Comparing Machine Learning to Evidence-Based Decision-Making Techniques for Identification of Mood, Trauma, and Behavior Disorders

Aug 5, 2026

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Clinical diagnosis is a pivotal step that determines subsequent therapy planning and treatment outcomes, making accurate and consensus-based decision-making essential. Conventional unstructured interviews can produce biased interpretations, symptom-order effects, and over- or under-diagnosis across disorders. Evidence-based strategies and computational advances enable algorithmic prediction, where simpler models can match expert clinicians. The work evaluates machine-learning models such as SVM and Random Forest alongside statistical approaches, addressing predictive accuracy, generalizability, complexity, implementation cost, and clinical workflow feasibility for identifying mood, trauma, and behavior disorders.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

AccessFree to download

Introduction

Limitations of unstructured clinical interviews

Evidence-based and algorithmic diagnostic approaches

Evaluation factors for clinical performance

Comparative modeling for pediatric bipolar disorder

Assessment methods and model complexity

Comparing Machine Learning to Evidence-Based Decision-Making Techniques for Identification of Mood, Trauma, and Behavior Disorders Zhuoyu Shi Advisor: Dr. Eric Youngstrom Committee Members: Dr. Aysenil Belger & Dr. Oscar Gonzalez Department of Psychology and Neuroscience University of North Carolina at Chapel Hill Acknowledgments This research was supported in part by a grant from the Lindquist Undergraduate Research Award. First of all, I would like to express my sincere appreciation here to Dr. Eric Youngstrom, for his supervision on this project and his support throughout my undergraduate education. He has enriched not only my academic journey with his knowledge and patience, but also my personal development by helping me founded the global cultural student organization (aCc) -a Culture club during my freshman year. I would also like to thank Dr. Aysenil Belger, my supervisor on Gil Internship and on my independent project on neuroimaging. Her academic and moral supports have been instrumental in my growth. Furthermore, I would like to thank Dr. Oscar Gonzalez, who kindly accepted the role of my committee member. His feedback and guidance have helped me a lot in improving my project. In addition, I must express my gratitude for my friends, whose mental support has been my refuge throughout my undergraduate studies. I am especially grateful to Wenfei Yu and Jerry You, for their invaluable encouragement and care to me. Congratulations on getting married! I would also like to thank Ziyu Sheng and Ziqian Zhao for their moral support throughout my undergraduate studies. Finally, to Wenjuan Cui, for our ten-year friendship that has never been separated by distance. Thank you all for your significant contributions to my undergraduate journey. It has been a privilege to know you all and work with you all. Your influence has shaped me in ways that will resonate throughout my future endeavors. I wish you all the best in the future. Introduction Diagnosis stands as a pivotal step in clinical practice, shaping the entire trajectory of patient care (Youngstrom, 2013) . It serves not only as the foundation for all subsequent clinical decisions, including therapy and treatment strategy selection, but also as a determining factor of treatment efficacy and patient outcomes. Thus, accuracy and consensus on diagnosis are imperative for consistent treatment approaches and positive clinical outcomes, making reliable diagnostic decisions critical in ensuring coherent and effective clinical activities. Although diagnostic decision-making lays the foundation for patient care, current diagnostic approaches have significant drawbacks (Mokros et al., 2018) . The most popular approach right now is unstructured clinical interviews (Jones, 2010), which rely on the clinician's intuition and personal experience to detect and interpret patient situations. While this approach can sometimes efficiently decompose complex data into actionable insights, it can lead to biased results (Jenkins & Youngstrom, 2016) and cause the overdiagnosis of certain conditions and the underestimation of others (Jensen-Doss et al., 2014) . Furthermore, even the order of patient symptoms reported to clinicians can affect the diagnosis (Cwik & Margraf, 2017) . Rather than making decisions through the lens of intuitive impressions, clinicians are encouraged to employ more complicated decision-making strategies and algorithms, which can analyze patient data comprehensively and enhance the reliability and accuracy of clinical decisions. The development of statistical and computational innovations has remarkably transformed the landscape of clinical diagnosis with evidence-based precision. Research has demonstrated that even simple algorithms can match or even outperform experienced clinicians (Ægisdóttir et al., 2006) . A diverse array of methods, ranging from traditional statistical approaches like Bayes theorem (Ledley & Lusted, 1959) to cutting-edge machine

FAQs

Why is diagnosis accuracy so critical in clinical practice?

Diagnosis sets the trajectory of patient care by shaping therapy choice and influencing treatment efficacy and patient outcomes. Reliable diagnostic decisions are necessary for coherent and effective clinical activities.

What drawbacks exist in current diagnostic approaches like unstructured clinical interviews?

Unstructured interviews rely on clinician intuition, which can introduce bias. Symptom-report order can also affect diagnosis, contributing to overdiagnosis of some conditions and underestimation of others.

How does the document compare machine learning with evidence-based decision-making for disorder identification?

It describes how statistical and computational methods support evidence-based prediction, including algorithms such as SVM and Random Forest. It also outlines evaluation criteria like predictive accuracy, generalizability, complexity, and feasibility for integration into clinical workflows.

Aug 5, 2026

Upload by Noah