Intelligent Medical Research Articles

Intelligent Medical Velostat Pressure Sensor Mat Based on Artificial Neural Network and Arduino Embedded System

The promising research on flexible and tactile sensors requires conducting polymer materials and an accurate system for the transduction of pressure into electrical signals. In this paper, the intelligent sensitive mat, based on Velostat, which is a polymeric material impregnated with carbon black, is investigated. Various designs and geometries for home-made sensor mats have been proposed, and their electrical and mechanical properties, including reproducibility, have been studied through the tests performed. The mat pressure sensors have been interfaced with an Arduino microcontroller in order to monitor, read with high precision, and control the variation of the resistance under applied pressure. An approximation method was then developed based on a neural network algorithm to explore the relationship between different mat shapes, the pressure and stresses applied on the mat, the resistance of the conductive Velostat material, and the number of active sensing cells in order to control system input signal management.

Intelligent medication manager: developing and implementing a mobile application based on WeChat.

Background: Time and space constraints have often hindered the provision of optimal pharmaceutical care, limiting medication therapy management. Social media tools have gained significant popularity in the field of pharmaceutical care. This study aimed to develop a WeChat-based intelligent medication manager platform that facilitates online pharmaceutical care and encourages self-management. Methods: We developed a WeChat-based Internet pharmacy service platform called Xiang Medicine Guidance (XMG). Through the analysis of surveys and user access data, we evaluated the demand and utilization of the XMG platform and assessed patients' satisfaction with its services. Patients' adherence before and after the XMG platform intervention was also investigated. Results: The XMG platform was launched in November 2022, offering medication guidance, reminders, and consultation services through the WeChat mini-program. By the end of April 2023, the platform had attracted 141.2 thousand users, accumulating 571.0 thousand visits. Moreover, 1,183 clients sought online medication consultations during this period. Six months after the launch of XMG, an impressive 91.02% of users expressed their satisfaction with the platform. The medication reminders and consultations provided by XMG significantly contributed to medication adherence, with 56.02% of users categorized as having good adherence, better than the previous 47.26%. Conclusion: Through its services and features, XMG empowers patients to better manage their medications, seek professional advice, and adhere to their prescribed treatment plans. XMG has the potential to positively impact public health on a broader scale.

Application of Intelligent Medical Sensing Technology.

With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues in order to conform to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, and stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation, while traditional medical sensors cannot meet such requirements. In addition, to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. Based on the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

Retracted: Meta-Analysis of Long-Term Efficacy of Proliferative Diabetic Retinopathy Based on Intelligent Medical Treatment.

[This retracts the article DOI: 10.1155/2022/6545553.].

Retracted: Influence of Taijiquan Exercise on Mentality and Emotion Regulation by Intelligent Medical Big Data Analysis.

[This retracts the article DOI: 10.1155/2022/5468317.].

Retracted: Meta-Analysis of the Relationship between the Prognosis of Acute Cerebral Infarction Intravenous Lysis and Cerebral Microbleeds Based on Intelligent Medical Care.

[This retracts the article DOI: 10.1155/2022/4329318.].

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

[This retracts the article DOI: 10.1155/2021/9994303.].

Intelligent Medical Knowledge Robot Based on Knowledge Graph

Artificial intelligence has been gradually integrated into life, and knowledge graph is also particularly important, and its application is gradually maturing. In recent years researchers have used the rich entity and relationship information in the knowledge graph to overcome many difficulties. Through the fusion, reasoning, and deep learning of large-scale knowledge graphs, knowledge graphs link and apply these memories to generate wisdom. It can be said that knowledge graph has become the infrastructure of the era of artificial intelligence. The article combines Neo4j with ECharts visualization to develop a visual question answering robot in the medical field. This uses a more scientific method to understand and present data, provides a humanized visual question answering robot. And finally shows the effect of visual knowledge graph as well as assists users in the analysis of specific diseases.

