Bipolar disorder is a multifaceted psychiatric illness characterized by unpredictable mood episodes and highly variable treatment responses across individuals. Predicting response to specific pharmacological treatments remains a key challenge in personalized psychiatry. This study aims to develop predictive models for treatment response subtypes—non-responders, lithium responders, and anticonvulsant responders—using a diverse array of biomarkers, including genetic variants, serum levels, neuroimaging-derived features, and clinical history. A dataset of 2000 patients was analyzed, containing 31 features spanning single nucleotide polymorphisms (SNPs), inflammatory and neurochemical markers, structural and functional brain imaging variables, and illness course descriptors. Initial exploratory data analysis revealed two variables with missing values, and class imbalance across response types. Correlation analysis highlighted strong associations between GABA, DLPFC_connectivity, and treatment outcomes. Dimensionality reduction with UMAP illustrated overlapping distributions among classes, justifying the need for non-linear classifiers. Five models—logistic regression, SVM, random forest, XGBoost, and a deep neural network—were trained and evaluated. The deep learning model achieved the highest validation accuracy (46%) and ROC AUC (0.65). Feature importance analysis across models identified BDNF_serum, COMT_Val158Met, and DLPFC_connectivity as top contributors. Despite comparable performance among classical models, deep learning showed superior generalization and interpretability through its learning curve. Our findings underscore the feasibility of integrating multi-modal biomarkers and deep learning for accurate stratification of bipolar disorder treatment response. The results support the future development of decision-support tools that incorporate genetic, proteomic, and neurobiological data to guide personalized psychiatry. Future work will include external validation, imputation strategies, and further interpretability using SHAP values.
Cite this paper
Filippis, R. D. and Foysal, A. A. (2025). Predicting Treatment Response in Bipolar Disorder Using Biomarker Profiles and Machine Learning Models. Open Access Library Journal, 12, e13871. doi: http://dx.doi.org/10.4236/oalib.1113871.
Ferrari, A.J., Stockings, E., Khoo, J., Erskine, H.E., Degenhardt, L., Vos, T., et al. (2016) The Prevalence and Burden of Bipolar Disorder: Findings from the Global Burden of Disease Study 2013. Bipolar Disorders, 18, 440-450. https://doi.org/10.1111/bdi.12423
Perugi, G., De Rossi, P., Fagiolini, A., Girardi, P., Maina, G., Sani, G., et al. (2019) Personalized and Precision Medicine as Informants for Treatment Management of Bipolar Disorder. International Clinical Psychopharmacology, 34, 189-205. https://doi.org/10.1097/yic.0000000000000260
Nayak, R., Rosh, I., Kustanovich, I. and Stern, S. (2021) Mood Stabilizers in Psychiatric Disorders and Mechanisms Learnt from in Vitro Model Systems. International Journal of Molecular Sciences, 22, Article 9315. https://doi.org/10.3390/ijms22179315
Yocum, A.K. and Singh, B. (2025) Global Trends in the Use of Pharmacotherapy for the Treatment of Bipolar Disorder. Current Psychiatry Reports, 2025, 1-9.
Gatchel, R.J. and Gardea, M.A. (1999) Psychosocial Issues: Their Importance in Predicting Disability, Response to Treatment, and Search for Compensation. Neurologic Clinics, 17, 149-166. https://doi.org/10.1016/s0733-8619(05)70119-5
Iwashyna, T.J., Ely, E.W., Smith, D.M. and Langa, K.M. (2010) Long-Term Cognitive Impairment and Functional Disability among Survivors of Severe Sepsis. Journal of the American Medical Association, 304, 1787-1794. https://doi.org/10.1001/jama.2010.1553
Koudriavtseva, T., Onesti, E., Pestalozza, I.F., Sperduti, I. and Jandolo, B. (2011) The Importance of Physician-Patient Relationship for Improvement of Adherence to Long-Term Therapy: Data of Survey in a Cohort of Multiple Sclerosis Patients with Mild and Moderate Disability. Neurological Sciences, 33, 575-584. https://doi.org/10.1007/s10072-011-0776-0
