.jtcm.2025.01.019
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Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension
CHE Qianzi1, LIU Dasheng2, XIANG Xinghua1, TIAN Yaxin1, XIE Feibiao1, XU Wenyuan1, LIU Jian4, WANG Xuejie3, WANG Liying3, BAI Weiguo1, HAN Xuejie3(
), YANG Wei1()
- 1 Department of Medical Statistics, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
2 Department of Science and Education, Medical Statistics Teaching and Research Office, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
3 Traditional Chinese Medicine Standards Research Center, Medical Statistics Teaching and Research Office, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
4 Computer Department, Xiyuan Hospital of the China Academy of Chinese Medical Sciences, Beijing 100091, China
-
Received:2024-01-22Accepted:2024-04-15Online:2025-02-15Published:2025-01-10 -
Contact:Prof. HAN Xuejie, Traditional Chinese Medicine Standards Research Center, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.xuejieh@126.com ; Prof. YANG Wei, Department of Medical Statistics, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.yangyxq@ruc.edu.cn Telephone: +86-18910162189 -
Supported by:the China Academy of Chinese Medical Sciences, Independent Topic Project: Application Research on Named Entity Recognition and Relationship Extraction of Case Records of Renowned Traditional Chinese Medicine Practitioners(Z0643);China Academy of Chinese Medical Sciences, Independent Topic Project: Analysis of Research Directions and Scope in the Discipline of Traditional Chinese Medicine Statistics(Z0723);China Academy of Chinese Medical Sciences, Science and Technology Innovation Project: Real-world Effectiveness Evaluation of Traditional Chinese Medicine and Translational Application Research on Causal Inference(CI2021A04706);China Academy of Chinese Medical Sciences, Science and Technology Innovation Project: Research on Causal Inference Methodology for Real-world Clinical Evaluation in Traditional Chinese Medicine(CI2021B003);National Key Research and Development Program of China: Integrated Evaluation Model and Key Technologies of "Syndrome-Disease-Prescription" for Traditional Chinese Medicine in the Prevention and Treatment of Coronary Heart Disease — Statistical Data Analysis and Data Mining(2017YFC1700406-2)
Cite this article
CHE Qianzi, LIU Dasheng, XIANG Xinghua, TIAN Yaxin, XIE Feibiao, XU Wenyuan, LIU Jian, WANG Xuejie, WANG Liying, BAI Weiguo, HAN Xuejie, YANG Wei. Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension[J]. Journal of Traditional Chinese Medicine, 2025, 45(1): 192-200.
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Table 1 Statistical summary of demographic and clinical features from the MACE group and no MACE group ($\bar{x}±s$)
| Characteristic | MACE (n = 1286) | No MACE (n = 152) | Total (n = 1438) | P value | |
|---|---|---|---|---|---|
| Age (years) | 72.9±12.3 | 61.3±14.5 | 71.6±13.0 | <0.001 | |
| ≤60 years old [n (%)] | 192 (14.9) | 69 (45.4) | 261 (18.2) | <0.001 | |
| > 60 years old [n (%)] | 1094 (85.1) | 83 (54.6) | 1177 (81.8) | ||
| Male [n (%)] | 641 (49.8) | 63 (41.4) | 704 (49.0) | 0.050 | |
