Objectives: Metabolic Bariatric Surgery (MBS) is a critical intervention for
patients living with obesity and related health issues. Accurate classification
and prediction of patient outcomes are vital for optimizing treatment
strategies. This study presents a novel machine learning approach to classify
patients in the context of metabolic bariatric surgery, providing insights into
the efficacy of different models and variable types. Methods: Various machine
learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN
with RandomOverSampler, and KNN with SMOTE, were applied to a dataset of 73
patients. The dataset, comprising psychometric, socioeconomic, and analytical
variables, was analyzed to determine the most efficient predictive model. The
study also explored the impact of different variable groupings and oversampling
techniques. Results: Experimental results indicate average accuracy values as
high as 66.7% for the best model. Enhanced versions of KNN and Decision Tree,
along with variations of KNN such as RandomOverSampler and SMOTE, yielded the
best results. Conclusions: The study unveils a promising avenue for classifying
patients in the realm of metabolic bariatric surgery. The results underscore
the importance of selecting appropriate variables and employing diverse
approaches to achieve optimal performance. The developed system holds potential
as a tool to assist healthcare professionals in decision-making, thereby
enhancing metabolic bariatric surgery outcomes. These findings lay the
groundwork for future collaboration between hospitals and healthcare entities
to improve patient care through the utilization of machine learning algorithms.
Moreover, the findings suggest room for improvement, potentially achievable
with a larger dataset and careful parameter tuning.

代谢性肥胖手术属于肥胖症患者和相关健康问题的重要干预措施，通过一种新颖的机器学习方法对患者进行分类，研究揭示了不同模型和变量类型的有效性，为优化治疗策略提供了洞察力。

机器学习算法在代谢性肥胖手术术后成功率分类中的应用：一项全面的研究

Application of Machine Learning Algorithms in Classifying Postoperative  Success in Metabolic Bariatric Surgery: A Comprehensive Study

In this paper, we propose a natural language processing architecture that can
handle tasks that previously required two models as one model. With a single
model, we analyze the language patterns and conversational context of
Alzheimer's patients and derive answers from two results: patient
classification and chatbot. If the patient's language characteristics are
identified by chatbots in daily life, doctors can plan more precise diagnosis
and treatment for early diagnosis. The proposed model is used to develop
chatbots that replace questionnaires that required experts. There are two
natural language processing tasks performed by the model. The first is a
'natural language classification' that indicates with probability whether the
patient has an illness, and the second is to generate the next 'answer' of the
chatbot to the patient's answer. In the first half, a context vector, which is
a characteristic of patient utterance, is extracted through a self-attention
neural network. This context vector and chatbot (expert, moderator) questions
are entered together into the encoder to obtain a matrix containing the
characteristics of the interaction between the questioner and the patient. The
vectorized matrix becomes a probability value for classification of patients.
Enter the matrix into the decoder with the next answer from the chatbot (the
moderator) to generate the next utterance. As a result of learning this
structure with DmentiaBank's cookie theft description corpus, it was confirmed
that the value of the loss function of the encoder and decoder was
significantly reduced and converged. This shows that capturing the speech
language pattern of Alzheimer's disease patients can contribute to early
diagnosis and longitudinal studies of the disease in the future.

本文提出了一个自然语言处理的架构，该架构可以处理以往需要两个模型完成的任务。通过单一模型，我们分析阿尔茨海默病患者的语言模式和对话上下文，并从两个结果中得出答案：患者分类和聊天机器人。如果聊天机器人在日常生活中识别了患者的语言特征，医生可以为早期诊断制定更精确的诊断和治疗计划。该模型用于开发替代需要专家问卷的聊天机器人。模型执行两个自然语言处理任务：一是 “自然语言分类”，指示患者是否患病的概率，二是生成聊天机器人接下来的 “回答”。通过自我注意神经网络提取患者发言的上下文向量，在编码器中将其与聊天机器人的问题一起输入，以获取包含提问者和患者之间交互特征的矩阵。矢量化的矩阵成为患者分类的概率值。将矩阵与聊天机器人（调解员）下一个答案一起输入到解码器中，以生成下一个发言。通过使用 DmentiaBank 的 cookie theft 描述语料库学习该结构，证实了编码器和解码器的损失函数值显著降低和收敛。这表明，捕捉阿尔茨海默病患者的言语语言模式可以为将来的早期诊断和疾病纵向研究做出贡献。

使用分层注意力和 Transformer 的阿尔茨海默病诊断和生成聊天机器人

Alzheimer's Diagnosis and Generation-Based Chatbot Using Hierarchical Attention and Transformer

Bedside monitors in Intensive Care Units (ICUs) frequently sound incorrectly,
slowing response times and desensitising nurses to alarms (Chambrin, 2001),
causing true alarms to be missed (Hug et al., 2011). We compare sliding window
predictors with recurrent predictors to classify patient state-of-health from
ICU multivariate time series; we report slightly improved performance for the
RNN for three out of four targets.

本文探讨了 ICU 中由于床边监测器发出过多错误的警报声导致护士对警报的反应迟缓和麻木，进而导致忽略真正的警报信号的问题，并比较了滑动窗口预测器和递归神经网络在分类 ICU 多元时间序列中患者健康状况的表现，发现在四个指标中有三个指标 RNN 表现更佳。