Iuga, A.O. and McGuire, M.J. (2014) Adherence and Health Care Costs. Risk Management and Healthcare Policy, 2014, 35-44. https://doi.org/10.2147/rmhp.s19801
Newman, F.L. and Howard, K.I. (1986) Therapeutic Effort, Treatment Outcome, and National Health Policy. American Psychologist, 41, 181-187. https://doi.org/10.1037//0003-066x.41.2.181
Zanardi, R., Prestifilippo, D., Fabbri, C., Colombo, C., Maron, E. and Serretti, A. (2021) Precision Psychiatry in Clinical Practice. International Journal of Psychiatry in Clinical Practice, 25, 19-27. https://doi.org/10.1080/13651501.2020.1809680
Williams, L.M., Carpenter, W.T., Carretta, C., Papanastasiou, E. and Vaidyanathan, U. (2024) Precision Psychiatry and Research Domain Criteria: Implications for Clinical Trials and Future Practice. CNS Spectrums, 29, 26-39. https://doi.org/10.1017/s1092852923002420
Abi-Dargham, A., Moeller, S.J., Ali, F., DeLorenzo, C., Domschke, K., Horga, G., et al. (2023) Candidate Biomarkers in Psychiatric Disorders: State of the Field. World Psychiatry, 22, 236-262. https://doi.org/10.1002/wps.21078
Chiu, F.Y. and Yen, Y. (2023) Imaging Biomarkers for Clinical Applications in Neuro-Oncology: Current Status and Future Perspectives. Biomarker Research, 11, Article No. 35. https://doi.org/10.1186/s40364-023-00476-7
Saykin, A.J., de Ruiter, M.B., McDonald, B.C., Deprez, S. and Silverman, D.H.S. (2013) Neuroimaging Biomarkers and Cognitive Function in Non-CNS Cancer and Its Treatment: Current Status and Recommendations for Future Research. Brain Imaging and Behavior, 7, 363-373. https://doi.org/10.1007/s11682-013-9283-7
Kang, S.G. and Cho, S.E. (2020) Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder. International Journal of Molecular Sciences, 21, Article 2148. https://doi.org/10.3390/ijms21062148
Du, W. and Elemento, O. (2015) Cancer Systems Biology: Embracing Complexity to Develop Better Anticancer Therapeutic Strategies. Oncogene, 34, 3215-3225. https://doi.org/10.1038/onc.2014.291
Yue, R. and Dutta, A. (2022) Computational Systems Biology in Disease Modeling and Control, Review and Perspectives. npj Systems Biology and Applications, 8, Article No. 37. https://doi.org/10.1038/s41540-022-00247-4
Cappuccio, A., Tieri, P. and Castiglione, F. (2015) Multiscale Modelling in Immunology: A Review. Briefings in Bioinformatics, 17, 408-418. https://doi.org/10.1093/bib/bbv012
Liu, F. and Panagiotakos, D. (2022) Real-World Data: A Brief Review of the Methods, Applications, Challenges and Opportunities. BMC Medical Research Methodology, 22, Article No. 287. https://doi.org/10.1186/s12874-022-01768-6
Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A. and Caldarelli, G. (2019) The Statistical Physics of Real-World Networks. Nature Reviews Physics, 1, 58-71. https://doi.org/10.1038/s42254-018-0002-6
Wilson, A. and Anwar, M.R. (2024) The Future of Adaptive Machine Learning Algorithms in High-Dimensional Data Processing. International Transactions on Artificial Intelligence, 3, 97-107. https://doi.org/10.33050/italic.v3i1.656
Wei, L., Niraula, D., Gates, E.D.H., Fu, J., Luo, Y., Nyflot, M.J., et al. (2023) Artificial Intelligence (AI) and Machine Learning (ML) in Precision Oncology: A Review on Enhancing Discoverability through Multiomics Integration. The British Journal of Radiology, 96, Article 20230211. https://doi.org/10.1259/bjr.20230211
Mahmud, M., Kaiser, M.S., McGinnity, T.M. and Hussain, A. (2021) Deep Learning in Mining Biological Data. Cognitive Computation, 13, 1-33. https://doi.org/10.1007/s12559-020-09773-x
Ruiz-Torres, D.A., Bryan, M.E., Hirayama, S., Merkin, R.D., Luciani, E., Roberts, T.J., et al. (2025) Spatial Characterization of Tertiary Lymphoid Structures as Predictive Biomarkers for Immune Checkpoint Blockade in Head and Neck Squamous Cell Carcinoma. OncoImmunology, 14, Article 2466308. https://doi.org/10.1080/2162402x.2025.2466308
Cearns, M., Amare, A.T., Schubert, K.O., Thalamuthu, A., et al. (2022) Using Polygenic Scores and Clinical Data for Bipolar Disorder Patient Stratification and Lithium Response Prediction: Machine Learning Approach. The British Journal of Psychiatry, 220, 219-228.
