全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

A Machine Learning Approach to Predicting Treatment Outcomes in Bipolar Depression with OCD Comorbidity

DOI: 10.4236/oalib.1112894, PP. 1-20

Subject Areas: Computer Engineering, Psychiatry & Psychology, Artificial Intelligence

Keywords: Bipolar Depression, Obsessive-Compulsive Disorder (OCD), Machine Learning (ML), XGBoost, Treatment Response Prediction, SHAP (SHapley Ad-ditive exPlanations)

Full-Text   Cite this paper   Add to My Lib

Abstract

Bipolar depression with comorbid obsessive-compulsive disorder (OCD) presents a significant clinical challenge due to its complex symptomatology, unpredictable treatment responses, and high relapse rates. Traditional ap-proaches to treatment planning lack reliable tools for predicting pa-tient-specific outcomes, leaving clinicians with limited options for personal-izing care. This study leverages advanced machine learning (ML), specifical-ly XGBoost, to develop a predictive framework capable of classifying treat-ment responses while identifying key predictors such as age, clinical scores (HDRS, YBOCS), and treatment characteristics (quetiapine dose). By incor-porating interpretability techniques such as SHAP (SHapley Additive exPla-nations), the model provides transparent insights into how individual fea-tures influence predictions, making the outputs actionable for clinical deci-sion-making. Furthermore, probabilistic predictions are evaluated and cali-brated using isotonic regression to ensure reliability, particularly for high-stakes applications in psychiatry. Through detailed visual analyses, in-cluding confusion matrices, ROC-AUC curves, SHAP plots, and calibration curves, this research bridges the gap between data-driven methodologies and clinical practice, offering a robust framework for advancing personalized treatment strategies in bipolar depression with OCD comorbidity.

 Cite this paper

Filippis, R. D. and Foysal, A. A. (2025). A Machine Learning Approach to Predicting Treatment Outcomes in Bipolar Depression with OCD Comorbidity. Open Access Library Journal, 12, e2894. doi: http://dx.doi.org/10.4236/oalib.1112894.

