Advancements in clinical application of machine learning for identifying depression and anxiety in patients with bronchial asthma_West China Medical Journal

Authors：

ZHU Meng ,  ZHAO Taihong

The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, P. R. China;

Corresponding?author：

ZHAO Taihong, Email: zhaotaihong@njmu.edu.cn

Keywords：

Bronchial asthma; depression and anxiety; machine learning

DOI：

10.7507/1002-0179.202508202

Video：

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Abstract Full text Figures/Tables Video References Cited by

Bronchial asthma, a typical psychosomatic disease, is frequently comorbid with depression and anxiety, while traditional assessment methods have significant limitations in this regard. Machine learning can integrate multimodal data including clinical, behavioral and environmental information to construct accurate predictive models, enabling early identification and intervention. This paper systematically reviews the epidemiological association between bronchial asthma and depression and anxiety as well as their neuro-immune-endocrine comorbidity mechanism, discusses the application potential of traditional machine learning models and deep learning models, emphasizes the core value of multimodal feature integration and model interpretability, and points out the key challenges in current research. This article aims to reveal the value of technological breakthroughs brought by machine learning, and proposes future development directions including the establishment of a multicenter standardized database and the enhancement of model interpretability, so as to provide scientific basis for constructing an integrated psychosomatic diagnosis and treatment system for bronchial asthma and improving long-term prognosis for patients.

Citation： ZHU Meng, ZHAO Taihong. Advancements in clinical application of machine learning for identifying depression and anxiety in patients with bronchial asthma. West China Medical Journal, 2026, 41(4): 658-664. doi: 10.7507/1002-0179.202508202 Copy