de Vries, L.P., van de Weijer, M.P. and Bartels, M. (2022) The Human Physiology of Well-Being: A Systematic Review on the Association between Neurotransmitters, Hormones, Inflammatory Markers, the Microbiome and Well-Being. Neuroscience & Biobehavioral Reviews, 139, Article 104733. https://doi.org/10.1016/j.neubiorev.2022.104733
Henson, R.N., Greve, A., Cooper, E., Gregori, M., Simons, J.S., Geerligs, L., et al. (2016) The Effects of Hippocampal Lesions on MRI Measures of Structural and Functional Connectivity. Hippocampus, 26, 1447-1463. https://doi.org/10.1002/hipo.22621
O’Doherty, D.C.M., Chitty, K.M., Saddiqui, S., Bennett, M.R. and Lagopoulos, J. (2015) A Systematic Review and Meta-Analysis of Magnetic Resonance Imaging Measurement of Structural Volumes in Posttraumatic Stress Disorder. Psychiatry Research: Neuroimaging, 232, 1-33. https://doi.org/10.1016/j.pscychresns.2015.01.002
McInnes, L., Healy, J., Saul, N. and Großberger, L. (2018) UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software, 3, Article 861. https://doi.org/10.21105/joss.00861
Allaoui, M., Kherfi, M.L. and Cheriet, A. (2020) Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study. In: Lecture Notes in Computer Science, Springer, 317-325. https://doi.org/10.1007/978-3-030-51935-3_34
Schmitz, S., Weidner, U., Hammer, H. and Thiele, A. (2021) Evaluating Uniform Manifold Approximation and Projection for Dimension Reduction and Visualization of Polinsar Features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 39-46. https://doi.org/10.5194/isprs-annals-v-1-2021-39-2021
Zou, K.H., Tuncali, K. and Silverman, S.G. (2003) Correlation and Simple Linear Regression. Radiology, 227, 617-628. https://doi.org/10.1148/radiol.2273011499
Shi, R. and Conrad, S.A. (2009) Correlation and Regression Analysis. Annals of Allergy, Asthma & Immunology, 103, S35-S41. https://doi.org/10.1016/s1081-1206(10)60820-4
Milošević, D., Medeiros, A.S., Stojković Piperac, M., Cvijanović, D., Soininen, J., Milosavljević, A., et al. (2022) The Application of Uniform Manifold Approximation and Projection (UMAP) for Unconstrained Ordination and Classification of Biological Indicators in Aquatic Ecology. Science of the Total Environment, 815, Article 152365. https://doi.org/10.1016/j.scitotenv.2021.152365
Vermeulen, M., Smith, K., Eremin, K., Rayner, G. and Walton, M. (2021) Application of Uniform Manifold Approximation and Projection (UMAP) in Spectral Imaging of Artworks. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 252, Article 119547. https://doi.org/10.1016/j.saa.2021.119547
Panagiotidou, A. (2024) Exploring the Enhancement of Predictive Accuracy for Minority Classes in Travel Mode Choice Models. PhD Dissertation, Delft University of Technology.
Erbani, J., Portier, P., Egyed-Zsigmond, E. and Nurbakova, D. (2024) Confusion Matrices: A Unified Theory. IEEE Access, 12, 181372-181419. https://doi.org/10.1109/access.2024.3507199
Phillips, G., Teixeira, H., Kelly, M.G., Salas Herrero, F., Várbíró, G., Lyche Solheim, A., et al. (2024) Setting Nutrient Boundaries to Protect Aquatic Communities: The Importance of Comparing Observed and Predicted Classifications Using Measures Derived from a Confusion Matrix. Science of the Total Environment, 912, Article 168872. https://doi.org/10.1016/j.scitotenv.2023.168872
Kerrigan, G., Smyth, P. and Steyvers, M. (2021) Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. Advances in Neural Information Processing Systems, 34, 4421-4434.
Rahmatinejad, Z., Dehghani, T., Hoseini, B., Rahmatinejad, F., Lotfata, A., Reihani, H., et al. (2024) A Comparative Study of Explainable Ensemble Learning and Logistic Regression for Predicting In-Hospital Mortality in the Emergency Department. Scientific Reports, 14, Article No. 3406. https://doi.org/10.1038/s41598-024-54038-4
Alangari, N., El Bachir Menai, M., Mathkour, H. and Almosallam, I. (2023) Exploring Evaluation Methods for Interpretable Machine Learning: A Survey. Information, 14, Article 469. https://doi.org/10.3390/info14080469
Reil, J.P.C. (2024) Beyond Generalized Linear Models: Advancing Insurance Pricing through Interpretable and Explainable Machine Learning. Master’s Thesis, University of Twente.
