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Open Access Library Journal
2025, Volume 12, e12896
ISSN Online: 2333-9721
ISSN Print: 2333-9705
DOI:
10.4236/oalib.1112896 Feb. 17, 2025 1
Open Access Library Journal
Cost-Optimized and Efficacy-Driven Analysis of
Antidepressants in Major Depressive Disorder:
A Machine Learning and Visualization
Approach
Rocco de Filippis1*, Abdullah Al Foysal2
1Department of Neuroscience, Institute of Psychopathology, Rome, Italy
2Department of Computer Engineering (AI), University of Genova, Genova, Italy
Abstract
The treatment of major depressive disorder (MDD) often involves antidepres-
sants, yet non-
response to initial therapies remains a significant clinical and
economic burden. This research aims to evaluate the comparative efficacy and
cost-efficiency of 13 commonly prescribed antidepressants, spanning four ma-
jor drug classes: SSRIs, SNRIs, NaSSAs, and TCAs. By employing machine
learning and simulated patient data, we model non-
response rates over two
years, highlighting each drug’s cumulative risk trajectories. This study also in-
vestigates the direct correlation between non-
response rates and estimated
healthcare costs, offering insights into the economic implications of antide-
pressant inefficacy. The analysis reveals distinct patterns of non-
response
across classes, with SSRIs exhibiting the lowest cumulative risk and cost varia-
bility. Conversely, NaSSA and TCA classes demonstrate higher non-
response
rates, contributing to greater financial strain. Visual representations, including
line plots with confidence int
ervals, bar plots, scatter diagrams, and box plots,
provide an intuitive breakdown of risk distribution and economic impact. The
primary goal of this research is to guide clinicians and policymakers in select-
ing cost-effective and efficacious antidepressan
ts, ultimately improving patient
outcomes while minimizing unnecessary healthcare expenditure. This study
addresses the dual challenges of clinical efficacy and economic sustainability in
MDD treatment by integrating statistical modelling with visual analytics. Fu-
ture work will focus on incorporating real-
world demographic and clinical data
to enhance the precision and applicability of the findings.
Subject Areas
Mental Health, Pharmacology, Healthcare Economics
How to cite this paper:
de Filippis, R. and
Al Foysal
, A. (2025) Cost-
Optimized and
Efficacy
-
Driven Analysis of Antidepressants
in Major Depressive Disorder: A Machine
Learning and Visualization Approach
.
Open
Access Library Journal
,
12
: e12896.
https://doi.org/10.4236/oalib.1112896
Received:
January 1, 2025
Accepted:
February 14, 2025
Published:
February 17, 2025
Copyright © 20
25 by author(s) and Open
Access Library Inc
.
This work is licensed under the Creative
Commons Attribution
International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
R. de Filippis, A. Al Foysal
DOI:
10.4236/oalib.1112896 2
Open Access Library Journal
Keywords
Antidepressants, Machine Learning, Major Depressive Disorder (MDD), Cost
Efficiency, Non-Response Rates
1. Introduction
Despite the availability of numerous pharmacological treatments, nearly 30% -
50% of patients fail to respond to initial antidepressant therapy [1]-[3]. This sub-
stantial rate of non-response often necessitates multiple treatment adjustments,
leading to prolonged patient suffering, increased hospitalization rates, and esca-
lating healthcare costs. Antidepressants, including selective serotonin reuptake in-
hibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), nora-
drenergic and specific serotonergic antidepressants (NaSSAs), and tricyclic anti-
depressants (TCAs), remain central to the pharmacological management of Major
Depressive Disorder (MDD) [4]-[7]. However, significant variability in patient
response across these drug classes underscores the pressing need for tailored and
cost-efficient treatment strategies. Among these classes, SSRIs are widely regarded
as the first-line treatment due to their favorable side effect profile, improved tol-
erability, and lower costs compared to other options [8] [9]. They have consist-
ently demonstrated lower rates of adverse events, such as sedation and cardiovas-
cular complications, making them suitable for a broad range of patients. Con-
versely, TCAs and NaSSAs, while effective in certain cases, are often associated
with higher risks of side effects and economic burden, resulting in their use being
reserved for treatment-resistant or complex cases where first-line options prove
inadequate [10]-[12]. This study aims to address the critical gap in understanding
how non-response rates to antidepressants correlate with their economic impact
on healthcare systems. By employing machine learning models and simulated pa-
tient data, the research offers a novel perspective on visualizing non-response tra-
jectories, cumulative risk, and the financial burden across various antidepressant
classes. Through detailed visual representations and cost projections, this study
seeks to empower clinicians and policymakers with data-driven insights to make
informed prescribing decisions. Ultimately, this research aspires to optimize an-
tidepressant selection, improve patient outcomes, and alleviate the economic
strain on healthcare systems.
