Information Systems Department, Faculty of Computers and Information, Damanhour University, 22511, Damanhour, Egypt.
Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt.
Sci Rep. 2024 Feb 21;14(1):4275. doi: 10.1038/s41598-024-54065-1.
The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.
制作灵活、标准和早期医学诊断的挑战是巨大的。然而，一些限制并未完全克服。首先，医学专家建立或从训练有素的数据集中学到的诊断规则是静态的且过于一般。当发现新情况时，这导致决策缺乏适应性灵活性。其次，医学术语互操作性至关重要。它提高了真实性和医学进步，避免了孤立的系统和数据交换、分析和解释的困难。第三，诊断标准通常是异构的和多变的。它包括症状、患者病史、人口统计学、治疗、遗传学、生物化学和成像。症状是早期检测的高影响指标。我们需要以不同的方式处理这些症状，这些症状与语义有很大关系，变化广泛，具有语言信息。这对早期诊断决策产生负面影响。根据情况，仅凭成像和一些医学测试进行诊断。在这种情况下，尽管诊断的准确性非常高，但这些决策能否被视为早期诊断或证明病情正在恶化？我们在本文中的贡献是提出一个基于语义、模糊和动态决策规则的真实医学诊断系统。我们试图整合本体语义推理和模糊推理。它促进了模糊推理，并处理了知识表示问题。在并发症和症状中，本体语义推理提高了规则在可解释性、动态性和智能性方面的评估过程。提出了一个涉及阿尔茨海默病（AD）领域的真实案例研究，ADNI。所提出的系统表明了该系统诊断 AD 的可能性，其准确率为 97.2%、95.4%、94.8%、93.1%和 96.3%，分别为 AD、LMCI、EMCI、SMC 和 CN。
