Intelligent Medicine Research Articles

A Blockchain-Based Auditable Access Control System for Private Data in Service-Centric IoT Environments

Internet of Things (IoT) devices are widely considered in smart cities, intelligent medicine, and intelligent transportation, among other fields that facilitate people's lives, producing a large amount of private data. However, due to the mobility, limited performance, and distributed deployment of IoT, traditional access control methods cannot support the security of private data's access control process in current IoT environments. To address such problems, this article proposes an auditable access control model, based on an attribute-based access control model, and manages the access control policy for private data through the request record, the response record, and the access record stored in the blockchain network. Additionally, a Blockchain-based auditable access control system is also proposed based on the auditable access control model, ensuring private data security in IoT environments and realizing effective management and auditable access to these data. Experimental results show that the proposed system can maintain high throughput while ensuring private data security for real application scenarios in IoT environments.

An ultralight, flexible, and biocompatible all-fiber motion sensor for artificial intelligence wearable electronics

New-generation human body motion sensors for wearable electronics and intelligent medicine are required to comply with stringent requirements in terms of ultralight weight, flexibility, stability, biocompatibility, and extreme precision. However, conventional sensors are hard to fulfill all these criteria due to their rigid structure, high-density sensing materials used as the constituents, as well as hermetical and compact assembly strategy. Here, we report an ultralight sensing material based on radial anisotropic porous silver fiber (RAPSF), which has been manufactured by phase separation and temperature-controlled grain growth strategy on a modified blow-spinning system. The resistance of RAPSF could be dynamically adjusted depending on the deflected shape. Furthermore, an all-fiber motion sensor (AFMS) with an ultra-low density of 68.70 mg cm−3 and an overall weigh of 7.95 mg was fabricated via layer-by-layer assembly. The sensor exhibited outstanding flexibility, breathability, biocompatibility, and remarkable body motion recognition ability. Moreover, the AFMS was shown to have great potential as an artificial intelligence throat sensor for throat state identification at the accuracy above 85%, allowing one to spot the early onset of the viral throat illness.

Wearable Electronics Powered by Triboelectrification between Hair and Cloth for Monitoring Body Motions

Diverse triboelectric nanogenerators (TENGs) have been extensively applied in self‐powered wearable electronics. The TENG‐based demonstration using the human body as a triboelectric material is rare, though wearable electronics plays a more and more pivotal role in portable intelligent medicine. Human hair, as part of the human body, can be a component of a TENG‐based wearable system. Herein, a novel body‐based TENG configuration made of human hair and polydimethylsiloxane (PDMS)‐coated carbon cloth is proposed. Due to triboelectrification between the hair and the PDMS layer, the output voltage can deliver 1.713 and 0.377 V in the vertical contact‐separation mode and sliding mode, respectively. In addition, a self‐powered application based on the hair‐based TENG for actively monitoring the motion state by comparing the amplitude and frequency of the output voltages is successfully demonstrated. This study broadens the application of body‐based TENGs and provides a promising and feasible strategy for developing smart health monitoring.

Intelligent Medicine Kit for Healthcare Monitoring, An IOT Based Solution

Increase in chronic diseases worldwide demands efficient healthcare solutions for maintaining well-beingof people. Treatment requires timely in-take of medicines and strict adherence to routine. This lack of adherenceis estimated to cause many deaths and hospitalizations. If we can get people to take their medications regularly,they won’t develop complications. In addition to improving patient outcomes, medication adherence will reducehealth care costs associated with these conditions. Healthcare monitoring solutions based on Internet of Things(IoT) technology has drawn significant research attention. This paper proposes an IoT based user configurablecustomized intelligent medicine kit augmented with Wi-Fi and Bluetooth Low Energy technologies. It hascapability to detect whether patient is taking all prescribed medicines on fix schedule and intelligentlycommunicates the same to patient and their close relatives using uniquely created Four Tier Notification System(FTNS), thus helping patient to live a healthy life. The paper discusses a novel theme on the functioning of amedical grade device which would consume information from sensor and send it to the central server with amaximum possibility of success using Four Tier Notification System (FTNS). This novel approach discusseshandshaking of connection to the central serve with a fallback mechanism to achieve maximum success of datasynchronization. This paper also discusses how this useful data from medicine kit can help healthcare sector toclosely track patient's physical activities and helps to influence the way healthcare sector operates in future.

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.

BackgroundNowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy.ObjectiveThis study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases.MethodsThe relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data.ResultsThe data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%.ConclusionsThe main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.

Study of Trunk Morphological Imbalance and Rehabilitation Outcome of Adolescent Idiopathic Scoliosis with Intelligent Medicine.