2. Methods
This study employs a multifaceted approach, integrating machine learning simu-
lations, synthetic data generation, and real-world data to comprehensively model
the efficacy and economic impact of 13 antidepressants. The methodology is struc-
tured into three primary phases: data acquisition and preparation, non-response
rate modelling, and cost estimation.
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2.1. Data Acquisition and Preparation
Data acquisition forms the foundation of this study, combining population-based
treatment records with synthetic data to ensure broader applicability and real-
world relevance. Treatment records from clinical studies and trials provided base-
line non-response rates, ranging from 48% to 82% over a two-year period [13]
[14]. To address data gaps and enhance the robustness of the analysis, synthetic
patient data was generated, reflecting variability in age, sex, comorbidities, and
prior treatment responses. The dataset encompasses 13 antidepressants classified
into four primary drug classes, as shown in Table 1 below.
Table 1. Classification of antidepressants by drug class.
Drug Class
Antidepressants
SSRIs Sertraline, Citalopram, Fluoxetine, Paroxetine, Escitalopram
SNRIs Venlafaxine, Duloxetine
NaSSA Mirtazapine, Mianserin
TCAs Amitriptyline, Nortriptyline, Clomipramine, Dosulepin
2.2. Machine Learning Simulation
A custom machine-learning pipeline was developed to model non-response rates
over 104 weeks. The goal was to predict patient outcomes based on historical data
and relevant clinical factors. The pipeline follows a structured process:
• Model Selection: Three machine learning algorithms—Random Forest, XGBoost,
and Logistic Regression—were employed to analyse patterns in non-response
trajectories. Each model’s performance was evaluated to ensure accurate pre-
dictions.
• Training and Validation: The dataset was split into training (70%) and vali-
dation (30%) subsets, ensuring the models could generalize to new data. Cross-
validation was applied to reduce overfitting and improve robustness.
• Confidence Intervals (CI): Bootstrapping techniques were applied to derive
95% confidence intervals for the predicted non-response rates, capturing po-
tential variability and uncertainty in patient outcomes.
• Feature Engineering: Critical predictors such as baseline depression severity,
medication adherence, socio-economic status, and prior treatment history were
included in the models. Feature importance rankings were generated to iden-
tify key factors influencing non-response.
2.3. Model Selection and Justification
This study utilized three machine learning algorithms—Random Forest, XGBoost,
and Logistic Regression—to model non-response rates among patients treated
with antidepressants. These models were selected based on their complementary
strengths:
• Random Forest: Effective in handling high-dimensional data and capturing
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non-linear interactions between clinical variables.
• XGBoost: Known for its gradient-boosting framework, offering superior ac-
curacy in complex datasets while mitigating overfitting through regularization.
• Logistic Regression: A baseline model ideal for binary classification tasks, al-
lowing for straightforward interpretation of predictor contributions.
The diversity of these algorithms ensures robust predictions while addressing
the varied complexities of clinical data.