References

[1]  Fineberg, N.A., Hollander, E., Pallanti, S., Walitza, S., Grünblatt, E., Dell’Osso, B.M., et al. (2020) Clinical Advances in Obsessive-Compulsive Disorder: A Position Statement by the International College of Obsessive-Compulsive Spectrum Disorders. International Clinical Psychopharmacology, 35, 173-193.
https://doi.org/10.1097/yic.0000000000000314
[2]  Brady, K.T., Killeen, T.K., Brewerton, T. and Lucerini, S. (2000) Comorbidity of Psychiatric Disorders and Posttraumatic Stress Disorder. Journal of Clinical Psychiatry, 61, 22-32.
[3]  McIntyre, R.S., Alda, M., Baldessarini, R.J., Bauer, M., Berk, M., Correll, C.U., et al. (2022) The Clinical Characterization of the Adult Patient with Bipolar Disorder Aimed at Personalization of Management. World Psychiatry, 21, 364-387.
https://doi.org/10.1002/wps.20997
[4]  Attiullah, N., Eisen, J.L. and Rasmussen, S.A. (2000) Clinical Features of Obsessive-Compulsive Disorder. Psychiatric Clinics of North America, 23, 469-491.
https://doi.org/10.1016/s0193-953x(05)70175-1
[5]  Bauer, M., Severus, E., Moller, H. and Young, A.H. (2017) Pharmacological Treatment of Unipolar Depressive Disorders: Summary of WFSBP Guidelines. International Journal of Psychiatry in Clinical Practice, 21, 166-176.
https://doi.org/10.1080/13651501.2017.1306082
[6]  Nemeroff, C.B. (2007) The Burden of Severe Depression: A Review of Diagnostic Challenges and Treatment Alternatives. Journal of Psychiatric Research, 41, 189-206.
https://doi.org/10.1016/j.jpsychires.2006.05.008
[7]  Dawoodbhoy, F.M., Delaney, J., Cecula, P., Yu, J., Peacock, I., Tan, J., et al. (2021) AI in Patient Flow: Applications of Artificial Intelligence to Improve Patient Flow in NHS Acute Mental Health Inpatient Units. Heliyon, 7, e06993.
https://doi.org/10.1016/j.heliyon.2021.e06993
[8]  Squires, M., Tao, X., Elangovan, S., Gururajan, R., Zhou, X., Acharya, U.R., et al. (2023) Deep Learning and Machine Learning in Psychiatry: A Survey of Current Progress in Depression Detection, Diagnosis and Treatment. Brain Informatics, 10, Article No. 10.
https://doi.org/10.1186/s40708-023-00188-6
[9]  Fineberg, N.A., Reghunandanan, S., Simpson, H.B., Phillips, K.A., Richter, M.A., Matthews, K., et al. (2015) Obsessive-Compulsive Disorder (OCD): Practical Strategies for Pharmacological and Somatic Treatment in Adults. Psychiatry Research, 227, 114-125.
https://doi.org/10.1016/j.psychres.2014.12.003
[10]  Sunny, M.N.M., Saki, M.B.H., Nahian, A.A., Ahmed, S.W., Shorif, M.N., Atayeva, J., et al. (2024) Optimizing Healthcare Outcomes through Data-Driven Predictive Modeling. Journal of Intelligent Learning Systems and Applications, 16, 384-402.
https://doi.org/10.4236/jilsa.2024.164019
[11]  Inoue, T., Ichikawa, D., Ueno, T., Cheong, M., Inoue, T., Whetstone, W.D., et al. (2020) XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Reports, 1, 8-16.
https://doi.org/10.1089/neur.2020.0009
[12]  Kalusivalingam, A.K., Sharma, A., Patel, N. and Singh, V. (2021) Leveraging SHAP and LIME for Enhanced Explainability in AI-Driven Diagnostic Systems. International Journal of AI and ML, 2, 1-23.
[13]  Staffa, S.J. and Zurakowski, D. (2021) Statistical Development and Validation of Clinical Prediction Models. Anesthesiology, 135, 396-405.
https://doi.org/10.1097/aln.0000000000003871
[14]  Shende, C., Sahoo, S., Sam, S., Patel, P., Morillo, R., Wang, X., et al. (2023) Predicting Symptom Improvement during Depression Treatment Using Sleep Sensory Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7, 1-21. https://doi.org/10.1145/3610932
[15]  McLaughlin, N.C.R., Dougherty, D.D., Eskandar, E., Ward, H., Foote, K.D., Malone, D.A., et al. (2021) Double Blind Randomized Controlled Trial of Deep Brain Stimulation for Obsessive-Compulsive Disorder: Clinical Trial Design. Contemporary Clinical Trials Communications, 22, Article ID: 100785.
https://doi.org/10.1016/j.conctc.2021.100785
