Treffer: Machine-based learning of multidimensional data in bipolar disorder - pilot results.
Title:
Machine-based learning of multidimensional data in bipolar disorder - pilot results.
Authors:
Birner A; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Mairinger M; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Elst C; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria., Maget A; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Fellendorf FT; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Platzer M; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Queissner R; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Lenger M; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Tmava-Berisha A; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Bengesser SA; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Reininghaus EZ; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria., Kreuzthaler M; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria., Dalkner N; Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria.
Source:
Bipolar disorders [Bipolar Disord] 2024 Jun; Vol. 26 (4), pp. 364-375. Date of Electronic Publication: 2024 Mar 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Munksgaard Country of Publication: Denmark NLM ID: 100883596 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1399-5618 (Electronic) Linking ISSN: 13985647 NLM ISO Abbreviation: Bipolar Disord Subsets: MEDLINE
Imprint Name(s):
Publication: Copenhagen : Wiley-Blackwell Munksgaard
Original Publication: Copenhagen, Denmark : Munksgaard, 1999-
Original Publication: Copenhagen, Denmark : Munksgaard, 1999-
MeSH Terms:
References:
Hirschfeld RMA, Lewis L, Vornik LA. Perceptions and impact of bipolar disorder: how far have we really come? Results of the national depressive and manic‐depressive association 2000 survey of individuals with bipolar disorder. J Clin Psychiatry. 2003;64(2):161‐174.
Baldessarini RJ, Tondo L, Baethge CJ, Lepri B, Bratti IM. Effects of treatment latency on response to maintenance treatment in manic‐depressive disorders. Bipolar Disord. 2007;9(4):386‐393. doi:10.1111/j.1399-5618.2007.00385.x.
Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1204‐1222. doi:10.1016/S0140-6736(20)30925-9.
Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age‐of‐onset distributions of DSM‐IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593‐602. doi:10.1001/archpsyc.62.6.593.
Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta‐analysis. JAMA Psychiatry. 2015;72(4):334‐341. doi:10.1001/jamapsychiatry.2014.2502.
Chan JKN, Tong CHY, Wong CSM, Chen EYH, Chang WC. Life expectancy and years of potential life lost in bipolar disorder: systematic review and meta‐analysis. Br J Psychiatry. 2022;221(3):567‐576. doi:10.1192/bjp.2022.19.
Ahmed GK, Elbeh K, Khalifa H, Samaan MR. Impact of duration of untreated illness in bipolar I disorder (manic episodes) on clinical outcome, socioecnomic burden in Egyptian population. Psychiatry Res. 2021;296:113659. doi:10.1016/j.psychres.2020.113659.
Altamura AC, Dell'Osso B, Berlin HA, Buoli M, Bassetti R, Mundo E. Duration of untreated illness and suicide in bipolar disorder: a naturalistic study. Eur Arch Psychiatry Clin Neurosci. 2010;260(5):385‐391. doi:10.1007/s00406-009-0085-2.
McCraw S, Parker G, Graham R, Synnott H, Mitchell PB. The duration of undiagnosed bipolar disorder: effect on outcomes and treatment response. J Affect Disord. 2014;168:422‐429. doi:10.1016/j.jad.2014.07.025.
Guo T, Yang Y, Zhao Q, et al. Prodromal symptoms of Chinese patients with bipolar disorder. J Affect Disord. 2021;294:908‐915. doi:10.1016/j.jad.2021.07.079.
Reininghaus EZ, McIntyre RS, Reininghaus B, et al. Tryptophan breakdown is increased in euthymic overweight individuals with bipolar disorder: a preliminary report. Bipolar Disord. 2014;16(4):432‐440. doi:10.1111/bdi.12166.
McIntyre RS, Danilewitz M, Liauw SS, et al. Bipolar disorder and metabolic syndrome: an international perspective. J Affect Disord. 2010;126(3):366‐387. doi:10.1016/j.jad.2010.04.012.
Vieta E, Reinares M, Rosa AR. Staging Bipolar Disorder. Neurotox Res. 2011;19(2):279‐285. doi:10.1007/s12640-010-9197-8.