2.4. Hyperparameter Tuning and Validation
To optimize model performance, hyperparameters were fine-tuned using grid
search with cross-validation. For Random Forest, parameters such as the number
of trees and maximum tree depth were calibrated. XGBoost’s learning rate, tree
depth, and number of boosting rounds were adjusted to maximize predictive ac-
curacy. Logistic Regression utilized regularization techniques (L1 and L2 penal-
ties) to prevent overfitting. Models were trained on 70% of the dataset and vali-
dated on the remaining 30%, ensuring generalizability.
Feature Engineering
Key clinical predictors were engineered to improve model inputs:
• Baseline depression severity: Quantified through established scales such as
the Hamilton Depression Rating Scale (HDRS).
• Medication adherence: Represented as a binary variable, indicating whether
patients followed prescribed regimens.
• Socio-economic status: Derived from simulated demographic data, reflecting
income and employment stability.
• Prior treatment history: Categorized based on the number of failed treatment
attempts and class of previous antidepressants.
The importance of these features was ranked using feature importance metrics
in Random Forest and Shapley additive explanations (SHAP) for XGBoost, offer-
ing transparency in model predictions.
2.5. Cost Estimation
The financial burden of non-response was quantified by correlating non-response
rates with economic factors, encompassing both direct and indirect healthcare
costs [15]-[18]. The cost model was divided into three components:
• Direct Costs: This category includes expenditures on medication, psychiatric
consultations, emergency visits, and hospitalizations [19]-[21].
• Indirect Costs: Productivity loss, absenteeism, and caregiver burdens were
factored into the model to estimate broader economic impacts [22]-[24].
• Monte Carlo Simulation: To account for cost variability across different pa-
tient demographics, Monte Carlo simulations were performed. This approach
modelled a range of potential outcomes, providing probabilistic estimates for
different scenarios [25]-[27].
By integrating machine learning predictions with cost estimations, this study
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provides a comprehensive evaluation of the long-term implications of antidepres-
sant non-response, informing both clinical decisions and healthcare policy [28]
[29].
3. Results and Visual Analysis
Understanding the progression of non-response risk and its economic burden is
critical for optimizing antidepressant prescriptions [30] [31]. This section pre-
sents visual analyses of the longitudinal trends and class-specific variations in
non-response rates, followed by an economic assessment of cost burdens.
This plot in Figure 1 tracks the evolving risk of non-response for 13 antidepres-
sants over 104 weeks. Confidence intervals (shaded areas) widen with time, re-
flecting increasing uncertainty and treatment variability. SSRIs like Sertraline ex-
hibit lower cumulative risk compared to TCAs like Dosulepin and NaSSA-class
drugs such as Mianserin [32].
Figure 1. Risk of non-response over time (with 95% CI).
Bar plots here in Figure 2 show the non-response rates for each antidepressant,
grouped by class. Mianserin (NaSSA) exhibits the highest non-response rate (82%),
while SSRIs demonstrate comparatively lower rates. This visualization under-
scores class differences and highlights potential targets for intervention.
Box plots in Figure 3 depict non-response rate variability within each antide-
pressant class. SSRIs show lower dispersion, indicating more consistent perfor-
mance, while NaSSA and TCA classes display greater variability, suggesting higher
risk and unpredictability.
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Figure 2. Non-response rates by antidepressant and class.
Figure 3. Distribution of non-response rates by class.
This cumulative risk graph here in Figure 4 highlights the increasing burden of
non-response as treatment progresses. By week 104, the cumulative risk approaches
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100% for some antidepressants, particularly in the NaSSA and TCA classes. SSRIs
remain below 60% cumulative risk.
Figure 4. Cumulative risk of non-response over time.
Figure 5. The estimated cost of non-response by antidepressant and class.
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This bar plot in Figure 5 visualizes the estimated cost of non-response per an-
tidepressant. SSRIs generally show lower costs, while TCAs and NaSSA antide-
pressants incur higher costs due to increased non-response rates. The financial
burden of Mianserin (NaSSA) and Amitriptyline (TCA) is notably high.