1.	Menzies-Gow A, Bafadhel M, Busse WW, et al. An expert consensus framework for asthma remission as a treatment goal.J Allergy Clin Immunol, 2020, 145(3): 757-765.
2.	Ramsahai JM, Hansbro PM, Wark PAB. Mechanisms and management of asthma exacerbations. Am J Respir Crit Care Med, 2019, 199(4): 423-432.
3.	Conway AE, Verdi M, Kartha N, et al. Allergic diseases and mental health. J Allergy Clin Immunol Pract, 2024, 12(9): 2298-2309.
4.	Hohls JK, K?nig HH, Quirke E, et al. Anxiety, depression and quality of life-a systematic review of evidence from longitudinal observational studies. Int J Environ Res Public Health, 2021, 18(22): 12022.
5.	Cazzola M, Rogliani P, Ora J, et al. Asthma and comorbidities: recent advances. Pol Arch Intern Med, 2022, 132(4): 16250.
6.	Jiang M, Qin P, Yang X. Comorbidity between depression and asthma via immune-inflammatory pathways: a meta-analysis. J Affect Disord, 2014, 166: 22-29.
7.	張靜, 魏軍, 龔玉蕾. 支氣管哮喘患者合并焦慮/抑郁情緒的風險因素. 國際精神病學雜志, 2023, 50(5): 1125-1127, 1131.
8.	張洋, 王慧淵, 耿妍, 等. 支氣管哮喘兒童情緒問題現狀及相關因素分析. 中國兒童保健雜志, 2022, 30(12): 1400-1403, 1408.
9.	Ye G, Baldwin DS, Hou R. Anxiety in asthma: a systematic review and meta-analysis. Psychol Med, 2021, 51(1): 11-20.
10.	de Boer GM, Houweling L, Hendriks RW, et al. Asthma patients experience increased symptoms of anxiety, depression and fear during the COVID-19 pandemic. Chron Respir Dis, 2021, 18: 14799731211029658.
11.	陳建麗, 褚旭麗, 李雁, 等. 社會支持對支氣管哮喘患兒家長家庭親密度適應性及抑郁水平的影響. 中國婦幼保健, 2021, 36(8): 1865-1868.
12.	Gao YH, Zhao HS, Zhang FR, et al. The relationship between depression and asthma: a meta-analysis of prospective studies. PLoS One, 2015, 10(7): e0132424.
13.	Leonard SI, Turi ER, Powell JS, et al. Associations of asthma self-management and mental health in adolescents: a scoping review. Respir Med, 2022, 200: 106897.
14.	Lehrer PM, Irvin CG, Lu SE, et al. Relationships among pulmonary function, anxiety and depression in mild asthma: an exploratory study. Biol Psychol, 2022, 168: 108244.
15.	Lee S, Rhee DK. Effects of ginseng on stress-related depression, anxiety, and the hypothalamic-pituitary-adrenal axis. J Ginseng Res, 2017, 41(4): 589-594.
16.	Plaza-González S, Zabala-Ba?os MDC, Astasio-Picado á, et al. Psychological and sociocultural determinants in childhood asthma disease: impact on quality of life. Int J Environ Res Public Health, 2022, 19(5): 2652.
17.	陳瓊琰, 戴元榮, 金晨慈, 等. 不同皮質醇水平哮喘患者哮喘相關指標及焦慮抑郁因素分析. 浙江醫學, 2019, 41(20): 2211-2214.
18.	Dereix AE, Ledyard R, Redhunt AM, et al. Maternal anxiety and depression in pregnancy and DNA methylation of the NR3C1 glucocorticoid receptor gene. Epigenomics, 2021, 13(21):1701-1709.
19.	Miller RL, Grayson MH, Strothman K. Advances in asthma: new understandings of asthma’s natural history, risk factors, underlying mechanisms, and clinical management. J Allergy Clin Immunol, 2021, 148(6): 1430-1441.
20.	Zhu X, Cui J, Yi L, et al. The role of t cells and macrophages in asthma pathogenesis: a new perspective on mutual crosstalk. Mediators Inflamm, 2020, 2020: 7835284.