Lackner N, Bengesser SA, Birner A, et al. Abdominal obesity is associated with impaired cognitive function in euthymic bipolar individuals. World J Biol Psychiatry. 2016;17(7):535‐546. doi:10.3109/15622975.2015.1046917.
Torres IJ, Boudreau VG, Yatham LN. Neuropsychological functioning in euthymic bipolar disorder: a meta‐analysis. Acta Psychiatr Scand Suppl. 2007;434:17‐26. doi:10.1111/j.1600-0447.2007.01055.x.
Cipriani G, Danti S, Carlesi C, Cammisuli DM, Di Fiorino M. Bipolar disorder and cognitive dysfunction: a complex link. J Nerv Ment Dis. 2017;205(10):743‐756. doi:10.1097/NMD.0000000000000720.
Martinez‐Aran A, Vieta E, Torrent C, et al. Functional outcome in bipolar disorder: the role of clinical and cognitive factors. Bipolar Disord. 2007;9(1–2):103‐113. doi:10.1111/j.1399-5618.2007.00327.x.
Macoveanu J, Freeman KO, Kjærstad HL, Knudsen GM, Kessing LV, Miskowiak KW. Structural brain abnormalities associated with cognitive impairments in bipolar disorder. Acta Psychiatr Scand. 2021;144(4):379‐391. doi:10.1111/acps.13349.
Salvadore G, Drevets WC, Henter ID, Zarate CA, Manji HK. Early intervention in bipolar disorder, part II: therapeutics. Early Interv Psychiatry. 2008;2(3):136‐146. doi:10.1111/j.1751-7893.2008.00072.x.
Frey BN, Andreazza AC, Ceresér KMM, et al. Effects of mood stabilizers on hippocampus BDNF levels in an animal model of mania. Life Sci. 2006;79(3):281‐286. doi:10.1016/j.lfs.2006.01.002.
Fellendorf FT, Manchia M, Squassina A, et al. Is poor lithium response in individuals with bipolar disorder associated with increased degradation of tryptophan along the kynurenine pathway? Results of an exploratory study. J Clin Med. 2022;11(9):2517. doi:10.3390/jcm11092517.
Wu M, Passos IC, Bauer IE, et al. Individualized identification of euthymic bipolar disorder using the Cambridge neuropsychological test automated battery (CANTAB) and machine learning. J Affect Disord. 2016;192:219‐225. doi:10.1016/j.jad.2015.12.053.
Jan Z, AI‐Ansari N, Mousa O, et al. The role of machine learning in diagnosing bipolar disorder: scoping review. J Med Internet Res. 2021;23(11):e29749. doi:10.2196/29749.
Sonkurt HO, Altınöz AE, Çimen E, Köşger F, Öztürk G. The role of cognitive functions in the diagnosis of bipolar disorder: a machine learning model. Int J Med Inform. 2021;145:104311. doi:10.1016/j.ijmedinf.2020.104311.
Elst C. Maschinelle Lernverfahren für multidimensionale Daten bei Bipolarer Störung ‐ Auswahl der besten Methoden und Pilotergebnisse. 2022.
Birner A, Bengesser SA, Seiler S, et al. Total gray matter volume is reduced in individuals with bipolar disorder currently treated with atypical antipsychotics. J Affect Disord. 2020;260:722‐727. doi:10.1016/j.jad.2019.09.068.
Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643‐662. doi:10.1037/h0054651.
Scarpina F, Tagini S. The Stroop color and word test. Front Psychol. 2017;8:557. doi:10.3389/fpsyg.2017.00557.
Woods SP, Delis DC, Scott JC, Kramer JH, Holdnack JA. The California verbal learning test – second edition: test‐retest reliability, practice effects, and reliable change indices for the standard and alternate forms. Arch Clin Neuropsychol. 2006;21(5):413‐420. doi:10.1016/j.acn.2006.06.002.
Brickenkamp R, Schmidt‐Atzert L, Liepmann D. Test d2 ‐ Revision Aufmerksamkeits und Konzentrationstest. 1st ed. Hogrefe; 2010.