Development Trend and Investment Prospect of Intelligent Medication Management in China

With the implementation of the policy of “hierarchical diagnosis and treatment” in China and the gradual establishment of local medical associations, the service capacity of basic medical and health institutions has been improved, and some patients with common diseases will make more use of primary medical resources to relieve the overload of leading hospitals, thus giving rise to the extensive demand for the information management system of primary medical and health institutions, and promoting the expansion of the downstream market of the industry. Catalyzed by the epidemic, new domestic bases are expected to further drive the expansion of the intelligent medication management industry. Therefore, this paper analyzes the current development status of this industry including analysis on which specific area is now the most profitable and which one has the greatest potential in the future. The analysis also includes plights, new trends, and market demands in the industry. Most importantly, the main target is to discuss the prospect of development and potential in investment in this industry in China.

P051 Development and evaluation of a text-analytics algorithm for automated application of the BSR COVID-19 shielding criteria in rheumatology patients

Abstract Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at the highest risk of COVID-19 requiring shielding. This guidance was based on patients’ age, comorbidities, and immunosuppressive therapies - including biologics that are not captured in primary care records. This meant rheumatologists needed to manually review outpatient letters to score patients’ risk. The process required considerable clinician time, with shielding decisions not always transparently communicated. Our aim was to develop an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review. Methods Rheumatology outpatient letters from Salford Royal Hospital, a large UK tertiary hospital, were retrieved between 2013-2020. The two most recent letters for each patient were extracted, created before 01.04.2020 when BSR guidance was published. Free-text diagnoses were processed using Intelligent Medical Objects® software1 (Concept Tagger), which utilised interface terminology for each condition mapped to a SNOMED-CT code. We developed the Medication Concept Recognition tool (MedCore Named Entity Recognition) to retrieve medications type, dose, duration and status (active/past) at the time of the letter. The medication status was established based on the heading where they appeared (e.g. past medications, current medications), but incorporated additional information such as medication stop dates. The age, diagnosis and medication variables were then combined to output the BSR shielding score. The algorithm’s performance was calculated using clinical review as the gold standard. Results To allow for the comparison with manual decisions, we focused on all 895 patients who were reviewed clinically. 64 patients (7.1%) had not consented for their data to be used for research as part of the national opt-out scheme. After removing duplicates, 803 patients were used to run the algorithm. 11,558 free-text diagnoses were extracted and mapped to SNOMED CT, with 15,003 free-text medications (that included past, present and any planned treatment). The automated shielding algorithm demonstrated a sensitivity of 80.3% (95% CI: 74.7, 85.2%) and specificity of 92.2% (95% CI: 89.7, 94.2%). Positive likelihood ratio was 10.3 (95% CI: 7.7, 13.7), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28), F1 score was 0.81. False positive rate was 7.9%, whilst false negative rate was 19.7%. Further evaluation of false positives/negatives revealed clinician interpretation of BSR guidance and misclassification of medications status were important contributing factors. Conclusion An automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and transparently communicating decisions based on individual risk. With further development, it has the potential to be adapted for future public health initiatives that requires prompt automated review of hospital outpatient letters. Disclosure M. Jani: None. G. Alfattni: None. M. Belousov: None. Y. Zhang: None. M. Cheng: None. K. Webb: None. A. Kanter: None. W. Dixon: Consultancies; Google (unrelated to this work). G. Nenadic: None.

Evaluation Protocol for Analogue Intelligent Medical Radars: Towards a Systematic Approach Based on Theory and a State of the Art

We propose the basis for a systematised approach to the performance evaluation of analogue intelligent medical radars. In the first part, we review the literature on the evaluation of medical radars and compare the provided experimental elements with models from radar theory in order to identify the key physical parameters that will be useful to develop a comprehensive protocol. In the second part, we present our experimental equipment, protocol and metrics to carry out such an evaluation.

Design and Implementation of Intelligent Medical Mobile Collaborative Office Approval System

Medical affairs approval system is a hot issue in the field of smart medicine. The existing medical affairs approval process is unscientific and opaque, and the approval process is slow, resulting in untimely supply of medical resources. To address this issue, a design and implementation method of a smart medical mobile collaborative office approval system is proposed. Based on the Internet big data information collaboration method, it provides heterogeneous fusion and integration of smart medical mobile collaborative office approval data, strengthens the global efficiency of information data analysis and decision-making, and considers improving data security and privacy protection. The system provides multi-level and multi-type user permissions. The test results show that the proposed method improves the efficiency of medical emergency approval, reduces the approval process, and increases the transparency of the approval process.