21.	Thompson DA, Wabara YB, Duran S, et al. Single cell analysis identifies distinct CD4 ⁺T cells associated with the pathobiology of pediatric obesity related asthma. Sci Rep, 2025, 15(1): 6844.
22.	Zonca V, Marizzoni M, Saleri S, et al. Inflammation and immune system pathways as biological signatures of adolescent depression-the IDEA-RiSCo study. Transl Psychiatry, 2024, 14(1): 230.
23.	Hinks TSC, Brown T, Lau LCK, et al. Multidimensional endotyping in patients with severe asthma reveals inflammatory heterogeneity in matrix metalloproteinases and chitinase 3-like protein 1. J Allergy Clin Immunol, 2016, 138(1): 61-75.
24.	Myint AM, Kim YK, Verkerk R, et al. Kynurenine pathway in major depression: evidence of impaired neuroprotection. J Affect Disord, 2007, 98(1/2): 143-151.
25.	Caspi A, Sugden K, Moffitt TE, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science, 2003, 301(5631): 386-389.
26.	Xie X, Zhu Y, Zhang J, et al. Association between Val66Met polymorphisms in brain-derived neurotrophic factor gene and asthma risk: a meta-analysis. Inflamm Res, 2015, 64(11): 875-883.
27.	Peter HL, Giglberger M, Frank J, et al. The association between genetic variability in the NPS/NPSR1 system and chronic stress responses: a gene-environment-(quasi-) experiment. Psychoneuroendocrinology, 2022, 144: 105883.
28.	He Q, Shen Z, Ren L, et al. Association of NPSR1 rs324981 polymorphism and treatment response to antidepressants in Chinese Han population with generalized anxiety disorder. Biochem Biophys Res Commun, 2018, 504(1): 137-142.
29.	Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
30.	Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence. Nature, 2023, 620(7972): 47-60.
31.	Khasha R, Sepehri MM, Mahdaviani SA. An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning. J Med Syst, 2019, 43(6): 158.
32.	Wu CP, Sleiman J, Fakhry B, et al. Novel machine learning identifies 5 asthma phenotypes using cluster analysis of real-world data. J Allergy Clin Immunol Pract, 2024, 12(8): 2084-2091.
33.	Coronato A, Naeem M, De Pietro G, et al. Reinforcement learning for intelligent healthcare applications: a survey. Artif Intell Med, 2020, 109: 101964.
34.	Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med, 2019, 49(9): 1426-1448.
35.	王兢, 尚志蕾, 劉偉志. 機器學習在心理衛生領域的應用. 海軍軍醫大學學報, 2023, 44(10): 1145-1153.
36.	Han W, Su Y, Wang X, et al. Altered resting-state brain activity in patients with major depression disorder and bipolar disorder: a regional homogeneity analysis. J Affect Disord, 2025, 379: 313-322.
37.	He Y, Pang Y, Yang W, et al. Development of a prediction model for suicidal ideation in patients with advanced cancer: a multicenter, real-world, pan-cancer study in China. Cancer Med, 2024, 13(12): e7439.
38.	位彥鴿, 秦士森, 劉榮勛, 等. 基于語音特征和隨機森林算法的小學生抑郁癥狀的識別研究. 中華實用兒科臨床雜志, 2024, 39(11): 853-857.
39.	Gokten ES, Uyulan C. Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. J Affect Disord, 2021, 279:256-265.
40.	Hu M, Shu X, Yu G, et al. A risk prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition: development and validation study. J Med Internet Res, 2021, 23(2): e20298.
41.	Mao L, Hong X, Hu M. Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task. J Affect Disord, 2024, 365: 9-20.
