Computer methods and programs in biomedicine update最新文献

Introduction

This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans.

Methods

A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies.

Results and discussion

Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training.

Conclusion

AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.

Background

COVID-19 is the first pandemic in history in which technology and social media are being used for people to be informed and be safe. Thus, digital health literacy skills affect the way people will protect and promote their health.

Methods

A cross-sectional web-based study was conducted with a convenience sample among university students (N=604) from one of the Universities located in Attica (Greece) during May - June 2020. The COVID-HL university students survey questionnaire was used for collecting the data.

Results

In regards to information search, 28 % of the university students indicated that they found it very difficult/difficult to find the exact information they were looking for and 20.4 % to make a choice from all the information they found. Additionally, 45.1 % of the participants found it very difficult/difficult to decide whether the information retrieved via online search is reliable or not.

Conclusion

The results indicate a need for the promotion of digital health literacy among university students and therefore, health education interventions need to optimize students’ seeking skills and critical thinking. Health educators should consider the results of this study and involve the university students in any intervention they plan in order to address the students’ specific needs. It is also suggested that these health education interventions should be integrated throughout all academic activities.

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Method:

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Dengue fever is a vital public health concern that affects about 40% of the world’s population. To address the dynamics of dengue disease, a mathematical model was formulated by incorporating three control strategies: vector control, treatment, and mass awareness. A stability analysis of the disease-free equilibrium (DFE) was conducted using the Jacobian matrix. The DFE was found to be locally and globally asymptotically stable when the effective reproductive number was less than one; otherwise, it was unstable. Additionally, an endemic equilibrium point (EEP) was identified. The global stability analysis of the EEP, performed using the Lyapunov method, showed that it is globally asymptotically stable whenever $R_e>1$; otherwise, it is unstable. Bifurcation analysis revealed that the model system exhibits a forward bifurcation. Furthermore, sensitivity analysis of the effective reproduction number revealed that the most sensitive parameters are the biting rate ($b$) and insecticide efficacy ($\delta$). Therefore, the results suggest that, in order to reduce new dengue cases, intervention strategies that decrease the biting rate, such as mosquito repellents and the use of insecticides to kill mosquitoes, should be implemented. Moreover, simulations were conducted for the extended model with vector control, treatment, and mass awareness. The results showed that the combination of vector control, treatment, and mass awareness has a more positive impact on the control of dengue fever than any single or paired intervention. Thus, for effective control of dengue fever, the three control measures should be implemented simultaneously, especially in endemic areas.

Background

Academic writing is an essential component of research, characterized by structured expression of ideas, data-driven arguments, and logical reasoning. However, it poses challenges such as handling vast amounts of information and complex ideas. The integration of Artificial Intelligence (AI) into academic writing has become increasingly important, offering solutions to these challenges. This review aims to explore specific domains where AI significantly supports academic writing.

Methods

A systematic review of literature from databases like PubMed, Embase, and Google Scholar, published since 2019, was conducted. Studies were included based on relevance to AI's application in academic writing and research, focusing on writing assistance, grammar improvement, structure optimization, and other related aspects.

Results

The search identified 24 studies through which six core domains were identified where AI helps academic writing and research: 1) facilitating idea generation and research design, 2) improving content and structuring, 3) supporting literature review and synthesis, 4) enhancing data management and analysis, 5) supporting editing, review, and publishing, and 6) assisting in communication, outreach, and ethical compliance. ChatGPT has shown substantial potential in these areas, though challenges like maintaining academic integrity and balancing AI use with human insight remain.

Conclusion and recommendations

AI significantly revolutionises academic writing and research across various domains. Recommendations include broader integration of AI tools in research workflows, emphasizing ethical and transparent use, providing adequate training for researchers, and maintaining a balance between AI utility and human insight. Ongoing research and development are essential to address emerging challenges and ethical considerations in AI's application in academia.

Background

The global increase in an aging population has led to more common age-related health challenges, particularly multimorbidity and frailty, but there is a significant gap.

Methods

This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (1999–2018). The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted multivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested cross-validation and tested with various algorithms including decision tree, Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AU-PRC) to evaluate six algorithms, select the optimal model, and test the discrimination and consistency of the optimal model.

Results

The study included 46,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impacting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary Heart Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cross-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration.

Conclusions

We found 49 years maintain the balance of physiological reserve and external aggression. In addition, chronic diseases are trigger factor of frailty, while acute diseases are contributing factor that exacerbates the body's rapid decline. Last, the XGBoost frailty prediction model, with its simplicity, high performance and high clinical value holds potential for clinical application.

Public health surveillance (PHS) systems are an important tool to map the distribution and burden of disease as well as enable efficient distribution of resources to fight a disease. The surveillance systems are used to detect, report, track a disease as well as assess the response to the disease and people's attitudes. PHS systems are changing with the rapid change in technology and are becoming more real-time responsive with availability of new type of data such as online content and social media data. This review presents the basics of surveillance systems and develop from it to show the evolution of surveillance systems. The different forms of data available, surveillance methods and surveillance types are also reviewed such as social media based, web-based and clinical data based PHS maps. This review provide comprehensive details of the surveillance systems in terms of data types used, source of data and purpose of the surveillance system.

Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention Network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals.

Background

Hospitals are transitioning away from traditional pagers to secure text messaging (STM) applications. STM is perceived to improve efficiency and accessibility. There is limited research on user's impressions of how STM impacts patient safety, provider wellness, and quality of patient care.

Objectives

To understand the use and perceptions of a clinical STM by pediatric residents at a free-standing quaternary care children's hospital.

Methods

A survey was conducted of pediatric residents regarding their experience with Diagnotes®. Demographic data were obtained along with use patterns, ability to perform tasks, and perceptions of intended purpose. Further questions evaluated agreement with communication strategies and satisfaction with features. Three open-ended questions asked about experience where STM impacted (1) patient care coordination and (2) patient safety. A final question asked for any additional STM feedback.

Results

Of 169 surveys, there were 112 respondents (66.3 % response rate). Respondents unanimously endorsed daily STM use on their personal mobile devices with good knowledge of basic features. Respondents were overall satisfied with Diagnotes® (73.9 %) including the ability to communicate efficiently (84.8 %) and effectively (79.5 %). Yet only 32.1 % were satisfied with Diagnotes® training. Only 59.5 % believed Diagnotes® was appropriate for urgent patient care needs and only 43.2 % believed its purpose was to inform the team of patient emergencies. Key qualitative themes included improved coordination of patient care tasks through STM, but there were concerns raised around sending and receiving messages, the additional cognitive burden placed by STM, and differences in culture of use that created conflict.

Conclusions

Diagnotes® is viewed positively including use for effective coordination of patient care and familiarity of functions of Diagnotes®. Barriers included unclear interprofessional expectations for use. Future research should incorporate a broad range of healthcare professionals' perceptions and co-creation of STM best practice guidelines for use, including around urgent or emergent patient care issues.

Background

Loneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon.

Objectives

This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed.

Methods

This study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations.

Results

The analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue.

Conclusion

This paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.