[16]  Rodriguez, C.I., Kegeles, L.S., Levinson, A., Feng, T., Marcus, S.M., Vermes, D., et al. (2013) Randomized Controlled Crossover Trial of Ketamine in Obsessive-Compulsive Disorder: Proof-of-Concept. Neuropsychopharmacology, 38, 2475-2483.
https://doi.org/10.1038/npp.2013.150
[17]  D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., et al. (2022) Under Specification Presents Challenges for Credibility in Modern Machine Learning. Journal of Machine Learning Research, 23, 1-61.
[18]  Thiagarajan, J.J., Venkatesh, B., Rajan, D. and Sattigeri, P. (2020) Improving Reliability of Clinical Models Using Prediction Calibration. In: Sudre, C.H., et al., Eds., Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, Springer, 71-80. https://doi.org/10.1007/978-3-030-60365-6_8
[19]  Wani, N.A., Kumar, R., Mamta, Bedi, J. and Rida, I. (2024) Explainable AI-Driven IoMT Fusion: Unravelling Techniques, Opportunities, and Challenges with Explainable AI in Healthcare. Information Fusion, 110, Article ID: 102472.
https://doi.org/10.1016/j.inffus.2024.102472
[20]  Amerio, A., Odone, A., Liapis, C.C. and Ghaemi, S.N. (2014) Diagnostic Validity of Comorbid Bipolar Disorder and Obsessive-Compulsive Disorder: A Systematic Review. Acta Psychiatrica Scandinavica, 129, 343-358. https://doi.org/10.1111/acps.12250
[21]  Mucci, F., Toni, C., Favaretto, E., Vannucchi, G., Marazziti, D. and Perugi, G. (2019) Obsessive-Compulsive Disorder with Comorbid Bipolar Disorders: Clinical Features and Treatment Implications. Current Medicinal Chemistry, 25, 5722-5730.
https://doi.org/10.2174/0929867324666171108145127
[22]  de Filippis, R., Aguglia, A., Costanza, A., Benatti, B., Placenti, V., Vai, E., et al. (2024) Obsessive-Compulsive Disorder as an Epiphenomenon of Comorbid Bipolar Disorder? An Updated Systematic Review. Journal of Clinical Medicine, 13, Article 1230.
https://doi.org/10.3390/jcm13051230
[23]  Sagman, D. and Tohen, M. (2012) Comorbidity in Bipolar Disorder: The Complexity of Diagnosis and Treatment. Psychiatric Times, 29, 30.
[24]  Belzer, K. and Schneier, F.R. (2004) Comorbidity of Anxiety and Depressive Disorders: Issues in Conceptualization, Assessment, and Treatment. Journal of Psychiatric Practice, 10, 296-306. https://doi.org/10.1097/00131746-200409000-00003
[25]  McIntyre, R.S., Konarski, J.Z. and Yatham, L.N. (2004) Comorbidity in Bipolar Disorder: A Framework for Rational Treatment Selection. Human Psychopharmacology: Clinical and Experimental, 19, 369-386. https://doi.org/10.1002/hup.612
[26]  Martinotti, G., Chiappini, S., Mosca, A., Miuli, A., Santovito, M.C., Pettorruso, M., et al. (2022) Atypical Antipsychotic Drugs in Dual Disorders: Current Evidence for Clinical Practice. Current Pharmaceutical Design, 28, 2241-2259.
https://doi.org/10.2174/1381612828666220623092853
[27]  Chekroud, A.M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., et al. (2021) The Promise of Machine Learning in Predicting Treatment Outcomes in Psychiatry. World Psychiatry, 20, 154-170. https://doi.org/10.1002/wps.20882
[28]  Ochs, L. (2006) The Low Energy Neurofeedback System (LENS): Theory, Background, and Introduction. Journal of Neurotherapy, 10, 5-39.
https://doi.org/10.1300/j184v10n02_02
[29]  Shatte, A.B.R., Hutchinson, D.M. and Teague, S.J. (2019) Machine Learning in Mental Health: A Scoping Review of Methods and Applications. Psychological Medicine, 49, 1426-1448. https://doi.org/10.1017/s0033291719000151
[30]  Kavzoglu, T. and Teke, A. (2022) Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost). Arabian Journal for Science and Engineering, 47, 7367-7385.
https://doi.org/10.1007/s13369-022-06560-8
[31]  Perna, G., Alciati, A., Daccò, S., Grassi, M. and Caldirola, D. (2020) Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables. Psychiatry Investigation, 17, 193-206. https://doi.org/10.30773/pi.2019.0289