| BMI (kg/m2) | 27.0±5.2 | 26.5±9.3 | 27.0±5.2 | 0.853 | |
| Temperature (℃) | 36.5±0.5 | 36.4±0.4 | 36.5±0.5 | 0.072 | |
| Heart rate (bpm) | 81.0±16.7 | 78.2±13.1 | 80.7±16.3 | 0.120 | |
| SBP (mm Hg) | 143.3±22.8 | 149.3±28.6 | 144.0±23.6 | 0.030 | |
| DBP (mm Hg) | 79.7±14.9 | 88.4±18.5 | 80.6±15.6 | <0.001 | |
| Smoking history [n (%)] | 326 (25.4) | 27 (17.8) | 353 (24.6) | 0.038 | |
| Smoking duration | 33.37 (14.7) | 24.00 (11.2) | 32.63 (14.7) | 0.007 | |
| Comorbidities [n (%)] | Hypertension | 1094 (85.3) | 93 (61.2) | 1187 (82.7) | <0.001 |
| Hypertension class Ⅲ | 773 (60.1) | 69 (45.4) | 842 (58.6) | <0.001 | |
| Hyperlipidemia | 313 (24.4) | 30 (19.7) | 343 (23.9) | 0.203 | |
| Diabetes | 510 (39.8) | 35 (23.0) | 545 (38.0) | <0.001 | |
| Arrhythmia | 193 (15.0) | 6 (3.9) | 199 (13.9) | <0.001 | |
| Cerebral infarction | 321 (25.0) | 1 (0.7) | 322 (22.4) | <0.001 | |
| Kidney dysfunction | 114 (8.9) | 0 (0.0) | 114 (7.9) | <0.001 | |
| Atherosclerotic | 115 (9.0) | 6 (3.9) | 121 (8.4) | 0.035 | |
| Symptoms [n (%)] | Asthmatoid dyspnea | 326 (25.3) | 6 (3.9) | 332 (23.1) | <0.001 |
| Chest congestion | 612 (47.6) | 31 (20.4) | 643 (44.7) | <0.001 | |
| Poor sleep | 578 (44.9) | 55 (36.2) | 633 (44.0) | 0.040 | |
| Pale complexion | 581 (45.2) | 35 (23.0) | 616 (42.8) | <0.001 | |
| Empty eyes | 589 (45.8) | 33 (21.7) | 622 (43.3) | <0.001 | |
| Fatigue | 537 (41.8) | 39 (25.7) | 576 (40.1) | <0.001 | |
| Poor appetite | 501 (39.0) | 34 (22.4) | 535 (37.2) | <0.001 | |
| Legs edema | 506 (39.3) | 32 (21.1) | 538 (37.4) | <0.001 | |
| Constipation | 350 (27.2) | 8 (5.3) | 358 (24.9) | <0.001 | |
| Out of condition | 487 (37.9) | 24 (15.8) | 511 (35.5) | <0.001 | |
| Tongue body [n (%)] | Tooth-marked tongue | 41 (3.2) | 4 (2.6) | 45 (3.1) | 0.709 |
| Old tongue | 514 (40.0) | 45 (29.6) | 559 (38.9) | 0.013 | |
| Fissured tongue | 39 (3.0) | 3 (2.0) | 42 (2.9) | 0.463 | |
| Fat tongue | 3 (2.0) | 81 (6.3) | 84 (5.8) | 0.032 | |
| Tongue proper [n (%)] | Dark | 138 (10.7) | 16 (10.5) | 154 (10.7) | 0.939 |
| Dark red | 380 (29.5) | 40 (26.3) | 420 (29.2) | 0.407 | |
| Pink | 75 (5.8) | 10 (6.6) | 85 (5.9) | 0.712 | |
| Pink and dark | 146 (11.4) | 11 (7.2) | 157 (10.9) | 0.124 | |
| Pink and red | 278 (21.6) | 40 (26.3) | 318 (22.1) | 0.187 | |
| Red | 208 (16.2) | 33 (21.7) | 241 (16.8) | 0.084 | |
| Tongue coating [n (%)] | White fur | 458 (50.2) | 61 (57.5) | 519 (51.0) | 0.273 |
| Yellow fur | 431 (47.3) | 43 (40.6) | 474 (46.6) | 0.195 | |
| Few coating | 87 (6.8) | 5 (3.3) | 92 (6.4) | 0.098 | |
| Moist fur | 1223 (95.1) | 150 (98.7) | 1373 (95.5) | 0.044 | |
| Dry fur | 57 (4.4) | 2 (1.3) | 59 (4.1) | 0.067 | |
| Thin fur | 409 (31.8) | 59 (38.8) | 468 (32.5) | 0.081 | |
| Greasy fur | 638 (49.6) | 61 (40.1) | 699 (48.6) | 0.027 | |
| Pulse conditions [(n (%)] | Deep | 293 (22.8) | 27 (17.8) | 320 (22.3) | 0.159 |
| Intermittent | 64 (5) | 0 (0) | 64 (4.5) | 0.005 | |
| Slippery | 485 (37.7) | 60 (39.5) | 545 (37.9) | 0.672 | |
| Relaxed | 40 (3.1) | 0 (0) | 40 (2.8) | 0.027 | |
| Weak | 67 (5.2) | 3 (2) | 70 (4.9) | 0.080 | |
| Rapid | 140 (10.9) | 13 (8.6) | 153 (10.6) | 0.378 | |
