Voice biomarkers， as an emerging smart medical technology， are now being used in applications such as assisting in the diagnosis and treatment of diseases， facilitating accurate and personalized medical services for patients. However， it also raises many ethical issues， including informed consent， privacy protection， accuracy and reliability， data security， legal risks， and other issues. This paper systematically sorted out the ethical issues in the applications of voice biomarkers in the medical field， summarized these issues， such as informed consent， privacy protection， accuracy and reliability， data security， and legal risks， as well as explored the corresponding coping strategies. These countermeasures encompassed utilizing new media platforms to raise public awareness of voice biomarkers， strengthening supervision and management to promote the privacy protection of voice biomarkers， reducing algorithm biases to promote the general benefits of voice biomarkers to the public， establishing multidisciplinary teams to protect the data security of voice biomarkers， and encouraging medical professionals and researchers to participate in policy research， with a view to providing references for promoting and regulating the applications of voice biomarkers in the medical field.

Objective:To overview the systematic reviews of prevention and management measures of medication errors, so as to provide evidence support for clinical decision-making for medical staff.Methods:Cochrane Library, Australia Joanna Briggs Institute Evidence-based Healthcare Center database, CINAHL, PubMed, Embase, CNKI, SinoMed, Wanfang database and VIP database were searched by computer to search for systematic reviews of prevention and management measures of medication errors, and the search period was from establishment of the databases to June 30, 2023. Two researchers with systematic evidence-based training applied A Measure Tool to Assess Systematic Reviews 2 (AMSTAR 2) to evaluate the literature quality, and Grades of Recommendations Assessment, Development and Evaluation (GRADE) was used to evaluate the quality of outcome indicators.Results:Finally, a total of 19 systematic reviews were included. The overall quality evaluation using AMSTAR 2 was relatively low, with one article rated as high-quality, one article rated as low-quality and 17 articles rated as extremely low-quality. According to the evidence quality evaluation results of GRADE system for 55 outcome indicators of 19 systematic reviews, 3 pieces of evidence were medium, 27 pieces of evidence were low and 25 pieces of evidence were extremely low, indicating an overall low quality of evidence.Conclusions:The related researches on prevention and management of medication errors have been carried out extensively, and the computer system is one of the effective measures to reduce medication errors. The effectiveness of measures such as administration process modification, doctor/nurse education and training, double check, pharmacist intervention, automated dispensing cabinet/ automated pump and drug display is still unclear and needs to be further confirmed by large sample size and high-quality studies.

Talaromycosis marneffei has been increasing in recent years. Our understanding of this disease has gradually deepened through extensive basic and clinical research, but there are still many limitations. In this article, by incorporating the latest research advancements, we discuss important issues in managing Talaromycosis marneffei trends, aiming to guide effective prevention and control of the disease, improving public health, and reducing the healthcare burden.

Objective To investigate the affecting factors on auditory and speech performances in preschool children with unilateral cochlear implantation (CI) .Methods The clinical data of the preschool children (n=165) with unilateral cochlear implantation in the Second Xiangya hospital from January 2006 to April 2013 were collected . These children received rehabilitation according to the method recommended by the China Rehabilitation Research Center for Deaf Children ,and the data were analyzed retrospectively .The categories of auditory performance (CAP) and speech intelligibility rating (SIR) were used to assess their auditory and speech performances .The relationships between the performance and gender ,implanted age ,genotype ,inner ear malformation ,history of hearing aid were evaluated .Results Implanted ages and genotypes were associated with the auditory and speech performance of par‐ticipants (P<0 .05) ,while genders ,hearing aid experience ,and inner ear malformations(enlarged vestibular aque‐duct syndrome ,EVAS)were not significant related (P<0 .05) .Children were found to have achieved better CAP and SIR growths when CI was implanted during 1~3 years old and 2~4 years old ,respectively (P<0 .05) .The outcomes of CI recipients with GJB2 mutation were significantly better than those of the GJB2-nonrelated CI recipi‐ents (P<0 .05) .Conclusion This study provides evidence that CIs during first 1~3 years old having better auditory rehabilitation results than those of during 4~6 years old ,and CIs during 2~4 years old obtaining a better speech development in the first 12 months after operation .Deaf children with GJB2 mutation show better auditory and speech performances after CIs than those of the peers without GJB2 mutation .CIs can be effectively performed in deaf children associated with EVAs as in those without EVAS .