Shipe, M.E., Deppen, S.A., Farjah, F. and Grogan, E.L. (2019) Developing Prediction Models for Clinical Use Using Logistic Regression: An Overview. Journal of Thoracic Disease, 11, S574-S584. https://doi.org/10.21037/jtd.2019.01.25
Westreich, D., Lessler, J. and Funk, M.J. (2010) Propensity Score Estimation: Neural Networks, Support Vector Machines, Decision Trees (CART), and Meta-Classifiers as Alternatives to Logistic Regression. Journal of Clinical Epidemiology, 63, 826-833. https://doi.org/10.1016/j.jclinepi.2009.11.020
Budimir, M.E.A., Atkinson, P.M. and Lewis, H.G. (2015) A Systematic Review of Landslide Probability Mapping Using Logistic Regression. Landslides, 12, 419-436. https://doi.org/10.1007/s10346-014-0550-5
Fratello, M. and Tagliaferri, R. (2018) Decision Trees and Random Forests. In: Encyclopedia of Bioinformatics and Computational Biology, Elsevier, 374-383. https://doi.org/10.1016/b978-0-12-809633-8.20337-3
Meghana, P., Annepu, V., Jweeg, M.J., Bagadi, K., Aljibori, H.S.S., Mohammed, M.N., et al. (2024) Analysis of Neural Network Algorithm in Comparison to Multiple Linear Regression and Random Forest Algorithm. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, Manama, 28-29 January 2024, 437-443. https://doi.org/10.1109/icetsis61505.2024.10459496
Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R. and Khovanova, N. (2019) Decision Tree and Random Forest Models for Outcome Prediction in Antibody Incompatible Kidney Transplantation. Biomedical Signal Processing and Control, 52, 456-462. https://doi.org/10.1016/j.bspc.2017.01.012
DeGroat, W., Abdelhalim, H., Patel, K., Mendhe, D., Zeeshan, S. and Ahmed, Z. (2024) Discovering Biomarkers Associated and Predicting Cardiovascular Disease with High Accuracy Using a Novel Nexus of Machine Learning Techniques for Precision Medicine. Scientific Reports, 14, Article No. 1. https://doi.org/10.1038/s41598-023-50600-8
Gelir, F., Akan, T., Alp, S., Gecili, E., Bhuiyan, M.S., Disbrow, E.A., et al. (2025) Machine Learning Approaches for Predicting Progression to Alzheimer’s Disease in Patients with Mild Cognitive Impairment. Journal of Medical and Biological Engineering, 45, 63-83. https://doi.org/10.1007/s40846-024-00918-z
Christodoulou, E., Ma, J., Collins, G.S., Steyerberg, E.W., Verbakel, J.Y. and Van Calster, B. (2019) A Systematic Review Shows No Performance Benefit of Machine Learning over Logistic Regression for Clinical Prediction Models. Journal of Clinical Epidemiology, 110, 12-22. https://doi.org/10.1016/j.jclinepi.2019.02.004
Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I. and de Mendonça, A. (2011) Data Mining Methods in the Prediction of Dementia: A Real-Data Comparison of the Accuracy, Sensitivity and Specificity of Linear Discriminant Analysis, Logistic Regression, Neural Networks, Support Vector Machines, Classification Trees and Random Forests. BMC Research Notes, 4, Article No. 299. https://doi.org/10.1186/1756-0500-4-299
Payrovnaziri, S.N., Chen, Z., Rengifo-Moreno, P., Miller, T., Bian, J., Chen, J.H., et al. (2020) Explainable Artificial Intelligence Models Using Real-World Electronic Health Record Data: A Systematic Scoping Review. Journal of the American Medical Informatics Association, 27, 1173-1185. https://doi.org/10.1093/jamia/ocaa053
Chattopadhyay, S., Barman, S. and Lakshmi, D. (2025) The Role of Explainable AI for Healthcare 5.0. In: Edge AI for Industry 5.0 and Healthcare 5.0 Applications, Auerbach Publications, 45-80. https://doi.org/10.1201/9781003442066-5
Nasarian, E., Alizadehsani, R., Acharya, U.R. and Tsui, K. (2024) Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework. Information Fusion, 108, Article 102412. https://doi.org/10.1016/j.inffus.2024.102412