| Thready | 333 (25.9) | 39 (25.7) | 372 (25.9) | 0.950 | |
| Wiry | 616 (47.9) | 93 (61.2) | 709 (49.3) | 0.002 | |
Table 1 Statistical summary of demographic and clinical features from the MACE group and no MACE group ($\bar{x}±s$)
| Characteristic | MACE (n = 1286) | No MACE (n = 152) | Total (n = 1438) | P value | |
|---|---|---|---|---|---|
| Age (years) | 72.9±12.3 | 61.3±14.5 | 71.6±13.0 | <0.001 | |
| ≤60 years old [n (%)] | 192 (14.9) | 69 (45.4) | 261 (18.2) | <0.001 | |
| > 60 years old [n (%)] | 1094 (85.1) | 83 (54.6) | 1177 (81.8) | ||
| Male [n (%)] | 641 (49.8) | 63 (41.4) | 704 (49.0) | 0.050 | |
| BMI (kg/m2) | 27.0±5.2 | 26.5±9.3 | 27.0±5.2 | 0.853 | |
| Temperature (℃) | 36.5±0.5 | 36.4±0.4 | 36.5±0.5 | 0.072 | |
| Heart rate (bpm) | 81.0±16.7 | 78.2±13.1 | 80.7±16.3 | 0.120 | |
| SBP (mm Hg) | 143.3±22.8 | 149.3±28.6 | 144.0±23.6 | 0.030 | |
| DBP (mm Hg) | 79.7±14.9 | 88.4±18.5 | 80.6±15.6 | <0.001 | |
| Smoking history [n (%)] | 326 (25.4) | 27 (17.8) | 353 (24.6) | 0.038 | |
| Smoking duration | 33.37 (14.7) | 24.00 (11.2) | 32.63 (14.7) | 0.007 | |
| Comorbidities [n (%)] | Hypertension | 1094 (85.3) | 93 (61.2) | 1187 (82.7) | <0.001 |
| Hypertension class Ⅲ | 773 (60.1) | 69 (45.4) | 842 (58.6) | <0.001 | |
| Hyperlipidemia | 313 (24.4) | 30 (19.7) | 343 (23.9) | 0.203 | |
| Diabetes | 510 (39.8) | 35 (23.0) | 545 (38.0) | <0.001 | |
| Arrhythmia | 193 (15.0) | 6 (3.9) | 199 (13.9) | <0.001 | |
| Cerebral infarction | 321 (25.0) | 1 (0.7) | 322 (22.4) | <0.001 | |
| Kidney dysfunction | 114 (8.9) | 0 (0.0) | 114 (7.9) | <0.001 | |
| Atherosclerotic | 115 (9.0) | 6 (3.9) | 121 (8.4) | 0.035 | |
| Symptoms [n (%)] | Asthmatoid dyspnea | 326 (25.3) | 6 (3.9) | 332 (23.1) | <0.001 |
| Chest congestion | 612 (47.6) | 31 (20.4) | 643 (44.7) | <0.001 | |
| Poor sleep | 578 (44.9) | 55 (36.2) | 633 (44.0) | 0.040 | |
| Pale complexion | 581 (45.2) | 35 (23.0) | 616 (42.8) | <0.001 | |
| Empty eyes | 589 (45.8) | 33 (21.7) | 622 (43.3) | <0.001 | |
| Fatigue | 537 (41.8) | 39 (25.7) | 576 (40.1) | <0.001 | |
| Poor appetite | 501 (39.0) | 34 (22.4) | 535 (37.2) | <0.001 | |
| Legs edema | 506 (39.3) | 32 (21.1) | 538 (37.4) | <0.001 | |
| Constipation | 350 (27.2) | 8 (5.3) | 358 (24.9) | <0.001 | |
| Out of condition | 487 (37.9) | 24 (15.8) | 511 (35.5) | <0.001 | |
| Tongue body [n (%)] | Tooth-marked tongue | 41 (3.2) | 4 (2.6) | 45 (3.1) | 0.709 |
| Old tongue | 514 (40.0) | 45 (29.6) | 559 (38.9) | 0.013 | |
| Fissured tongue | 39 (3.0) | 3 (2.0) | 42 (2.9) | 0.463 | |
| Fat tongue | 3 (2.0) | 81 (6.3) | 84 (5.8) | 0.032 | |
| Tongue proper [n (%)] | Dark | 138 (10.7) | 16 (10.5) | 154 (10.7) | 0.939 |
| Dark red | 380 (29.5) | 40 (26.3) | 420 (29.2) | 0.407 | |
| Pink | 75 (5.8) | 10 (6.6) | 85 (5.9) | 0.712 | |
| Pink and dark | 146 (11.4) | 11 (7.2) | 157 (10.9) | 0.124 | |
| Pink and red | 278 (21.6) | 40 (26.3) | 318 (22.1) | 0.187 | |
| Red | 208 (16.2) | 33 (21.7) | 241 (16.8) | 0.084 | |
| Tongue coating [n (%)] | White fur | 458 (50.2) | 61 (57.5) | 519 (51.0) | 0.273 |
| Yellow fur | 431 (47.3) | 43 (40.6) | 474 (46.6) | 0.195 | |