The extraction and identification of ECG (electrocardiogram) signal characteristic parameters are the basic steps toward ECG analysis and diagnosis. The fast and precise detection of QRS complexes is very important in ECG signal analysis, for it is the precondition of the correlative parameters calculation and diagnosis. In our work, firstly, we used the modulus value of complex wavelet decomposition to detect QRS complexes from ECG signal. As the shape and amplitude of ECG signal varies from person to person, we utilized the self-learning algorithm for adjusting the threshold to adapt the changes. The correct detection rate of QRS complexes is up to 99.81% based on MIT-BIH ECG data. Finally, we used the similar methods to detect the P and T waves, after QRS complexes detection.
Algorithms ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

In this paper is proposed a new method that approximates line segment with angle to control line as a basis for improving radial fitting. Experiments on selected records from the Massachusettes Institute of Technology and Boston's Beth Isral Hospital (MIT-BIH) arrhythmia database have revealed that the improved algorithm not only increases computation quantity, but also improves approximating quality and potentiates Real-time application of the linear approximation distance thresholding (LADT).
Algorithms ; Data Compression ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

The extraction and identification of ECG (electrocardiogram) signal characteristic parameters are the basis of ECG analysis and diagnosis. The fast and precise detection of QRS complexes is very important in ECG signal analysis; for it is a pre-requisite for the correlative parameters calculation as well as for correct diagnosis. In our work, firstly, the modulus maximum of wavelet transform is applied to the QRS complexes detection from ECG signal. Once there are mis-detections or missed detections happening, we utilize the Golden Section Search algorithm to adjust the threshold of maxima determination. The correct detection rate of the QRS complexes is up to 99.6% based on MIT-BIH ECG data.
Algorithms ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

Electrocardiogram (ECG) signal is important for physician to diagnose diseases. Various existing techniques on ECG classification have been reported. Generally, these techniques classify only two or three arrhythmias and need significantly long processing time. A new algorithm based on Support vector machine (SVM) is presented to solve the problem in this paper, which has been successfully applied to the classification of ECG. And in this paper are clarified the fundamental ideas of the classification of ECG based on SVM. Compared with the traditional neural network, this method is superior to it in theory. Because this new method deals with the minimization of the test samples, not the training samples.
Algorithms ; Artificial Intelligence ; Diagnosis, Computer-Assisted ; methods ; Electrocardiography ; methods ; statistics & numerical data ; Humans ; Models, Statistical ; Signal Processing, Computer-Assisted

The similarity measures between Vague sets are one of the most important technologies in Vague sets, In this paper, the new similarity measures based on Huang Guoshun's related works are presented and applied in electrocardiogram auto-recognition. Based on medical requiresments, in this paper, the characteristic parameters of signals from Massachusettes Institute of Technology (database) have been picked up and studied with BP neural network. In the end, the electrocardiogram samples are classified with the use of those characteristic parameters. The result shows that the accuracy of recognition goes up to 99.04%.
Algorithms ; Electrocardiography ; methods ; Fuzzy Logic ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted

An electrocardiogram (ECG) classify system based on the features of the ECG and neural network classification, which is the simulation of the real world situation, was present. First, a modified approach of the linear approximation distance thresholding (LADT) algorithm was studied and the features of the ECG were obtained. Then a neural network which can classify the multi-lead ECG data was trained with these features along the theory of the ECG diagnosis and the situation of ECG diagnosis in practice. Thus take a new idea for the ECG automatic analysis. The algorithm was tested using several ECG signals of MIT-BIH, and the performance was good. The correct rate of the trained wave is 100%, untrained is 78.2%.
Algorithms ; Databases, Factual ; Electrocardiography ; classification ; Neural Networks (Computer) ; Signal Processing, Computer-Assisted