[32]  Fisher, A.L., Arora, K., Maehashi, S., Schweitzer, D. and Akefe, I.O. (2024) Unveiling the Neurolipidome of Obsessive-Compulsive Disorder: A Scoping Review Navigating Future Diagnostic and Therapeutic Applications. Neuroscience & Biobehavioral Reviews, 166, Article ID: 105885. https://doi.org/10.1016/j.neubiorev.2024.105885
[33]  Pyles, D.A.M., Muniz, K., Cade, A. and Silva, R. (1997) A Behavioral Diagnostic Paradigm for Integrating Behavior-Analytic and Psychopharmacological Interventions for People with a Dual Diagnosis. Research in Developmental Disabilities, 18, 185-214.
https://doi.org/10.1016/s0891-4222(97)00003-6
[34]  Tai, A.M.Y., Albuquerque, A., Carmona, N.E., Subramanieapillai, M., Cha, D.S., Sheko, M., et al. (2019) Machine Learning and Big Data: Implications for Disease Modeling and Therapeutic Discovery in Psychiatry. Artificial Intelligence in Medicine, 99, Article ID: 101704. https://doi.org/10.1016/j.artmed.2019.101704
[35]  Rane, N., Choudhary, S. and Rane, J. (2023) Explainable Artificial Intelligence (XAI) in Healthcare: Interpretable Models for Clinical Decision Support. SSRN Electronic Journal.
[36]  Kanyongo, W. and Ezugwu, A.E. (2023) Feature Selection and Importance of Predictors of Non-Communicable Diseases Medication Adherence from Machine Learning Research Perspectives. Informatics in Medicine Unlocked, 38, Article ID: 101232.
https://doi.org/10.1016/j.imu.2023.101232
[37]  Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L. and Steyerberg, E.W. (2019) Calibration: The Achilles Heel of Predictive Analytics. BMC Medicine, 17, Article No. 230. https://doi.org/10.1186/s12916-019-1466-7
[38]  Peetluk, L.S., Rebeiro, P.F., Ridolfi, F.M., Andrade, B.B., Cordeiro-Santos, M., Kritski, A., Durovni, B., et al. (2022) A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes. Clinical Infectious Diseases, 74, 973-982.
[39]  Huang, Y., Li, W., Macheret, F., Gabriel, R.A. and Ohno-Machado, L. (2020) A Tutorial on Calibration Measurements and Calibration Models for Clinical Prediction Models. Journal of the American Medical Informatics Association, 27, 621-633.
https://doi.org/10.1093/jamia/ocz228
[40]  Keren, G. (1991) Calibration and Probability Judgements: Conceptual and Methodological Issues. Acta Psychologica, 77, 217-273.
https://doi.org/10.1016/0001-6918(91)90036-y
[41]  Tiet, Q.Q. and Mausbach, B. (2007) Treatments for Patients with Dual Diagnosis: A Review. Alcoholism: Clinical and Experimental Research, 31, 513-536.
https://doi.org/10.1111/j.1530-0277.2007.00336.x
[42]  Anuyah, S., Singh, M.K. and Nyavor, H. (2024) Advancing Clinical Trial Outcomes Using Deep Learning and Predictive Modelling: Bridging Precision Medicine and Patient-Centered Care. arXiv: 2412.07050.
[43]  Meehan, A.J., Lewis, S.J., Fazel, S., Fusar-Poli, P., Steyerberg, E.W., Stahl, D., et al. (2022) Clinical Prediction Models in Psychiatry: A Systematic Review of Two Decades of Progress and Challenges. Molecular Psychiatry, 27, 2700-2708.
https://doi.org/10.1038/s41380-022-01528-4
[44]  Souery, D., Oswald, P., Massat, I., Bailer, U., Bollen, J., Demyttenaere, K., et al. (2007) Clinical Factors Associated with Treatment Resistance in Major Depressive Disorder: Results from a European Multicenter Study. The Journal of Clinical Psychiatry, 68, 1062-1070. https://doi.org/10.4088/jcp.v68n0713
[45]  Gibbons, R.D., Clark, D.C. and Kupfer, D.J. (1993) Exactly What Does the Hamilton Depression Rating Scale Measure? Journal of Psychiatric Research, 27, 259-273.
https://doi.org/10.1016/0022-3956(93)90037-3
[46]  Rosario-Campos, M.C., Miguel, E.C., Quatrano, S., Chacon, P., Ferrao, Y., Findley, D., et al. (2006) The Dimensional Yale-Brown Obsessive-Compulsive Scale (DY-BOCS): An Instrument for Assessing Obsessive-Compulsive Symptom Dimensions. Molecular Psychiatry, 11, 495-504. https://doi.org/10.1038/sj.mp.4001798
[47]  Rabinowitz, J., Young, R.C., Yavorsky, C., Williams, J.B.W., Sedway, J., Marino, P., et al. (2024) Consistency Checks to Improve Measurement with the Young Mania Rating Scale (YMRS). Journal of Affective Disorders, 345, 24-31.
