Type of the Paper Article

Labels	Reason	Threshold values	0	1	2	3
4
Hypertension	High BP	(SBP≥120 and DBP≥80) or MBP≥105	X	X	X	○	X
Hypotension	Low BP	(SBP≤90 and DBP≤60) or MBP≤70	X	○	○	X	○
Tachycardia	High HR	HR ≥ 100	X	○	X	○	○
Bradycardia	Low HR	HR ≤ 60	X	X	○	X	X
Tachypena	High RR	RR ≥ 17	X	○	○	○	X
Bradypena	Low RR	RR ≤ 12	X	X	X	X	○
Hypoxia	Low SPO2	SPO2 ≤ 93%	X	○	○	○	○
Acronym			NNNN	THTH	BHTH	TTTH	THBH
Number of samples			145085	45186	31132	27915	12840

Table 4 categorizes unique clinical events in a patient's health status, based on simultaneous occurrences of specific vital signs deviations. Each clinical event is associated with an acronym and a distinct label. The acronyms THTH, BHTH, TTTH, and THBH denote combinations of abnormal vital signs including heart rate, blood pressure, breathing rate, and oxygen saturation. The NNNN category represents a normal state, where all vital signs are within the expected range. This classification is intended to facilitate the rapid and accurate identification of a patient's health condition, supporting timely and effective medical intervention.

Table 4. Classification of clinical events based on concurrent abnormalities or normalcy in patient vital signs.

Clinical events	Acronym	label
Simultaneous Tachycardia, Hypotension, Tachypnea, and Hypoxia	THTH	1
Simultaneous Bradycardia, Hypotension, Tachypnea, and Hypoxia	BHTH	2
Simultaneous Tachycardia, Hypertension, Tachypnea, and Hypoxia	TTTH	3
Simultaneous Tachycardia, Hypotension, Bradypnea, and Hypoxia	THBH	4
All six bio-signals are within the normal range	NNNN	0