Llinàs‐Reglà J, Vilalta‐Franch J, López‐Pousa S, Calvó‐Perxas L, Torrents Rodas D, Garre‐Olmo J. The trail making test. Assessment. 2017;24(2):183‐196. doi:10.1177/1073191115602552.
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit‐learn: machine learning in python. J Mach Learn Res. 2011;12:2825‐2830.
Kluyver T, Ragan‐Kelley B, Pérez F, et al. Jupyter Notebooks – a publishing format for reproducible computational workflows.In Positioning and Power in Academic Publishing: Players, Agents and Agendas In: Loizides, F. & Schmidt, B.IOS Press;2016:87‐90.
Pearson KLIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh Dublin Philo Maga J Sci. 1901;2(11):559‐572. doi:10.1080/14786440109462720.
Maaten L, Hinton G. Visualizing Data using t‐SNE. J Mach Learn Res. 2008;9(86):2579‐2605.
Cox DR. The regression analysis of binary sequences. J R Stat Soc B Methodol. 1958;20(2):215‐242.
Vapnik V, Chervonenkis A. Theory of Pattern Recognition [in Russian]. Nauka; 1974.
Freund Y, Schapire RE. A decision‐theoretic generalization of on‐line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119‐139. doi:10.1006/jcss.1997.1504.
Breiman L. Random forests. Mach Learn. 2001;45(1):5‐32. doi:10.1023/A:1010933404324.
Varma S, Simon R. Bias in error estimation when using cross‐validation for model selection. BMC Bioinform. 2006;7:91. doi:10.1186/1471-2105-7-91.
Raschka S, Mirjalili V. Python Machine Learning ‐ Third Edition. Packt Web site. Accessed October 19, 2022 https://www.packtpub.com/product/python‐machine‐learning‐third‐edition/9781789955750.
Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2004; 46,(1):389‐422 doi:10.1023/A:1012487302797.
Korobov M, Lopuhin K. ELI5. Accessed October 20, 2022 https://pypi.org/project/eli5/.
McGee S. Simplifying likelihood ratios. J Gen Intern Med. 2002;17(8):647‐650. doi:10.1046/j.1525-1497.2002.10750.x.
Baldessarini RJ, Tondo L, Baethge CJ, Lepri B, Bratti IM. Effects of treatment latency on response to maintenance treatment in manic‐depressive disorders. Bipolar Disord. 2007;9(4):386‐393. doi:10.1111/j.1399-5618.2007.00385.x.
Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1204‐1222. doi:10.1016/S0140-6736(20)30925-9.
Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age‐of‐onset distributions of DSM‐IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593‐602. doi:10.1001/archpsyc.62.6.593.
Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta‐analysis. JAMA Psychiatry. 2015;72(4):334‐341. doi:10.1001/jamapsychiatry.2014.2502.
Chan JKN, Tong CHY, Wong CSM, Chen EYH, Chang WC. Life expectancy and years of potential life lost in bipolar disorder: systematic review and meta‐analysis. Br J Psychiatry. 2022;221(3):567‐576. doi:10.1192/bjp.2022.19.
Ahmed GK, Elbeh K, Khalifa H, Samaan MR. Impact of duration of untreated illness in bipolar I disorder (manic episodes) on clinical outcome, socioecnomic burden in Egyptian population. Psychiatry Res. 2021;296:113659. doi:10.1016/j.psychres.2020.113659.
Altamura AC, Dell'Osso B, Berlin HA, Buoli M, Bassetti R, Mundo E. Duration of untreated illness and suicide in bipolar disorder: a naturalistic study. Eur Arch Psychiatry Clin Neurosci. 2010;260(5):385‐391. doi:10.1007/s00406-009-0085-2.
McCraw S, Parker G, Graham R, Synnott H, Mitchell PB. The duration of undiagnosed bipolar disorder: effect on outcomes and treatment response. J Affect Disord. 2014;168:422‐429. doi:10.1016/j.jad.2014.07.025.
Guo T, Yang Y, Zhao Q, et al. Prodromal symptoms of Chinese patients with bipolar disorder. J Affect Disord. 2021;294:908‐915. doi:10.1016/j.jad.2021.07.079.