42.	Nogay HS, Adeli H. Multiple classification of brain MRI autism spectrum disorder by age and gender using deep learning. J Med Syst, 2024, 48(1): 15.
43.	Xie W, Wang C, Lin Z, et al. Multimodal fusion diagnosis of depression and anxiety based on CNN-LSTM model. Comput Med Imaging Graph, 2022, 102: 102128.
44.	孟祥輝. 基于多模態數據融合的精神分裂癥分類研究. 西安: 長安大學, 2021.
45.	Abuhantash F, Abu Hantash MK, AlShehhi A. Comorbidity-based framework for Alzheimer’s disease classification using graph neural networks. Sci Rep, 2024, 14(1): 21061.
46.	Joo H, Lee D, Lee SH, et al. Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method. BMC Pulm Med, 2023, 23(1): 196.
47.	Turcatel G, Xiao Y, Caveney S, et al. Predicting asthma exacerbations using machine learning models. Adv Ther, 2025, 42(1): 362-374.
48.	Rehmani F, Shaheen Q, Anwar M, et al. Depression detection with machine learning of structural and non-structural dual languages. Healthc Technol Lett, 2024, 11(4): 218-226.
49.	Zheng Y, Zhang C, Liu Y. Risk prediction models of depression in older adults with chronic diseases. J Affect Disord, 2024, 359:182-188.
50.	曹寧, 張慧如, 牛麗薇, 等. 利用多導睡眠監測構建失眠患者抑郁癥診斷模型. 中國神經精神疾病雜志, 2024, 50(11): 661-667.
51.	陳梅妹, 王洋, 雷黃偉, 等. 基于多種機器學習算法和語音情緒特征的閾下抑郁辨識模型構建. 南方醫科大學學報, 2025, 45(4): 711-717.
52.	Lin Y, Li C, Li H. Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey. J Affect Disord, 2025, 381: 573-583.
53.	Hu W, Liu BP, Jia CX. Association and biological pathways between lung function and incident depression: a prospective cohort study of 280, 032 participants. BMC Med, 2024, 22(1): 160.
54.	Hwang H, Jang JH, Lee E, et al. Prediction of the number of asthma patients using environmental factors based on deep learning algorithms. Respir Res, 2023, 24(1): 302.
55.	Cao T, Tian M, Hu H, et al. The relationship between air pollution and depression and anxiety disorders - a systematic evaluation and meta-analysis of a cohort-based study. Int J Soc Psychiatry, 2024, 70(2): 241-270.
56.	McMahon EM, Corcoran P, O’Regan G, et al. Physical activity in European adolescents and associations with anxiety, depression and well-being. Eur Child Adolesc Psychiatry, 2017, 26(1):111-122.
57.	McLoughlin RF, Clark VL, Urroz PD, et al. Increasing physical activity in severe asthma: a systematic review and meta-analysis. Eur Respir J, 2022, 60(6): 2200546.
58.	趙健, 崔騫, 石佳, 等. 基于文本和聲學特征的雙模態融合抑郁傾向識別算法. 計算機工程, 2024, 50(11): 49-58.
59.	Brumen B, ?ernezel A, Bo?njak L. Overview of machine learning process modelling. Entropy (Basel), 2021, 23(9): 1123.
60.	Huang X, Zhang XY. Development and validation of a prediction model for co-occurring moderate-to-severe anxiety symptoms in first-episode and drug na?ve patients with major depressive disorder. Depress Anxiety, 2024, 2024: 9950256.
61.	Zhou Y, Han W, Yao X, et al. Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: a cross-sectional observational study. Int J Nurs Stud, 2023, 146: 104562.
62.	Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. Psychoradiology, 2023, 3: kkad026.
63.	李錦瓏, 陳瓊瓊, 丁志杰, 等. 基于面部行為與語音融合特征的重度抑郁識別. 北京郵電大學學報, 2023, 46(1): 32-37.