Identification of Known Use-Related Problems in Artificial Intelligence Medical Imaging Devices

As Artificial Intelligence (AI) advances, it is included into more and more medical device systems to carry out these systems’ intended functions. Given AI’s ability to learn from real-world use and its capability to continuously improve performance, manufacturers of medical devices are utilizing AI to innovate their products to better assist health care providers. However, like application of other types of medical devices, the application of AI in medical care might pose different types of potential risks to the patients, the users themselves, and to the use environment. For manufactures to successfully ensure the safety of AI medical devices, it is crucial to identify known problems by investigating use-related, user interface, and user interaction in-cidents that have occurred in comparable medical devices. The objective of this study is to identify potential use-related problems of DeepView® Wound Imaging System that assesses the healing potential of thermal burn wounds by analyzing multispectral images with an ML algorithm. We use a variety of sources of information on reports and recalls of medical devices that are associated with deaths, serious injuries and mal-functions. After examining relevant reports and recalls, 19 use-related problems were identified. An in-depth analysis was then conducted, considering each identified use-related problem and determining how it relates to the specific features and functionality of DeepView® Wound Imaging System as well as identifying patterns and commonalities among them. The information gained from this analysis can be beneficial in enhancing the safety of AI medical devices by providing a deeper understanding of the reasons for failure, to avoid similar issues in the future, and ultimately to improve patient safety and public health.

Refined Answer Selection Method with Attentive Bidirectional Long Short-Term Memory Network and Self-Attention Mechanism for Intelligent Medical Service Robot

Answer selection, as a crucial method for intelligent medical service robots, has become more and more important in natural language processing (NLP). However, there are still some critical issues in the answer selection model. On the one hand, the model lacks semantic understanding of long questions because of noise information in a question–answer (QA) pair. On the other hand, some researchers combine two or more neural network models to improve the quality of answer selection. However, these models focus on the similarity between questions and answers without considering background information. To this end, this paper proposes a novel refined answer selection method, which uses an attentive bidirectional long short-term memory (Bi-LSTM) network and a self-attention mechanism to solve these issues. First of all, this paper constructs the required knowledge-based text as background information and converts the questions and answers from words to vectors, respectively. Furthermore, the self-attention mechanism is adopted to extract the global features from the vectors. Finally, an attentive Bi-LSTM network is designed to address long-distance dependent learning problems and calculate the similarity between the question and answer with consideration of the background knowledge information. To verify the effectiveness of the proposed method, this paper constructs a knowledge-based QA dataset including multiple medical QA pairs and conducts a series of experiments on it. The experimental results reveal that the proposed approach could achieve impressive performance on the answer selection task and reach an accuracy of 71.4%, MAP of 68.8%, and decrease the BLUE indicator to 3.10.

Intelligent Medical App Design for Parkinson's Disease Based on Augmented Reality Technique

Based on the theory of augmented reality technology, this article proposes the design method of follow-up practice for Parkinson's disease patients' daily management difficulties and inconvenient exercise medical treatment, and through the design practice, the intelligent medical app for Parkinson's disease is created. The article mixes AR (augmented reality) technology to provide Parkinson's disease sufferers with a clearer, more scientific, and more entertaining medical training experience. By merging multiple medical resources and augmented reality teaching methods, it eliminates the pain spots in sports medical and daily management procedures through intelligent approaches, thereby motivating and directing patients to confront life optimistically. Through expert and user validation, it was demonstrated that this product can improve the experience of patients in sports medical care and effectively enhance the emotional stability of patients, thereby providing design ideas for the future development of daily management methods for elderly people with limited mobility.