A scatter plot in Figure 6 shows the relationship between non-response rates
and the estimated cost of treatment. TCAs and NaSSA drugs cluster at higher non-
response rates and costs, suggesting an economic impact aligned with lower treat-
ment efficacy.
Box plots here in Figure 7 depict the distribution of estimated costs across dif-
ferent antidepressant classes. SSRIs show narrower cost variability, while NaSSA
and TCA classes demonstrate broader distributions, indicating higher financial
risks and variability in treatment outcomes.
4. Discussion
The findings of this study reinforce the superiority of SSRIs as the primary phar-
macological intervention for managing MDD, given their consistently lower non-
response rates and reduced cumulative risk over time [33] [34]. SSRIs stand out
not only for their clinical efficacy but also for their economic advantages, mini-
mizing the financial strain on healthcare systems by reducing the need for fre-
quent treatment adjustments, hospitalizations, and additional psychiatric inter-
ventions [35] [36]. This makes them a cost-effective solution for patients initiating
antidepressant therapy. However, the study highlights significant outliers, partic-
ularly within the NaSSA and TCA classes. Mianserin and TCAs such as Clomi-
pramine and Dosulepin exhibit higher non-response rates [37], leading to greater
healthcare expenditures over the treatment period. While these drugs remain val-
uable options for treatment-resistant depression, their use should be carefully
weighed against their economic burden. This finding suggests that healthcare pro-
viders should adopt a stepped approach to prescribing antidepressants, prioritiz-
ing SSRIs for initial treatment, and reserving NaSSAs and TCAs for patients who
demonstrate poor responses to first-line therapies. Despite the promising results,
certain limitations must be acknowledged. The simulated data used in this study
lacks granularity in demographic variations, which could influence real-world
outcomes. Additionally, while confidence intervals account for variability, they
may not fully capture extreme outliers or rare patient responses. Finally, cost es-
timates are generalized and may differ across healthcare systems, underscoring
the need for localized studies to validate these findings further. Future research
should integrate diverse patient populations and real-world clinical data to en-
hance the model’s applicability across different demographic and economic con-
texts.
5. Conclusion
This study leverages machine learning and visual analytics to optimize antidepres-
sant selection by analyzing non-response rates and associated healthcare costs.
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The findings underscore the superiority of selective serotonin reuptake inhibitors
(SSRIs) as the most effective and economically viable option for treating major
depressive disorder (MDD) [38]. SSRIs consistently demonstrated lower non-re-
sponse rates and slower cumulative risk progression over a two-year period, rein-
forcing their status as the preferred first-line treatment [39]-[41]. Their use min-
imizes treatment adjustments, reduces hospitalizations, and alleviates indirect
costs such as productivity loss, contributing to broader economic benefits for
healthcare systems. In contrast, antidepressants within the noradrenergic and spe-
cific serotonergic antidepressants (NaSSAs) and tricyclic antidepressants (TCAs)
classes exhibited higher non-response rates and greater economic burdens [42]-
[46]. Drugs like Mianserin (NaSSA) and Clomipramine (TCA) emerged as costly
outliers, suggesting that their use should be reserved for patients who do not re-
spond to SSRIs or other lower-risk options [47] [48]. The study highlights the im-
portance of a tiered prescribing approach, prioritizing SSRIs while reserving
higher-risk medications for more severe, treatment-resistant cases [49]-[51]. By
adopting such strategies, clinicians can improve patient outcomes while curbing
unnecessary healthcare expenditure. Future research should focus on integrating
real-world clinical data to refine the predictive accuracy of cost and risk models,
ensuring applicability across diverse patient populations and healthcare environ-
ments [52]. This approach will further enhance personalized treatment strategies
and economic sustainability in MDD management.
Conflicts of Interest
The authors declare no conflicts of interest.
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