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Research Article | DOI: https://doi.org/10.31579/2690-1919/504
Department of Haematology, Oncology and Immunology, Philipps University Marburg, Marburg, Germany.
*Corresponding Author: Gerhard Zugmaier, MD, Department of Haematology, Oncology and Immunology, Philipps University Marburg, Marburg, Germany.
Citation: Gerhard Zugmaier, (2025), Mathematical logic (Boolean Algebra) for computation of Treatment Recommendation in Systemic Sclerosis, J Clinical Research and Reports, 19(1); DOI:10.31579/2690-1919/504
Copyright: © 2025, Gerhard Zugmaier. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 11 February 2025 | Accepted: 28 February 2025 | Published: 22 March 2025
Keywords: Boolean algebra; mathematical logic; computational medicine; systemic sclerosis; treatment
Background: Treatment guidelines have become more and more complex due to targeting the needs of the individual patient. This results in numerous treatment options driven by permutations of risk factors
Aim: This study used mathematical logic (Boolean algebra) as precise method to calculate treatment permutations.
Results: Each medication was represented as a Boolean variable. Premises were sub entities of systemic sclerosis; conclusions were combinations of medications.
Conclusion: Mathematical logic (Boolean algebra) can be used to precisely calculate treatment combination in systemic sclerosis based on disease conditions.
The European Alliance of Associations for Rheumatology (EULAR) periodically updates its recommendations for the treatment of systemic sclerosis (SSc) to incorporate new evidence and therapies. The most recent update, published in 2023, introduced several new recommendations, particularly concerning skin fibrosis and interstitial lung disease (ILD). These include the use of mycophenolate mofetil, nintedanib, rituximab, and tocilizumab for treating these critical disease manifestations. Additionally, the recommendations provide guidance on first -line and second - line interventions, enhancing their utility for rheumatology practitioners. The update also emphasizes a new research agenda focusing on novel interventions for managing vascular, musculoskeletal, and gastrointestinal manifestations, as well as calcinosis and the local management of digital ulcers1.
While the EULAR recommendations are primarily based on systematic literature reviews and expert consensus, the integration of mathematical models can enhance the development and application of these guidelines. Mechanistic models, for instance, can simulate disease progression and predict therapeutic outcomes, thereby informing clinical decision-making and optimizing treatment strategies. In the context of systemic sclerosis such models can be particularly valuable given the disease's complexity and heterogeneity. For example, the ILDsym® software is a quantitative systems pharmacology model designed to simulate interstitial lung disease associated with systemic sclerosis, aiding in predicting treatment efficacy and enhancing clinical trial design.
Incorporating insights from mathematical logic into the EULAR recommendations could provide a more robust framework for understanding disease mechanisms and tailoring treatments to individual patients. This approach aligns with the personalized medicine paradigm, aiming to improve patient outcomes by considering individual variability in disease presentation and response to therapy. In this study we investigated the use of mathematical logic to calculate treatment combination and systemic sclerosis based on disease conditions.
1.Number system of mathematical logic (Boolean algebra) :
only the numbers 1 or 0 are permitted. Each mathematical operation can only result in 1 or 0. .
2.Boolean Operations:
o AND:
o Inclusive OR:
o NOT:
Example: 1 ⊕ 0 = 1, 1 ⊕ 1 = 0
o XNOR:
"if A, then B” is: expressed as A → B = ¬ A v B. The arithmetic rules of the inclusive OR are applied.
In the formulas the following operators take precedence:
v, ⊕ over =17
Examples of various symbols for the Same Operations7:
AND:
A⋅B or AB: Common in traditional Boolean algebra and engineering
A ∧ B: Used in formal logic and computer science
A AND B Seen in programming pseudocode or textual descriptions
OR (Inclusive OR):
A + B: Traditional Boolean algebra
A ∨ B: Used in logic and theoretical fields
A OR B: Common in programming and textual representations
XOR (Exclusive OR):
A ⊕ B: Theoretical computer science and mathematics
A XOR B: Textual or programming contexts
Systemic sclerosis ∧ digital ulcers → phosphodiesterase inhibitors v ilopost v bosentan
Systemic sclerosis ∧ interstitial lung disease → mycophenolate mofetil v rituximab v nintedanib
Systemic sclerosis ∧ poor prognosis → cyclophosphamide ∧ autologous hematopoietic stem cell transplantation
Systemic sclerosis ∧ pulmonary artery hypertension → phosphodiesterase inhibitors ∧ endothelin receptor antagonists v intravenous epoprostenol
Systemic sclerosis ∧ pulmonary artery hypertension Grade 3 ⊕ 4 → intravenous epoprostenol
Systemic sclerosis ∧ Raynaud phenomenon → dihydropyridine – type calcium antagonists v phosphodiesterase inhibitors
Systemic sclerosis ∧ Raynaud phenomenon ∧ failure of oral therapy → intravenous iloprost
Systemic sclerosis ∧ skin involvement → methotrexate v mycophenolate mofetil v rituximab
Mathematical logic, particularly Boolean logic, plays a crucial role in enhancing artificial intelligence (AI) applications within the medical field, especially in disease diagnosis and treatment planning. By providing a structured framework for representing and analyzing medical data, these mathematical tools facilitate the development of interpretable and efficient AI models.
The integration of Boolean logic into AI models contributes to the development of interpretable machine learning systems. Explainable AI (XAI) models based on expressive Boolean formulas allow for the classification of medical data with rules of tunable complexity. This approach enhances transparency in AI-driven diagnostics, enabling healthcare providers to understand and trust the decision-making process of AI systems [2]
Reinforcement learning (RL), a branch of AI, utilizes mathematical frameworks to optimize dynamic treatment regimes in healthcare. By modeling disease progression and treatment effects, RL algorithms can suggest personalized treatment plans that adapt to individual patient responses over time. This approach aims to enhance therapeutic outcomes by continuously learning from patient data and adjusting treatment strategies accordingly [3]
Mathematical modeling also plays a pivotal role in understanding the complex mechanisms underlying autoimmune diseases, including systemic sclerosis (SSc). These models integrate biological and clinical data to simulate disease progression, predict therapeutic outcomes, and identify potential treatment targets.
A comprehensive review by Ugolkov et al. (2024) highlights the application of mechanistic mathematical models across various autoimmune diseases. The study emphasizes that such models can elucidate disease dynamics and support drug development by quantitatively describing immune responses. While the review covers multiple conditions, it underscores the potential of these models in diseases like SSc, where complex immune interactions are prevalent [4]
Interstitial lung disease is a significant complication of systemic sclerosis. The ILDsym® software is a quantitative systems pharmacology (QSP) model specifically designed to simulate ILD associated with SSc. This mechanistic model predicts treatment efficacy, aids in clinical trial design, and enhances decision-making in drug development. By incorporating patient variability and disease progression, ILDsym® serves as a valuable tool for researchers and clinicians focusing on interstitial lung disease of systemic sclerosis.
Recent advancements in machine learning have introduced deep generative models to analyze complex disease trajectories in systemic sclerosis. Trottet et al. (2023) developed a model that captures temporal patterns in disease progression, enabling personalized predictions and identification of novel disease subtypes. This approach combines generative modeling with medical knowledge, offering a comprehensive framework for understanding SSc dynamics [5]
The integration of mathematical modeling in systemic sclerosis provides profound insights into therapeutic strategies. Mathematical logic (Boolean algebra) enhances our ability to optimize treatment interventions in systemic sclerosis.
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