Robots and Intelligent Medical Devices in the Intensive Care Unit: Vision, State of the Art, and Economic Analysis

Robotics is widely seen as a key enabling technology for the society of tomorrow. We examine the role of robotics and intelligent medical devices in Intensive Care Medicine. Demographics predict that more elderly patients will need to be treated with fewer healthcare personnel, calling for innovations in ICU management. Robotics is a key enabling technology for this century and may help smoothing the foreseeable workload/manpower disparity in medicine. While robotics cannot replace the crucial human interactions in healthcare, it’s important to identify those areas where it can contribute in the near and foreseeable future. Studying the application of robotics in the ICU in a manner beneficial for patients and accepted by ICU team is therefore desirable. Financial sustainability is essential for the introduction of new technologies to the ICU. This study assesses the state-of-the-art of robotics in intensive care medicine and identifies opportunities for progress, using an observational approach in a teaching hospital ICU in combination with an indepth review of the literature and a survey of the market. Tasks potentially amenable to robotics are identified, their acceptability to patients and caregivers are examined and their quantitative contribution to future management of an intensive care unit is assessed.

Promoting Health Equity through Improved Regulation of Artificial Intelligence Medical Devices

Existing health disparities in the United States are partially driven by the way healthcare is delivered. There is interest in using Artificial Intelligence (AI)-driven software as medical devices (SaMD) to aid in healthcare delivery and reduce health disparities. However, AI-driven tools have the potential to codify bias in healthcare settings. Some AI-driven SaMDs have displayed substandard performance among racial and ethnic minorities. Auditing these tools for biased output can help produce more equitable outcomes across populations. However, there are currently no explicit Food and Drug Administration (FDA) regulations that examine bias in AI software in healthcare. Therefore, we propose the FDA develop a distinct regulatory process for AI-driven SaMDs that includes assessing equitable output across populations and avoiding potential health disparity exacerbation. This change could help prevent AI-driven health disparities nationwide.

Smart Skin‐Adhesive Patches: From Design to Biomedical Applications

AbstractWith the advancement of medical and digital technologies, smart skin adhesive patches have emerged as a key player for complex medical purposes. In particular, skin adhesive patches with integrated electronics have created an excellent platform for monitoring health conditions and intelligent medication. However, the efficient design of the adhesive patches is still challenging as it requires a strong combination of network structure, adhesion, physical properties, and biocompatibility. To design an assimilated device, one must have a deep knowledge of various skin adhesive patches. This article provides a comprehensive review of the recent advances in skin‐adhesive patches, including hydrogel‐based adhesive patches, transdermal patches, and electronic skin (E‐skin) patches, for various biomedical applications such as wound healing, drug delivery, biosensing, and health monitoring. Furthermore, the key challenges, implementable strategies, and future designs that can potentially provide researchers in designing innovative multipurpose smart skin patches are discussed. These advanced approaches are promising for managing the health and fitness of patients who require regular medical care.

Statistical Considerations and Challenges for Pivotal Clinical Studies of Artificial Intelligence Medical Tests for Widespread Use: Opportunities for Inter-Disciplinary Collaboration

The application of Artificial Intelligence to medical testing has received much attention in recent years, as evidenced by the flurry of published studies describing Artificial Intelligence software developed to solve problems in medical testing. While this recent activity is exciting, developed Artificial Intelligence medical tests ultimately can only be considered as candidates for widespread use if these tests demonstrate good performance in pivotal clinical studies. What are pivotal clinical studies for Artificial Intelligence medical tests aimed for widespread use? What are some of the major considerations and challenges for assessing performance of these tests in this context? What are some of the outstanding areas where statisticians, in collaboration with professionals outside the statistical community, could help in this endeavor? This article addresses these questions. This article is meant to appeal to a broad audience with varying levels of statistical and medical testing knowledge so that inter-disciplinary collaboration could be enhanced.

Development of Intelligent Medicine and Construction of Intelligent Medical Engineering Specialty

Based on clinical big data and artificial intelligence and web technologies, Intelligent Medicine takes form as a medical service model centered on patient data. This paper introduced the development of intelligent medical treatment, analyzed the current application of big data analysis in intelligent medical practice, and looked forward to the challenge and future trend of intelligent medicine. Focusing on the training mode of intelligent medical engineering specialty, this paper explored architecture of intelligent medical engineering education platform, made the construction of a scientific and reasonable interdisciplinary curriculum system in the process of interdisciplinary postgraduate training according to the aim and characteristics of interdisciplinary, hoping to realize a unique new interdisciplinary education mode.