Additionally, the LSTM model in [44] considers the interdependence between hours of the same day. The LSTM model [45] performs hyper-parameter tuning to find the optimal values of the parameters. After that, further data is added to see how it affects the improved model. In [46], LSTM ...
Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learnin
Machine learning (ML) enables a system to scrutinize data and deduce knowledge. It goes beyond simply learning or extracting knowledge, to utilizing and improving knowledge over time and with experience. In essence, the goal of ML is to identify and exploit hidden patterns in “training” data....
In the field of micro-service system fault detection, a wide range of techniques have been proposed and widely applied. These techniques can be generally categorized into two major groups: machine learning methods, and deep learning methods. Machine learning methods: They have been widely applied i...
Synthetic data generation describes the process of learning the underlying distribution of a given real dataset in a model, which is, in turn, sampled to produce new data objects still adhering to the original distribution. This approach often finds application where circumstances limit the availabilit...
Subsequently, a global context vector can be computed based on the code weights, and all medical code representations undergo weighted aggregation to form the final visit representation in the form of an embedding matrix. During model training, we also append the lab values to the visit ...
In UGAN-GRUD, to overcome the challenge of capturing the distribution pattern of high-missing-rate datasets, we introduce the uncertainty matrix unit U into GAN to form the UGAN, which is an improvement not considered in existing methods. To utilize the time interval information and U in the...
machine learning, disregarding the temporal structure of TS. However, in so doing, most of the information related to the time domain may be lost. A third type, the symbolic approximation technique, has recently drawn much attention, in which raw TS is converted into symbols of arbitrary ...
In contrast to traditional machine learning methods, our DL-based method takes sequence data in the form of windows directly as an input, reducing the need for hand-crafted feature extraction. A pre-requisite for this approach is that the sequence data must be encoded in a form that is reada...
advantages in HAR, especially CNN and LSTM because of CNN’s automated feature extraction capability and LSTM’s ability to persist older information from time series data. Yang et al. [44] used CNN’s ability of automatic feature learning and found it to outperform four conventional machine lea...