1. Menzies-Gow A, Bafadhel M, Busse WW, et al. An expert consensus framework for asthma remission as a treatment goal.J Allergy Clin Immunol, 2020, 145(3): 757-765.
2. Ramsahai JM, Hansbro PM, Wark PAB. Mechanisms and management of asthma exacerbations. Am J Respir Crit Care Med, 2019, 199(4): 423-432.
3. Conway AE, Verdi M, Kartha N, et al. Allergic diseases and mental health. J Allergy Clin Immunol Pract, 2024, 12(9): 2298-2309.
4. Hohls JK, K?nig HH, Quirke E, et al. Anxiety, depression and quality of life-a systematic review of evidence from longitudinal observational studies. Int J Environ Res Public Health, 2021, 18(22): 12022.
5. Cazzola M, Rogliani P, Ora J, et al. Asthma and comorbidities: recent advances. Pol Arch Intern Med, 2022, 132(4): 16250.
6. Jiang M, Qin P, Yang X. Comorbidity between depression and asthma via immune-inflammatory pathways: a meta-analysis. J Affect Disord, 2014, 166: 22-29.
7. 張靜, 魏軍, 龔玉蕾. 支氣管哮喘患者合并焦慮/抑郁情緒的風險因素. 國際精神病學雜志, 2023, 50(5): 1125-1127, 1131.
8. 張洋, 王慧淵, 耿妍, 等. 支氣管哮喘兒童情緒問題現狀及相關因素分析. 中國兒童保健雜志, 2022, 30(12): 1400-1403, 1408.
9. Ye G, Baldwin DS, Hou R. Anxiety in asthma: a systematic review and meta-analysis. Psychol Med, 2021, 51(1): 11-20.
10. de Boer GM, Houweling L, Hendriks RW, et al. Asthma patients experience increased symptoms of anxiety, depression and fear during the COVID-19 pandemic. Chron Respir Dis, 2021, 18: 14799731211029658.
11. 陳建麗, 褚旭麗, 李雁, 等. 社會支持對支氣管哮喘患兒家長家庭親密度適應性及抑郁水平的影響. 中國婦幼保健, 2021, 36(8): 1865-1868.
12. Gao YH, Zhao HS, Zhang FR, et al. The relationship between depression and asthma: a meta-analysis of prospective studies. PLoS One, 2015, 10(7): e0132424.
13. Leonard SI, Turi ER, Powell JS, et al. Associations of asthma self-management and mental health in adolescents: a scoping review. Respir Med, 2022, 200: 106897.
14. Lehrer PM, Irvin CG, Lu SE, et al. Relationships among pulmonary function, anxiety and depression in mild asthma: an exploratory study. Biol Psychol, 2022, 168: 108244.
15. Lee S, Rhee DK. Effects of ginseng on stress-related depression, anxiety, and the hypothalamic-pituitary-adrenal axis. J Ginseng Res, 2017, 41(4): 589-594.
16. Plaza-González S, Zabala-Ba?os MDC, Astasio-Picado á, et al. Psychological and sociocultural determinants in childhood asthma disease: impact on quality of life. Int J Environ Res Public Health, 2022, 19(5): 2652.
17. 陳瓊琰, 戴元榮, 金晨慈, 等. 不同皮質醇水平哮喘患者哮喘相關指標及焦慮抑郁因素分析. 浙江醫學, 2019, 41(20): 2211-2214.
18. Dereix AE, Ledyard R, Redhunt AM, et al. Maternal anxiety and depression in pregnancy and DNA methylation of the NR3C1 glucocorticoid receptor gene. Epigenomics, 2021, 13(21):1701-1709.
19. Miller RL, Grayson MH, Strothman K. Advances in asthma: new understandings of asthma’s natural history, risk factors, underlying mechanisms, and clinical management. J Allergy Clin Immunol, 2021, 148(6): 1430-1441.
20. Zhu X, Cui J, Yi L, et al. The role of t cells and macrophages in asthma pathogenesis: a new perspective on mutual crosstalk. Mediators Inflamm, 2020, 2020: 7835284.
21. Thompson DA, Wabara YB, Duran S, et al. Single cell analysis identifies distinct CD4 ⁺T cells associated with the pathobiology of pediatric obesity related asthma. Sci Rep, 2025, 15(1): 6844.
22. Zonca V, Marizzoni M, Saleri S, et al. Inflammation and immune system pathways as biological signatures of adolescent depression-the IDEA-RiSCo study. Transl Psychiatry, 2024, 14(1): 230.
23. Hinks TSC, Brown T, Lau LCK, et al. Multidimensional endotyping in patients with severe asthma reveals inflammatory heterogeneity in matrix metalloproteinases and chitinase 3-like protein 1. J Allergy Clin Immunol, 2016, 138(1): 61-75.
24. Myint AM, Kim YK, Verkerk R, et al. Kynurenine pathway in major depression: evidence of impaired neuroprotection. J Affect Disord, 2007, 98(1/2): 143-151.
25. Caspi A, Sugden K, Moffitt TE, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science, 2003, 301(5631): 386-389.
26. Xie X, Zhu Y, Zhang J, et al. Association between Val66Met polymorphisms in brain-derived neurotrophic factor gene and asthma risk: a meta-analysis. Inflamm Res, 2015, 64(11): 875-883.
27. Peter HL, Giglberger M, Frank J, et al. The association between genetic variability in the NPS/NPSR1 system and chronic stress responses: a gene-environment-(quasi-) experiment. Psychoneuroendocrinology, 2022, 144: 105883.
28. He Q, Shen Z, Ren L, et al. Association of NPSR1 rs324981 polymorphism and treatment response to antidepressants in Chinese Han population with generalized anxiety disorder. Biochem Biophys Res Commun, 2018, 504(1): 137-142.
29. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
30. Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence. Nature, 2023, 620(7972): 47-60.