| Few coating | 87 (6.8) | 5 (3.3) | 92 (6.4) | 0.098 | |
| Moist fur | 1223 (95.1) | 150 (98.7) | 1373 (95.5) | 0.044 | |
| Dry fur | 57 (4.4) | 2 (1.3) | 59 (4.1) | 0.067 | |
| Thin fur | 409 (31.8) | 59 (38.8) | 468 (32.5) | 0.081 | |
| Greasy fur | 638 (49.6) | 61 (40.1) | 699 (48.6) | 0.027 | |
| Pulse conditions [(n (%)] | Deep | 293 (22.8) | 27 (17.8) | 320 (22.3) | 0.159 |
| Intermittent | 64 (5) | 0 (0) | 64 (4.5) | 0.005 | |
| Slippery | 485 (37.7) | 60 (39.5) | 545 (37.9) | 0.672 | |
| Relaxed | 40 (3.1) | 0 (0) | 40 (2.8) | 0.027 | |
| Weak | 67 (5.2) | 3 (2) | 70 (4.9) | 0.080 | |
| Rapid | 140 (10.9) | 13 (8.6) | 153 (10.6) | 0.378 | |
| Thready | 333 (25.9) | 39 (25.7) | 372 (25.9) | 0.950 | |
| Wiry | 616 (47.9) | 93 (61.2) | 709 (49.3) | 0.002 | |
Table 2 Machine learning model evaluation in the testing set
| Model | Accuracy | Sensitivity | Specificity | Precision | F-value | G-mean |
|---|---|---|---|---|---|---|
| GBM | 0.893 | 0.964 | 0.222 | 0.921 | 0.942 | 0.453 |
| XGBoost | 0.934 | 0.989 | 0.385 | 0.942 | 0.965 | 0.617 |
Table 2 Machine learning model evaluation in the testing set
| Model | Accuracy | Sensitivity | Specificity | Precision | F-value | G-mean |
|---|---|---|---|---|---|---|
| GBM | 0.893 | 0.964 | 0.222 | 0.921 | 0.942 | 0.453 |
| XGBoost | 0.934 | 0.989 | 0.385 | 0.942 | 0.965 | 0.617 |
Table 3 Feature importance scores for the prediction of MACE provide by GBM and XGBoost classifier
| GBM-feature | GBM-importance | XGBoost-feature | XGBoost-importance | |
|---|---|---|---|---|
| DBP | 10.421 | DBP | 0.124 | |
| Hypertension | 7.510 | ≤60 years old | 0.095 | |
| Cerebral infarction | 6.238 | Heart rate | 0.09 | |
| SBP | 5.529 | SBP | 0.088 | |
| Chest congestion | 5.233 | Chest congestion | 0.063 | |
| ≤60 years old | 5.143 | Gender | 0.055 | |
| > 60 years old | 4.651 | >60 years old | 0.054 | |
| Smoking duration | 3.995 | Legs edema | 0.034 | |
| Heart rate | 3.980 | Old tongue | 0.032 | |
| Empty eyes | 3.295 | Constipation | 0.032 | |
| Constipation | 3.250 | Hypertension | 0.028 | |
| Temperature | 3.228 | Hypertension class Ⅲ | 0.028 | |
| Asthmatoid dyspnea | 2.581 | Cerebral infarction | 0.026 | |
| Atherosclerotic | 2.485 | Empty eyes | 0.024 | |
| Gender | 1.861 | Asthmatoid dyspnea | 0.024 |
Table 3 Feature importance scores for the prediction of MACE provide by GBM and XGBoost classifier
| GBM-feature | GBM-importance | XGBoost-feature | XGBoost-importance | |
|---|---|---|---|---|
| DBP | 10.421 | DBP | 0.124 | |
| Hypertension | 7.510 | ≤60 years old | 0.095 | |
| Cerebral infarction | 6.238 | Heart rate | 0.09 | |
| SBP | 5.529 | SBP | 0.088 | |
| Chest congestion | 5.233 | Chest congestion | 0.063 | |
| ≤60 years old | 5.143 | Gender | 0.055 | |
| > 60 years old | 4.651 | >60 years old | 0.054 | |
| Smoking duration | 3.995 | Legs edema | 0.034 | |
| Heart rate | 3.980 | Old tongue | 0.032 | |
| Empty eyes | 3.295 | Constipation | 0.032 | |
| Constipation | 3.250 | Hypertension | 0.028 | |
| Temperature | 3.228 | Hypertension class Ⅲ | 0.028 | |
| Asthmatoid dyspnea | 2.581 | Cerebral infarction | 0.026 | |
| Atherosclerotic | 2.485 | Empty eyes | 0.024 | |