In recent years, artificial intelligence technology has been widely used in various medical fields to effectively assist physicians in patient treatment operations. In this paper, we design and implement a deep biblical network model-based orthotic design for adolescent idiopathic scoliosis to quickly and effectively assist physicians in designing orthotics for adolescent idiopathic scoliosis. A fuzzy set is used to express the knowledge of adolescent idiopathic scoliosis orthosis design, and a fuzzy reasoning based on the confidence level is implemented. Finally, the efficiency of the design of adolescent idiopathic scoliosis orthoses was improved by 50% through two cases of adolescent idiopathic scoliosis patients, and the deviation rate between the inference value and the actual operation value of the domain experts was less than 10%.

MTAL: A Novel Chinese Herbal Medicine Classification Approach with Mutual Triplet Attention Learning

Chinese herbal medicine classification is a critical task in medication distribution and intelligent medicine, as well as a significant topic in computer vision. However, the majority of contemporary mainstream techniques are semiautomatic, with low efficiency and performance. To tackle this problem, a novel Chinese herbal medicine classification approach, Mutual Triplet Attention Learning (MTAL), is proposed. The motivation of our approach is to leverage a group of student networks to learn collaboratively and teach each other about cross-dimension dependencies throughout the training process, with the goal of quickly gaining strong feature representations and improving the outcomes. The results of the experiments show that MTAL outperforms other models in terms of accuracy and computation time. MTAL, in particular, improves accuracy by over 5.5 percent while reducing calculation time by over 50 percent.

Expert recommendations on collection and annotation of otoscopy images for intelligent medicine

Middle and outer ear diseases are common otological diseases worldwide. Otoscopy and otoendoscopy examinations are essential first steps in the evaluation of patients with otological diseases. Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy, leading to delays in treatment or complications. Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future. However, the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems, and no standardized process for data acquisition, and annotation of otoscopy images in intelligent medicine has yet been fully established. The standards for data storage and data management are unified with those of other specialties and are introduced in detail here. This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine; it would thus lay a solid foundation for the standardized collection, storage, and annotation of otoscopy images and the application of training algorithms, and promote the development of automatic diagnosis and treatment for otological diseases. The full text introduced image collection (including patient preparation, equipment standards, and image storage), image annotation standards, and quality control.

Hybrid Janus Membrane with Dual-Asymmetry Integration of Wettability and Conductivity for Ultra-Low-Volume Sweat Sensing.

Highly sensitive and selective analysis of sweat at ultra-low sample volume remains a major challenge in the field of biosensing. Manipulation of small volumes of liquid for efficient sampling is essential to address this challenge. A hybrid Janus membrane with dual-asymmetry integration of wettability and conductivity is developed for regulated micro-volume liquid transport in wearable sweat biosensing. Unlike the uncontrollable liquid diffusion in a conventional porous membrane, the asymmetric wettability of porous Janus membrane leads to unique unidirectional liquid transport with high breakthrough pressure (1737.66 Pa) and fast self-pumping rate (35.94 μL/min) for micro-volume liquid sampling. The asymmetric conductive layer shows excellent flexible conductivity, anti-interference of friction, and efficient electrochemical interface due to the in situ generation of gold nanoparticles on one side of the membrane. The fabricated Pt-enzyme electrodes on the membrane promises effective testing range, great selectivity, and high sensitivity and accuracy (correlation efficiency, glucose: R2 = 0.999, lactate: R2 = 0.997), enabling ultra-low volume (∼0.15 μL) real time measurements on the skin surface. The innovative Janus membrane with unidirectional, self-pumping, and anti-interference performance provides a new strategy for miniaturized wearable microfluidic sweat electrochemical biosensor preparation in athletic performance evaluation, health monitoring, disease diagnosis, intelligent medicine, and so forth.

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data.

This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.

The Design of the Intelligent Medicine Box for the Elderly

With the increase in the number of elderly people in the world, the problem of population aging has gradually become prominent. People are paying more and more attention to the elderly, and medical supplies for the elderly have also attracted great attention. At present, various intelligent medical boxes have appeared in the domestic and foreign markets. These medicine boxes mainly include functions such as setting time, reminding to take medicine regularly, and SMS notification service. This project designed an intelligent medicine box, which is based on 80C51 single-chip microcomputer, loaded with infrared on-beam sensor, three-axis acceleration sensor, short message module, and Bluetooth module. The main functions of the system include intelligent detection module, timing reminder function, short message notification module and anti-fall detection module. On the one hand, the intelligent system can monitor and remind the elderly to take medications in time. On the other hand, it can also monitor the personal safety of the elderly alone at home. This is different from the current intelligent medical box on the market and is also the highlight of the system.