31. Khasha R, Sepehri MM, Mahdaviani SA. An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning. J Med Syst, 2019, 43(6): 158.
32. Wu CP, Sleiman J, Fakhry B, et al. Novel machine learning identifies 5 asthma phenotypes using cluster analysis of real-world data. J Allergy Clin Immunol Pract, 2024, 12(8): 2084-2091.
33. Coronato A, Naeem M, De Pietro G, et al. Reinforcement learning for intelligent healthcare applications: a survey. Artif Intell Med, 2020, 109: 101964.
34. Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med, 2019, 49(9): 1426-1448.
35. 王兢, 尚志蕾, 劉偉志. 機器學習在心理衛生領域的應用. 海軍軍醫大學學報, 2023, 44(10): 1145-1153.
36. Han W, Su Y, Wang X, et al. Altered resting-state brain activity in patients with major depression disorder and bipolar disorder: a regional homogeneity analysis. J Affect Disord, 2025, 379: 313-322.
37. He Y, Pang Y, Yang W, et al. Development of a prediction model for suicidal ideation in patients with advanced cancer: a multicenter, real-world, pan-cancer study in China. Cancer Med, 2024, 13(12): e7439.
38. 位彥鴿, 秦士森, 劉榮勛, 等. 基于語音特征和隨機森林算法的小學生抑郁癥狀的識別研究. 中華實用兒科臨床雜志, 2024, 39(11): 853-857.
39. Gokten ES, Uyulan C. Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. J Affect Disord, 2021, 279:256-265.
40. Hu M, Shu X, Yu G, et al. A risk prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition: development and validation study. J Med Internet Res, 2021, 23(2): e20298.
41. Mao L, Hong X, Hu M. Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task. J Affect Disord, 2024, 365: 9-20.
42. Nogay HS, Adeli H. Multiple classification of brain MRI autism spectrum disorder by age and gender using deep learning. J Med Syst, 2024, 48(1): 15.
43. Xie W, Wang C, Lin Z, et al. Multimodal fusion diagnosis of depression and anxiety based on CNN-LSTM model. Comput Med Imaging Graph, 2022, 102: 102128.
44. 孟祥輝. 基于多模態數據融合的精神分裂癥分類研究. 西安: 長安大學, 2021.
45. Abuhantash F, Abu Hantash MK, AlShehhi A. Comorbidity-based framework for Alzheimer’s disease classification using graph neural networks. Sci Rep, 2024, 14(1): 21061.
46. Joo H, Lee D, Lee SH, et al. Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method. BMC Pulm Med, 2023, 23(1): 196.
47. Turcatel G, Xiao Y, Caveney S, et al. Predicting asthma exacerbations using machine learning models. Adv Ther, 2025, 42(1): 362-374.
48. Rehmani F, Shaheen Q, Anwar M, et al. Depression detection with machine learning of structural and non-structural dual languages. Healthc Technol Lett, 2024, 11(4): 218-226.
49. Zheng Y, Zhang C, Liu Y. Risk prediction models of depression in older adults with chronic diseases. J Affect Disord, 2024, 359:182-188.
50. 曹寧, 張慧如, 牛麗薇, 等. 利用多導睡眠監測構建失眠患者抑郁癥診斷模型. 中國神經精神疾病雜志, 2024, 50(11): 661-667.
51. 陳梅妹, 王洋, 雷黃偉, 等. 基于多種機器學習算法和語音情緒特征的閾下抑郁辨識模型構建. 南方醫科大學學報, 2025, 45(4): 711-717.
52. Lin Y, Li C, Li H. Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey. J Affect Disord, 2025, 381: 573-583.
53. Hu W, Liu BP, Jia CX. Association and biological pathways between lung function and incident depression: a prospective cohort study of 280, 032 participants. BMC Med, 2024, 22(1): 160.
54. Hwang H, Jang JH, Lee E, et al. Prediction of the number of asthma patients using environmental factors based on deep learning algorithms. Respir Res, 2023, 24(1): 302.
55. Cao T, Tian M, Hu H, et al. The relationship between air pollution and depression and anxiety disorders - a systematic evaluation and meta-analysis of a cohort-based study. Int J Soc Psychiatry, 2024, 70(2): 241-270.
56. McMahon EM, Corcoran P, O’Regan G, et al. Physical activity in European adolescents and associations with anxiety, depression and well-being. Eur Child Adolesc Psychiatry, 2017, 26(1):111-122.
57. McLoughlin RF, Clark VL, Urroz PD, et al. Increasing physical activity in severe asthma: a systematic review and meta-analysis. Eur Respir J, 2022, 60(6): 2200546.
58. 趙健, 崔騫, 石佳, 等. 基于文本和聲學特征的雙模態融合抑郁傾向識別算法. 計算機工程, 2024, 50(11): 49-58.
59. Brumen B, ?ernezel A, Bo?njak L. Overview of machine learning process modelling. Entropy (Basel), 2021, 23(9): 1123.
60. Huang X, Zhang XY. Development and validation of a prediction model for co-occurring moderate-to-severe anxiety symptoms in first-episode and drug na?ve patients with major depressive disorder. Depress Anxiety, 2024, 2024: 9950256.
61. Zhou Y, Han W, Yao X, et al. Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: a cross-sectional observational study. Int J Nurs Stud, 2023, 146: 104562.
62. Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. Psychoradiology, 2023, 3: kkad026.
63. 李錦瓏, 陳瓊瓊, 丁志杰, 等. 基于面部行為與語音融合特征的重度抑郁識別. 北京郵電大學學報, 2023, 46(1): 32-37.

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