| Gender | 1.861 | Asthmatoid dyspnea | 0.024 |
Figure 1 Meaningful association rules between key risk factors and Chinese herbal materials
Figure 1 Meaningful association rules between key risk factors and Chinese herbal materials
Figure 2 Meaningful association rules between key risk factors and Chinese herbal materials
Figure 2 Meaningful association rules between key risk factors and Chinese herbal materials
Figure 3 Meaningful association rules between key risk factors and Traditional Chinese Medicine syndrome elements
Figure 3 Meaningful association rules between key risk factors and Traditional Chinese Medicine syndrome elements
Table 4 Application rules of combined TCM and western medicine treatment on major adverse cardiovascular events and their target population
| Main symptoms | Western Medicine | Complication | TCM syndrome elements | Herbal materials |
|---|---|---|---|---|
| Chest congestion | diuretics, β receptor blockers, CCBs, ARBs | Wiry pulse, poor sleep | Blood stasis | Chuanxiong (Rhizoma Chuanxiong), Danggui (Radix Angelica Sinensis), Chishao (Radix Paeoniae Rubra) |
| Greasy fur, slippery pulse | Phlegm | Banxia (Rhizoma Pinelliae), Chenpi (Pericarpium Citri Reticulatae), Fuling (Poria) | ||
| Empty eyes, leg edema | Qi deficiency | Huangqi (Radix Astragali Mongolici), Dangshen (Radix Codonopsis), Baizhu (Rhizoma Atractylodis Macrocephalae) and Gancao (Radix Glycyrrhizae) | ||
| Empty eyes | diuretics, β-blockers, CCBs | Greasy fur, slippery pulse | Phlegm | Fuling (Poria), Chenpi (Pericarpium Citri Reticulatae) |
| Wiry pulse, chest congestion | Blood stasis | Chuanxiong (Rhizoma Chuanxiong), Danggui (Radix Angelica Sinensis), Chishao (Radix Paeoniae Rubra) | ||
| Fatigue, pale complexion, out of condition | Qi deficiency | Huangqi (Radix Astragali Mongolici), Dangshen (Radix Codonopsis), Gancao (Radix Glycyrrhizae) |
Table 4 Application rules of combined TCM and western medicine treatment on major adverse cardiovascular events and their target population
| Main symptoms | Western Medicine | Complication | TCM syndrome elements | Herbal materials |
|---|---|---|---|---|
| Chest congestion | diuretics, β receptor blockers, CCBs, ARBs | Wiry pulse, poor sleep | Blood stasis | Chuanxiong (Rhizoma Chuanxiong), Danggui (Radix Angelica Sinensis), Chishao (Radix Paeoniae Rubra) |
| Greasy fur, slippery pulse | Phlegm | Banxia (Rhizoma Pinelliae), Chenpi (Pericarpium Citri Reticulatae), Fuling (Poria) | ||
| Empty eyes, leg edema | Qi deficiency | Huangqi (Radix Astragali Mongolici), Dangshen (Radix Codonopsis), Baizhu (Rhizoma Atractylodis Macrocephalae) and Gancao (Radix Glycyrrhizae) | ||
| Empty eyes | diuretics, β-blockers, CCBs | Greasy fur, slippery pulse | Phlegm | Fuling (Poria), Chenpi (Pericarpium Citri Reticulatae) |
| Wiry pulse, chest congestion | Blood stasis | Chuanxiong (Rhizoma Chuanxiong), Danggui (Radix Angelica Sinensis), Chishao (Radix Paeoniae Rubra) | ||
| Fatigue, pale complexion, out of condition | Qi deficiency | Huangqi (Radix Astragali Mongolici), Dangshen (Radix Codonopsis), Gancao (Radix Glycyrrhizae) |
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