In the intricate field of medical diagnostics, capturing the subtle
manifestations of diseases remains a challenge. Traditional methods, often
binary in nature, may not encapsulate the nuanced variances that exist in
real-world clinical scenarios. This paper introduces a novel approach by
leveraging Fuzzy Logic Rules to derive disease classes based on expert domain
knowledge from a medical practitioner. By recognizing that diseases do not
always fit into neat categories, and that expert knowledge can guide the
fuzzification of these boundaries, our methodology offers a more sophisticated
and nuanced diagnostic tool.
Using a dataset procured from a prominent hospital, containing detailed
patient blood count records, we harness Fuzzy Logic Rules, a computational
technique celebrated for its ability to handle ambiguity. This approach, moving
through stages of fuzzification, rule application, inference, and ultimately
defuzzification, produces refined diagnostic predictions. When combined with
the Random Forest classifier, the system adeptly predicts hematological
conditions using Complete Blood Count (CBC) parameters.
Preliminary results showcase high accuracy levels, underscoring the
advantages of integrating fuzzy logic into the diagnostic process. When
juxtaposed with traditional diagnostic techniques, it becomes evident that
Fuzzy Logic, especially when guided by medical expertise, offers significant
advancements in the realm of hematological diagnostics. This paper not only
paves the path for enhanced patient care but also beckons a deeper dive into
the potentialities of fuzzy logic in various medical diagnostic applications.

通过使用模糊逻辑规则结合医学专家领域知识，本文介绍了一种新方法来诊断血液病学状况，展示了高准确性的初步结果，强调了模糊逻辑在血液病学诊断中的潜力和优势。

应用模糊逻辑和专家知识推导血液病类别：基于全面的机器学习方法与 CBC 参数

Deriving Hematological Disease Classes Using Fuzzy Logic and Expert  Knowledge: A Comprehensive Machine Learning Approach with CBC Parameters

Modeling a system's temporal behaviour in reaction to external stimuli is a
fundamental problem in many areas. Pure Machine Learning (ML) approaches often
fail in the small sample regime and cannot provide actionable insights beyond
predictions. A promising modification has been to incorporate expert domain
knowledge into ML models. The application we consider is predicting the
progression of disease under medications, where a plethora of domain knowledge
is available from pharmacology. Pharmacological models describe the dynamics of
carefully-chosen medically meaningful variables in terms of systems of Ordinary
Differential Equations (ODEs). However, these models only describe a limited
collection of variables, and these variables are often not observable in
clinical environments. To close this gap, we propose the latent hybridisation
model (LHM) that integrates a system of expert-designed ODEs with
machine-learned Neural ODEs to fully describe the dynamics of the system and to
link the expert and latent variables to observable quantities. We evaluated LHM
on synthetic data as well as real-world intensive care data of COVID-19
patients. LHM consistently outperforms previous works, especially when few
training samples are available such as at the beginning of the pandemic.

该研究提出一种潜在的混合模型 (LHM)，该模型将专家设计的常微分方程系统与机器学习的神经微分方程系统相结合，以完全描述系统动态并将专家和隐变量与可观元量链接起来，特别是在样本较少的情况下表现优异，其应用场景为药物治疗疾病的进展预测。

将专家 ODE 集成到神经 ODE 中：药理学和疾病进展

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease  Progression

In recent years, word embeddings have been surprisingly effective at
capturing intuitive characteristics of the words they represent. These vectors
achieve the best results when training corpora are extremely large, sometimes
billions of words. Clinical natural language processing datasets, however, tend
to be much smaller. Even the largest publicly-available dataset of medical
notes is three orders of magnitude smaller than the dataset of the oft-used
"Google News" word vectors. In order to make up for limited training data
sizes, we encode expert domain knowledge into our embeddings. Building on a
previous extension of word2vec, we show that generalizing the notion of a
word's "context" to include arbitrary features creates an avenue for encoding
domain knowledge into word embeddings. We show that the word vectors produced
by this method outperform their text-only counterparts across the board in
correlation with clinical experts.

文中提出了使用专家领域知识来构建单词嵌入向量的方法，在临床自然语言处理中获得了可观的效果。通过将单词的 “上下文” 概念推广为包括任意特征，我们展示了使用这种方法产生的单词向量在与临床专家的相关性方面普遍优于其纯文本的对应方法。