A Lightweight and Robust User Authentication Protocol with User Anonymity for IoT-Based Healthcare

With the rise of the Internet of Things (IoT), the word “intelligent medical care” has increasingly become a major vision. Intelligent medicine adopts the most advanced IoT technology to realize the interaction between patients and people, medical institutions, and medical equipment. However, with the openness of network transmission, the security and privacy of information transmission have become a major problem. Recently, Masud et al. proposed a lightweight anonymous user authentication protocol for IoT medical treatment, claiming that their method can resist various attacks. However, through analysis of the protocol, we observed that their protocol cannot effectively resist privileged internal attacks, sensor node capture attacks, and stolen authentication attacks, and their protocol does not have perfect forward security. Therefore, we propose a new protocol to resolve the security vulnerabilities in Masud’s protocol and remove some redundant parameters, so as to make the protocol more compact and secure. In addition, we evaluate the security and performance of the new protocol and prove that the overall performance of the new protocol is better than that of other related protocols.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine.

As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What’s more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Brain Activity Recognition Method Based on Attention-Based RNN Mode

Brain activity recognition based on electroencephalography (EEG) marks a major research orientation in intelligent medicine, especially in human intention prediction, human–computer control and neurological diagnosis. The literature research mainly focuses on the recognition of single-person binary brain activity, which is limited in the more extensive and complex scenarios. Therefore, brain activity recognition in multiperson and multi-objective scenarios has aroused increasingly more attention. Another challenge is the reduction of recognition accuracy caused by the interface of external noise as well as EEG’s low signal-to-noise ratio. In addition, traditional EEG feature analysis proves to be time-intensive and it relies heavily on mature experience. The paper proposes a novel EEG recognition method to address the above issues. The basic feature of EEG is first analyzed according to the band of EEG. The attention-based RNN model is then adopted to eliminate the interference to achieve the purpose of automatic recognition of the original EEG signal. Finally, we evaluate the proposed method with public and local data sets of EEG and perform lots of tests to investigate how factors affect the results of recognition. As shown by the test results, compared with some typical EEG recognition methods, the proposed method owns better recognition accuracy and suitability in multi-objective task scenarios.

Expert recommendations on data collection and annotation of two dimensional ultrasound images in azoospermic males for evaluation of testicular spermatogenic function in intelligent medicine

Testicular two-dimensional ultrasound is a testing modality that is often used to evaluate azoospermia and other related diseases. With the continuous development of deep learning in recent years, the combination of deep learning and testicular ultrasound appears unstoppable despite a lack of relevant standards. One of the major problems associated with the digitization of ultrasound images is the uneven quality of data however, and a standardized data source and acquisition process has not yet been developed. Such a standard could fill the current gap, and establish acquisition criteria for ultrasound images of testes during the male reproductive period, including grayscale ultrasound, shear wave elastography, and contrast-enhanced ultrasound. By following these guidelines the quality of testicular ultrasound images would be improved and standardized, which would lay a solid foundation for the standardization of testicular ultrasound images, and assist automated evaluation of testicular spermatogenic function of whole testis in azoospermic males.

Personality Classification of Social Users Based on Feature Fusion.

Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechanism to extract personality information from user data, which leads to semantic loss to some extent. In addition, researchers encode scattered user posts in a sequential or hierarchical manner, ignoring the connection between posts and the unequal value of different posts to classification tasks. We propose a hierarchical hybrid model based on a self-attention mechanism, namely HMAttn-ECBiL, to fully excavate deep semantic information horizontally and vertically. Multiple modules composed of convolutional neural network and bi-directional long short-term memory encode different types of personality representations in a hierarchical and partitioned manner, which pays attention to the contribution of different words in posts and different posts to personality information and captures the dependencies between scattered posts. Moreover, the addition of a word embedding module effectively makes up for the original semantics filtered by a deep neural network. We verified the hybrid model on the MyPersonality dataset. The experimental results showed that the classification performance of the hybrid model exceeds the different model architectures and baseline models, and the average accuracy reached 72.01%.

Research on Data Analysis of Traditional Chinese Medicine with Improved Differential Evolution Clustering Algorithm.