Reininghaus EZ, McIntyre RS, Reininghaus B, et al. Tryptophan breakdown is increased in euthymic overweight individuals with bipolar disorder: a preliminary report. Bipolar Disord. 2014;16(4):432‐440. doi:10.1111/bdi.12166.
McIntyre RS, Danilewitz M, Liauw SS, et al. Bipolar disorder and metabolic syndrome: an international perspective. J Affect Disord. 2010;126(3):366‐387. doi:10.1016/j.jad.2010.04.012.
Vieta E, Reinares M, Rosa AR. Staging Bipolar Disorder. Neurotox Res. 2011;19(2):279‐285. doi:10.1007/s12640-010-9197-8.
Lackner N, Bengesser SA, Birner A, et al. Abdominal obesity is associated with impaired cognitive function in euthymic bipolar individuals. World J Biol Psychiatry. 2016;17(7):535‐546. doi:10.3109/15622975.2015.1046917.
Torres IJ, Boudreau VG, Yatham LN. Neuropsychological functioning in euthymic bipolar disorder: a meta‐analysis. Acta Psychiatr Scand Suppl. 2007;434:17‐26. doi:10.1111/j.1600-0447.2007.01055.x.
Cipriani G, Danti S, Carlesi C, Cammisuli DM, Di Fiorino M. Bipolar disorder and cognitive dysfunction: a complex link. J Nerv Ment Dis. 2017;205(10):743‐756. doi:10.1097/NMD.0000000000000720.
Martinez‐Aran A, Vieta E, Torrent C, et al. Functional outcome in bipolar disorder: the role of clinical and cognitive factors. Bipolar Disord. 2007;9(1–2):103‐113. doi:10.1111/j.1399-5618.2007.00327.x.
Macoveanu J, Freeman KO, Kjærstad HL, Knudsen GM, Kessing LV, Miskowiak KW. Structural brain abnormalities associated with cognitive impairments in bipolar disorder. Acta Psychiatr Scand. 2021;144(4):379‐391. doi:10.1111/acps.13349.
Salvadore G, Drevets WC, Henter ID, Zarate CA, Manji HK. Early intervention in bipolar disorder, part II: therapeutics. Early Interv Psychiatry. 2008;2(3):136‐146. doi:10.1111/j.1751-7893.2008.00072.x.
Frey BN, Andreazza AC, Ceresér KMM, et al. Effects of mood stabilizers on hippocampus BDNF levels in an animal model of mania. Life Sci. 2006;79(3):281‐286. doi:10.1016/j.lfs.2006.01.002.
Fellendorf FT, Manchia M, Squassina A, et al. Is poor lithium response in individuals with bipolar disorder associated with increased degradation of tryptophan along the kynurenine pathway? Results of an exploratory study. J Clin Med. 2022;11(9):2517. doi:10.3390/jcm11092517.
Wu M, Passos IC, Bauer IE, et al. Individualized identification of euthymic bipolar disorder using the Cambridge neuropsychological test automated battery (CANTAB) and machine learning. J Affect Disord. 2016;192:219‐225. doi:10.1016/j.jad.2015.12.053.
Jan Z, AI‐Ansari N, Mousa O, et al. The role of machine learning in diagnosing bipolar disorder: scoping review. J Med Internet Res. 2021;23(11):e29749. doi:10.2196/29749.
Sonkurt HO, Altınöz AE, Çimen E, Köşger F, Öztürk G. The role of cognitive functions in the diagnosis of bipolar disorder: a machine learning model. Int J Med Inform. 2021;145:104311. doi:10.1016/j.ijmedinf.2020.104311.
Elst C. Maschinelle Lernverfahren für multidimensionale Daten bei Bipolarer Störung ‐ Auswahl der besten Methoden und Pilotergebnisse. 2022.
Birner A, Bengesser SA, Seiler S, et al. Total gray matter volume is reduced in individuals with bipolar disorder currently treated with atypical antipsychotics. J Affect Disord. 2020;260:722‐727. doi:10.1016/j.jad.2019.09.068.
Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643‐662. doi:10.1037/h0054651.
Scarpina F, Tagini S. The Stroop color and word test. Front Psychol. 2017;8:557. doi:10.3389/fpsyg.2017.00557.