3.3 MACHINE LEARNING PART
Machine learning can be applied to clinical event classification tasks in several ways. One common approach is to use supervised machine learning algorithms, such as decision trees, random forests, or support vector machines, to predict the class of a given clinical event based on a set of features or attributes. The algorithm is trained on a labeled dataset of past clinical events and their corresponding classes and then used to make predictions on new, unseen data. In a clinical event classification task, the features used as inputs to the machine learning algorithm could include demographic information, vital signs, laboratory test results, medications, and other relevant information. The target variable or output of the algorithm would be the class of the clinical event, such as sepsis, pneumonia, or a heart attack. Overall, using machine learning in the clinical event classification task can enhance the accuracy and efficiency of healthcare delivery by enabling the rapid and reliable identification of patients with specific conditions. This study implemented several ML methods to compare and get the best result on clinical event classification tasks, such as Random Forest Classifier, XGBoost classifier, AdaBoost classifier, Stochastic Gradient Decent, and Bayesian Ridge classifier.
3.3.1 RANDOM FOREST CLASSIFIER
Random Forest Classifier is a machine learning algorithm used in healthcare to predict outcomes, classify patients, and identify disease risk factors [30, 31]. It is a method of ensemble learning that combines multiple decision trees to enhance the accuracy and robustness of the model. In healthcare, Random Forest Classifier is often used in medical image analysis to detect and diagnose diseases like cancer, Alzheimer's, and cardiovascular disease. It can also predict readmission rates, length of hospital stays, and mortality rates. The algorithm works by randomly selecting subsets of the features and building a decision tree based on the selected features. The process is repeated multiple times to create a forest of decision trees. Each decision tree in the forest predicts the outcome during prediction, and the majority vote determines the final prediction. Random Forest Classifier is known for its ability to handle high-dimensional data, missing values, and noisy data. It is also less prone to overfitting compared to other machine learning algorithms. Overall, Random Forest Classifier is a powerful tool in healthcare for improving diagnosis and treatment outcomes.
3.3.2 LOGISTIC REGRESSION
Logistic regression is a statistical technique used to analyse datasets where independent variables determine outcomes. In healthcare, it is commonly employed to predict the probability of an event based on patient characteristics such as age, gender, medical history, and laboratory results. For example, logistic regression is employed to predict the probability of patients developing a particular disease using their demographic and clinical information [31]. It also aids in assessing the effectiveness of treatments or interventions by examining the relationship between the treatment and the outcome. A key advantage of logistic regression in healthcare is its simplicity and interpretability, allowing for informed decision-making and predictions. However, it assumes a linear relationship between the independent variables and the outcome, which may not always hold true in complex healthcare scenarios. Additionally, logistic regression's performance can be influenced by the quality and completeness of the training data. Biased or inaccurate predictions can arise from missing or incomplete data. Hence, it is crucial to thoroughly evaluate the data's quality and completeness when utilizing logistic regression in healthcare settings.
3.3.3 ADABOOST CLASSIFIER
Adaptive Boosting, or AdaBoost, is a boosting algorithm that can be used for both binary and multi-class classification problems. AdaBoost is also an ensemble learning method that combines multiple weak classifiers to create a strong and robust classifier [32]. The idea behind AdaBoost is to adjust the weights of the samples in the training data at each iteration to give more emphasis to the samples that are misclassified by the current ensemble of classifiers. In AdaBoost, a weak classifier is first trained on the data and used to make predictions. The samples misclassified by the weak classifier are given a higher weight, and a new weak classifier is trained on the reweighted data. This process is repeated multiple times, and the predictions of each weak classifier are combined to form the final prediction. AdaBoost is a simple and effective algorithm in various applications, including image and speech recognition, bioinformatics, and medical diagnosis.
3.3.4 STOCHASTIC GRADIENT DESCENT
Stochastic Gradient Descent (SGD) [36] is a widely utilized optimization algorithm for training various machine learning models, including classifiers. In SGD-based classifiers, the model learns to make predictions by updating its weights iteratively to minimize a loss function that measures the difference between the predicted outputs and the actual outputs. In the case of SGD classifiers, the weights are updated based on the gradient of the loss function, computed on a small subset of the training data called a mini batch. This contrasts with batch gradient descent, where the gradient is computed on the entire training set. Using mini batches makes SGD computationally efficient and allows the model to converge faster. SGD is particularly useful when dealing with large datasets, where computing the gradient on the entire dataset can be very expensive. Additionally, SGD is a flexible algorithm that can be used with several loss functions and regularization methods, making it suitable for a wide range of classification tasks. To use SGD for classification, one needs to define the loss function, the regularization method, and other hyperparameters, such as the learning rate and the size of the mini batches. In practice, a common approach is cross-entropy loss and L2 regularization, although other choices are possible depending on the task and the data.
3.3.5 GAUSSIAN CLASSIFIER
The Gaussian classifier, or the Gaussian naive Bayes classifier [37], is a probabilistic classification model used in ML. It is based on Bayes' theorem and assumes that the features of a dataset are independent and normally distributed. The model calculates the probability of a data point belonging to each class and assigns it to the class with the highest probability. The model is extensively employed in tasks such as text classification, spam filtering, and image recognition. It is a simple yet effective model and can manage large datasets with high-dimensional feature spaces. However, its assumption of feature independence may not hold in some datasets, which can lead to decreased accuracy.
3.4 FEDERATED LEARNING
Multiple parties can train a shared model using federated learning without sharing raw data. Instead, the raw data remains on the participants' devices, and only the model parameters are communicated and aggregated to form the final model. Each participant has a local model trained on its own data in a federated learning structure. The local models are then used to make predictions on new data, and the weights of the loss function concerning the model parameters are calculated. These weights are then communicated to a central server, which aggregates the weights and updates the global model parameters. The updated model parameters are then sent back to the participants, and the process is repeated until the model has converged.
Federated learning trains models on data distributed across many parties or devices, like hospitals in healthcare or individual devices. It allows for shared model training, such as in clinical event classification, without compromising privacy, as raw data never leaves the local device. This cross-institution and cross-device learning approach ensures data security and privacy, making it valuable in a data-driven world.
3.4.1 Hyperparameters in Federated Learning
Hyperparameters in federated learning are crucial settings that determine the behavior and performance of the learning algorithm. They are chosen prior to training and play a significant role in achieving optimal model performance. Common hyperparameters in federated learning include the learning rate, the number of communication rounds, the aggregation method, regularization parameters, batch size, and the number of local training epochs. The learning rate controls the step size during optimization, while the number of communication rounds determines the total number of iterations. The aggregation method specifies how updates from multiple clients are combined, and regularization parameters help prevent overfitting. The batch size determines the number of data samples used for local updates, and the number of local training epochs defines the iterations on each client's data. Choosing appropriate hyperparameter values is crucial for achieving efficient convergence, accuracy, and robustness in federated learning models.
3.4.2 Flower Framework
F
lower [38] is a federated learning method that aims to improve the performance and fairness of federated learning models. It stands for fairness, accuracy, and privacy in federated learning and is based on differential privacy. In the Flower federated learning method, the participants first locally train their models on their own data and then send their model parameters to the central server. The central server then computes a global model by aggregating the model parameters while adding noise to the aggregated weights to ensure differential privacy, as shown in figure 2.
Figure 2. Configuration diagram of FL operation that manages the FL lifecycle.
Flower is a flexible, friendly, and fast framework designed for Federated Learning (FL). The primary components of the Flower FL architecture include the server, the clients, and the gRPC communication layer which interconnects them.
In this setup, the server plays a central role in coordinating the entire FL process. Its responsibilities encompass orchestrating the federated learning process, facilitating communication with participating clients, receiving model updates from clients, and aggregating these updates using a designated strategy, such as Federated Averaging (FedAvg). Once the updates have been aggregated, the server sends the newly updated global model back to the clients, keeping them synchronized and allowing the learning process to continue iteratively.
3.4.3 Federated optimization algorithm FedAvg
FedAvg [39] is an algorithm commonly used in Federated Learning (FL). It's used to aggregate the model updates sent by different clients to the server in an FL setup. The server initializes a global model and sends it to the selected clients for training. Each participating client receives a copy of the global model and trains it on their local data for several epochs, producing a local model. After local training, each client sends the weights of their local model back to the server. The server then aggregates these local model weights into a new global model. The aggregation is usually a weighted average, where the weights could be proportional to the number of samples each client has. This is the key step that constitutes Federated Averaging (1). It ensures that the new global model is representative of the data from all clients. The aggregated model becomes the new global model and is sent back to the clients for the next round of training. The last stage is iteration, that process is repeated over multiple rounds until the global model's performance reaches a satisfactory level or other stopping criteria are met.
(1)
– model weights on communication rounds #t, - model weights on communication rounds #t on client k, µ learning rate,

Algorithm 1. FederatedeAveraging. The K clients are indexed by k; B is local minibatch size, E is the number of local epochs, and η is the learning rate.