Medical data analysis is an important part of intelligent medicine, and clustering analysis is a commonly used method for data analysis of Traditional Chinese Medicine (TCM); however, the classical K-Means algorithm is greatly affected by the selection of initial clustering center, which is easy to fall into the local optimal solution. To avoid this problem, an improved differential evolution clustering algorithm is proposed in this paper. The proposed algorithm selects the initial clustering center randomly, optimizes and locates the clustering center in the process of evolution iteration, and improves the mutation mode of differential evolution to enhance the overall optimization ability, so that the clustering effect can reach the global optimization as far as possible. Three University of California, Irvine (UCI), data sets are selected to compare the clustering effect of the classical K-Means algorithm, the standard DE-K-Means algorithm, the K-Means++ algorithm, and the proposed algorithm. The experimental results show that, in terms of global optimization, the proposed algorithm is obviously superior to the other three algorithms, and in terms of convergence speed, the proposed algorithm is better than DE-K-Means algorithm. Finally, the proposed algorithm is applied to analyze the drug data of Traditional Chinese Medicine in the treatment of pulmonary diseases, and the analysis results are consistent with the theory of Traditional Chinese Medicine.

Multivariate saturation treatment for pancreatic neuroendocrine neoplasms with liver metastasis

Pancreatic neuroendocrine neoplasms (pNENs) are relatively rare clinical diseases, accounting for about 4%-5% of all primary pancreatic tumors, and 60% of patients had had distant metastases when pNENs were diagnosed, with liver being the most frequently involved metastatic organ. Currently, treatment options for pNENs with liver metastasis include surgery, local embolization and ablation, chemotherapy, targeted therapy represented by everolimus and bevacizumab, somatostatin receptor therapy and immunotherapy. However, how to accurately evaluate the condition of patients at different stages of the disease, choose the above-mentioned treatment methods and optimize the treatment effect is a hot and difficult research topic. As a new tumor treatment concept in the era of intelligent medicine recently proposed by the author, the concept of multivariate saturation treatment used artificial intelligence technology to integrate existing tumor treatment methods, combined with the individual heterogeneity of patients; used the deep learning technology of artificial intelligence to accurately assess disease stages and predict the possible response of the corresponding treatment; used the robotic surgery platform to adopt the most suitable surgical method for the patient, which was dynamically adjusted at different stages of tumor treatment to maximize and optimize the treatment effect. Multivariate saturation treatment can provide the possibility to achieve the optimal prognosis of the patient.

Analysis on Applied Effect of Intelligent Medicine Integration System Construction Combined with Problem-based Learning Approach in Operation Room Nursing Management and Training

To explore the applied effect of intelligent medicine integration system construction combined with problem-based learning (PBL) approach in operating room nursing management and training. 120 nursing staff working in our hospital from January 2019 to January 2020 were selected as the research object and randomly divided into group A (n=60) and group B (n=60), with the routine PBL approach and the PBL approach combined with intelligent medicine integration system performed to group B and group A respectively to compare the assessment results of operating room nursing management, scores on evaluation of the teaching methods, incidence rates of adverse nursing events, nursing management quality and patient satisfaction. Compared with group B, the trained nursing staff in group A had significantly better performance in operating room nursing management (P<0.001), higher score on evaluation of the teaching method (P<0.001), lower incidence rate of adverse nursing events in operating room (P<0.05), higher nursing management quality (P<0.001), and higher patient satisfaction (P<0.05). The intelligent medicine integration system construction combined with the PBL approach can effectively improve the comprehensive quality of nursing staff, optimize the quality of operating room nursing management, and lower the incidence of adverse nursing events, which should be promoted in clinical practice.

The Current Situation and Influencing Factors of the Alarm Fatigue of Nurses' Medical Equipment in the Intensive Care Unit Based on Intelligent Medical Care.

After rapid development and reform, the health level and medical diagnosis and treatment capabilities of Chinese residents have been significantly improved, and high-quality medical resources have significantly improved the life safety and health of the masses. As the most concentrated area of medical equipment in the hospital, the intensive care unit produces the most alarms during the operation of the equipment. The intensive care unit nurses are responsible for heavier nursing work, and the problem of alarming in other departments is more prominent. Therefore, this paper presents an analysis and research on the current situation and influencing factors of the alarm fatigue of nurse medical equipment in the intensive care unit based on intelligent medicine. This article uses a variety of related methods such as literature data method and questionnaire survey method to deeply study the theoretical knowledge of intelligent medical treatment, medical equipment alarm fatigue device, and so on. The logistic regression analysis method is introduced to classify its influencing factors, and the analysis experiment on the influencing factors of the medical equipment alarm fatigue of nurses in the intensive care unit is designed. The nurses' cognition of clinical alarms and the analysis of clinical alarm fatigue questionnaire data are studied. The alarm fatigue of nurses in the intensive care unit is at a severe level, which needs to be taken seriously in the intensive care unit. Unmarried, high-level positions, long working years, high professional titles, and high education are negatively correlated with alarm fatigue (P < 0.05), and those without an alarm habit are positively correlated with alarm fatigue (P < 0.05), and the number of night shifts per month is related to alarm fatigue. There is no correlation between them (P > 0.05).