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Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.

The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently.

Radiotherapy is one of the main treatments for tumor with increasingly high request for technique precision and the equipment stability. Machine learning may bring radiotherapy simplicity, individualization and precision, and may improve the automatic level of planning and quality assurance. Based on the process of radiotherapy, this paper reviews the applications and researches on machine learning, with an emphasis on deep learning, and proposes the prospects in the following aspects: segmentation of normal tissue and tumor, planning, treatment delivery, quality assurance and prognosis prediction.

Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can't continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.

The automatic classification of epileptic electroencephalogram(EEG)is significant in the diagnosis and therapy of epilepsy.A classification algorithm for epileptic EEG based on wavelet multiscale analysis and extreme learning machine(ELM)is proposed in this paper.Firstly,wavelet multiscale analysis is applied to the original EEG to extract its sub-bands.Then,two nonlinear methods,i.e.Hurst exponent(Hurst)and sample entropy(SamEn)are used to the feature extraction of EEG and its sub-bands.Finally,ELM algorithm is employed in epileptic EEG classification with the nonlinear features.The proposed method in this paper achieved 99.5% classification accuracy for the discrimination between epileptic ictal and interictal EEG.The result implies that this method has good prospects in the diagnosis and therapy of epilepsy.

With the increasing number of electrocardiogram(ECG)data,extensive application requirements of computer-aided ECG analysis have occurred.In the paper,we propose a variety of strategies to improve the performance of clinical ECG classification algorithm based on Lead Convolutional Neural Network(LCNN).Firstly,we obtained two classifiers by using different preprocessing methods and training methods in the study.Then,we applied the multiple output prediction method to both of them independently.Finally,the Bayesian approach was employed to fuse them.Tests conducted using more than 150 000 ECG records showed that the proposed method had an accuracy of 85.04% and the area under receiver operating characteristic curve(AUC)was 0.918 5,which significantly outperforms traditional methods based on feature extraction techniques.

Considering the survival rate of small animals and the continuity of the experiments,high-dose X-ray shooting process is not suitable for the small animals in computed tomography(CT)experiments.But the low-dose process results with images might be polluted by noises which are not conducive for the experiments.In order to solve this problem,we in this paper introduce a global dictionary learning based denoising method to apply the promotion of the low dose CT image.We at first adopted the K-means singular value decomposition(K-SVD)algorithm to train a global dictionary based on the high dose CT image.Then,the noise image could be decomposed into sparse component which was free from noise through the orthogonal matching pursuit(OMP)algorithm.Finally,the noisefree image could be achieved by reconstructing the image only with its sparse components.The experiments results showed that the method we proposed here could decrease the noise efficiently and remain the details,and it would help promote the low dose image quality and increase the survival rate of the small animals.