https://doi.org/10.1016/j.jad.2023.10.098
[48]  Bandelow, B., Chouinard, G., Bobes, J., Ahokas, A., Eggens, I., Liu, S., et al. (2009) Extended-Release Quetiapine Fumarate (Quetiapine XR): A Once-Daily Monotherapy Effective in Generalized Anxiety Disorder. Data from a Randomized, Double-Blind, Placebo- and Active-Controlled Study. The International Journal of Neuropsychopharmacology, 13, 305-320. https://doi.org/10.1017/s1461145709990423
[49]  Kavzoglu, T. and Teke, A. (2022) Advanced Hyperparameter Optimization for Improved Spatial Prediction of Shallow Landslides Using Extreme Gradient Boosting (XGBoost). Bulletin of Engineering Geology and the Environment, 81, Article No. 201.
https://doi.org/10.1007/s10064-022-02708-w
[50]  Trizoglou, P., Liu, X. and Lin, Z. (2021) Fault Detection by an Ensemble Framework of Extreme Gradient Boosting (XGBoost) in the Operation of Offshore Wind Turbines. Renewable Energy, 179, 945-962. https://doi.org/10.1016/j.renene.2021.07.085
[51]  Levy, J.J. and O’Malley, A.J. (2020) Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning. BMC Medical Research Methodology, 20, Article No. 171. https://doi.org/10.1186/s12874-020-01046-3
[52]  Fatima, S., Hussain, A., Amir, S.B., Ahmed, S.H. and Aslam, S.M.H. (2023) XGBoost and Random Forest Algorithms: An in Depth Analysis. Pakistan Journal of Scientific Research, 3, 26-31. https://doi.org/10.57041/pjosr.v3i1.946
[53]  de Filippis, R. and Foysal, A.A. (2024) Case Report: The Role of Monoamine Oxidase Inhibitors in Treating Resistant Depression. Open Access Library Journal, 11, 1-12.
https://doi.org/10.4236/oalib.1112369
[54]  de Filippis, R. and Foysal, A.A. (2024) Case Report: The Role of Tricyclic Antidepressants in Treating Resistant Depression. Open Access Library Journal, 11, 1-12.
https://doi.org/10.4236/oalib.1112368
[55]  Adler, D.A., Yang, Y., Viranda, T., Xu, X., Mohr, D.C., Van Meter, A.R., et al. (2024) Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8, 1-33. https://doi.org/10.1145/3699755
[56]  de Filippis, R. and Foysal, A.A. (2024) Evaluating Pharmacological and Rehabilitation Strategies for Effective Management of Bipolar Disorder: A Comprehensive Clinical Study. Advances in Bioscience and Biotechnology, 15, 406-431.
https://doi.org/10.4236/abb.2024.157025
[57]  Feretzakis, G., Sakagianni, A., Anastasiou, A., Kapogianni, I., Bazakidou, E., Koufopoulos, P., et al. (2024) Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions. Applied Sciences, 14, Article 5925.
https://doi.org/10.3390/app14135925
[58]  Hazari, N., Narayanaswamy, J.C. and Arumugham, S.S. (2016) Predictors of Response to Serotonin Reuptake Inhibitors in Obsessive-Compulsive Disorder. Expert Review of Neurotherapeutics, 16, 1175-1191. https://doi.org/10.1080/14737175.2016.1199960
[59]  Cederlof, M., Lichtenstein, P., Larsson, H., Boman, M., Rück, C., Landén, M., et al. (2014) Obsessive-Compulsive Disorder, Psychosis, and Bipolarity: A Longitudinal Cohort and Multigenerational Family Study. Schizophrenia Bulletin, 41, 1076-1083.
https://doi.org/10.1093/schbul/sbu169
[60]  de Filippis, R. and Foysal, A.A. (2024) Securing Predictive Psychological Assessments: The Synergy of Blockchain Technology and Artificial Intelligence. Open Access Library Journal, 11, 1-23. https://doi.org/10.4236/oalib.1112378
[61]  de Filippis, R. and Foysal, A.A. (2024) Decoding Emotions: How AI and Machine Learning Unravel the Human Psyche. Voice of the Publisher, 10, 382-399.
https://doi.org/10.4236/vp.2024.104030
[62]  de Filippis, R. and Foysal, A.A. (2024) Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach. Advances in Bioscience and Biotechnology, 15, 289-309.
https://doi.org/10.4236/abb.2024.155018

Full-Text


Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133




©2008-2020 OALib, Inc | Public user content licensed CC-BY 4.0 unless otherwise specified. OALib ISSN Online: 2333-9721 OALib ISSN Print: 2333-9705