Woods SP, Delis DC, Scott JC, Kramer JH, Holdnack JA. The California verbal learning test – second edition: test‐retest reliability, practice effects, and reliable change indices for the standard and alternate forms. Arch Clin Neuropsychol. 2006;21(5):413‐420. doi:10.1016/j.acn.2006.06.002.
Brickenkamp R, Schmidt‐Atzert L, Liepmann D. Test d2 ‐ Revision Aufmerksamkeits und Konzentrationstest. 1st ed. Hogrefe; 2010.
Llinàs‐Reglà J, Vilalta‐Franch J, López‐Pousa S, Calvó‐Perxas L, Torrents Rodas D, Garre‐Olmo J. The trail making test. Assessment. 2017;24(2):183‐196. doi:10.1177/1073191115602552.
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit‐learn: machine learning in python. J Mach Learn Res. 2011;12:2825‐2830.
Kluyver T, Ragan‐Kelley B, Pérez F, et al. Jupyter Notebooks – a publishing format for reproducible computational workflows.In Positioning and Power in Academic Publishing: Players, Agents and Agendas In: Loizides, F. & Schmidt, B.IOS Press;2016:87‐90.
Pearson KLIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh Dublin Philo Maga J Sci. 1901;2(11):559‐572. doi:10.1080/14786440109462720.
Maaten L, Hinton G. Visualizing Data using t‐SNE. J Mach Learn Res. 2008;9(86):2579‐2605.
Cox DR. The regression analysis of binary sequences. J R Stat Soc B Methodol. 1958;20(2):215‐242.
Vapnik V, Chervonenkis A. Theory of Pattern Recognition [in Russian]. Nauka; 1974.
Freund Y, Schapire RE. A decision‐theoretic generalization of on‐line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119‐139. doi:10.1006/jcss.1997.1504.
Breiman L. Random forests. Mach Learn. 2001;45(1):5‐32. doi:10.1023/A:1010933404324.
Varma S, Simon R. Bias in error estimation when using cross‐validation for model selection. BMC Bioinform. 2006;7:91. doi:10.1186/1471-2105-7-91.
Raschka S, Mirjalili V. Python Machine Learning ‐ Third Edition. Packt Web site. Accessed October 19, 2022 https://www.packtpub.com/product/python‐machine‐learning‐third‐edition/9781789955750.
Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2004; 46,(1):389‐422 doi:10.1023/A:1012487302797.
Korobov M, Lopuhin K. ELI5. Accessed October 20, 2022 https://pypi.org/project/eli5/.
McGee S. Simplifying likelihood ratios. J Gen Intern Med. 2002;17(8):647‐650. doi:10.1046/j.1525-1497.2002.10750.x.
Contributed Indexing:
Keywords: bipolar disorder; cognition; machine learning
Entry Date(s):
Date Created: 20240326 Date Completed: 20240610 Latest Revision: 20240610
Update Code:
20250114
DOI:
10.1111/bdi.13426
PMID:
38531635
Database:
MEDLINE
Weitere Informationen
Introduction: Owing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as a whole in individuals with bipolar disorder. Therefore, we hypothesize that implementing machine learning techniques can be used to support the diagnostic process of bipolar disorder and minimize misdiagnosis rates.
Materials and Methods: To test this hypothesis, a de-identified data set of only demographic information and the results of cognitive tests of 196 patients with bipolar disorder and 145 healthy controls was used to train and compare five different machine learning algorithms.
Results: The best performing algorithm was logistic regression, with a macro-average F1-score of 0.69 [95% CI 0.66-0.73]. After further optimization, a model with an improved macro-average F1-score of 0.75, a micro-average F1-score of 0.77, and an AUROC of 0.84 was built. Furthermore, the individual amount of contribution per variable on the classification was assessed, which revealed that body mass index, results of the Stroop test, and the d2-R test alone allow for a classification of bipolar disorder with equal performance.
Conclusion: Using these data for clinical application results in an acceptable performance, but has not yet reached a state where it can sufficiently augment a diagnosis made by an experienced clinician. Therefore, further research should focus on identifying variables with the highest amount of contribution to a model's classification.
(© 2024 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)