Computer methods and programs in biomedicine update最新文献

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

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.

Imbalanced datasets can impair the learning performance of many Machine Learning techniques. Nevertheless, many real-world datasets, especially in the healthcare field, are inherently imbalanced. For instance, in the medical domain, the classes representing a specific disease are typically the minority of the total cases. This challenge justifies the substantial research effort spent in the past decades to tackle data imbalance at the data and algorithm levels. In this paper, we describe the strategies we used to deal with an imbalanced classification task on data extracted from a database generated from the Electronic Health Records of the Mental Health Service of the Ferrara Province, Italy. In particular, we applied balancing techniques to the original data, such as random undersampling and oversampling, and Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC). In order to assess the effectiveness of the balancing techniques on the classification task at hand, we applied different Machine Learning algorithms. We employed cost-sensitive learning as well and compared its results with those of the balancing methods. Furthermore, a feature selection analysis was conducted to investigate the relevance of each feature. Results show that balancing can help find the best setting to accomplish classification tasks. Since real-world imbalanced datasets are increasingly becoming the core of scientific research, further studies are needed to improve already existing techniques.

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.

The present global health threat is the novel coronavirus disease (COVID-19), caused by a new strain of the SARS-CoV-2 coronavirus. In this study, have employed optimal control theory, aided by Pontryagin’s Maximum Principle, to explore optimal control measures. Specifically, we have investigated time-dependent intervention strategies, including the proper use of personal protective measures and vaccination. Bifurcation analysis was conducted and results shows that the model system exhibit a forward bifurcation. The optimal control system have been numerically simulated using the fourth-order Runge–Kutta methods. The results show that the implementation of the combination of the two interventions was more significant and effective in minimizing the spread of the COVID-19 than the implementation of a single control measure. These findings underscore the significance of multifaceted intervention approaches over singular control measures. Notably, the combined implementation of interventions emerges as markedly more effective in containing COVID-19 transmission. Moreover, our study identifies personal protective measures as a particularly cost-effective intervention, offering substantial relief from the burden of the pandemic within the population. We anticipate that our research will inform evidence-based approaches to pandemic control and aid in the ongoing global efforts to safeguard public health.

Lung cancer is the second common cancer with the highest death rate in the world. Cancer diagnosis in the early stages is a critical factor for increasing the treatment speed. This paper proposes a new machine learning method based on a fuzzy approach to detect benign and malignant lung nodules to early-diagnose lung cancer by investigating the computed tomography (CT) images. First, the lung nodule images are pre-processed via the Gabor wavelet transform. Then, some of the texture features are extracted from the transformed domain based on the statistical characteristics and histogram of the local patterns of images. Finally, based on the fuzzy information granulation (FIG) method, which is widely recognized as being able to distinguish between similar textures, a FIG-based classifier is introduced to classify the benign and malignant lung nodules. The clinical data set used for this research are a combination of 150 CT scans of LIDC and SPIE-APPM data sets. Also the LIDC data set is analyzed alone. The results show that the proposed method can be an innovative alternative to classify the benign and malignant nodules in the CT images.

Background and objective:

Globally, irreversible electroporation (IRE) emerges as a promising technique for tissue ablation as it overcomes the limitations of the benchmark techniques. However, achieving the desired and safe ablation volume of tissue pivots on multiple factors, such as pulse profile, shape, and number of electrodes, besides the IRE treatment parameters, like pulse type, field strength, number of pulses, pulse length, and frequency. This work aims to develop a $\mathrm{3D}$ computation platform that predicts the ablation volume using the IRE procedure and provides insights such as electric field, temperature and its corresponding cell survival regions. Thereby, such a platform aids in selecting optimized treatment parameters to avoid thermal damage. In addition, the developed IRE model estimates the relationship between the pulse protocol and different electrode geometries, number of electrodes, and electrode configurations.

Methods:

The computational model for IRE is developed with Laplace’s equation and Penn’s bio-heat equation for the electric potential and temperature profiles, respectively, and the Finite Difference method is considered for the numerical solution. The statistical Fermi equation-based Peleg model has been adapted to estimate the ablation volume as a function of the magnitude of the electric field and other electric field parameters.

Results:

The tissue ablation platform allows computation and visualization of ablation volume estimation using the IRE technique with a pair of plate-type and multiple pairs of needle-type electrodes. IRE treatment with different combinations of electric pulse parameters, i.e., pulse length, voltage, and number of pulses, causes different levels of temperature rise. By adapting our platform, one can avoid thermal damage in the IRE treatment with the right combination of pulse parameters. For instance, one can apply a maximum of 10 pulses restricting temperature within $50°C$ in the IRE treatment of cervical tissue with a couple of pairs of needle-type electrodes and $100\mu s$ electric pulses of $3000V$.

Conclusion:

The proposed IRE model aids in treatment planning for tissue ablation with $\mathrm{3D}$ visual outputs through the platform’s user interface for better clinical insights, including interpretability, data resolution, and computational cost.

Aims

Various cardiovascular risk prediction models have been developed for patients with type 2 diabetes mellitus. Yet few models have been validated externally. We perform a comprehensive validation of existing risk models on a heterogeneous population of patients with type 2 diabetes using secondary analysis of electronic health record data.

Methods

Electronic health records of 47,988 patients with type 2 diabetes between 2013 and 2017 were used to validate 16 cardiovascular risk models, including 5 that had not been compared previously, to estimate the 1-year risk of various cardiovascular outcomes. Discrimination and calibration were assessed by the c-statistic and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. Each model was also evaluated based on the missing measurement rate. Sub-analysis was performed to determine the impact of race on discrimination performance.

Results

There was limited discrimination (c-statistics ranged from 0.51 to 0.67) across the cardiovascular risk models. Discrimination generally improved when the model was tailored towards the individual outcome. After recalibration of the models, the Hosmer-Lemeshow statistic yielded p-values above 0.05. However, several of the models with the best discrimination relied on measurements that were often imputed (up to 39% missing).

Conclusion

No single prediction model achieved the best performance on a full range of cardiovascular endpoints. Moreover, several of the highest-scoring models relied on variables with high missingness frequencies such as HbA1c and cholesterol that necessitated data imputation and may not be as useful in practice. An open-source version of our developed Python package, cvdm, is available for comparisons using other data sources.