TANGO: Performance and Fault Management in Cellular Networks through Cooperation between Devices and Edge Computing Nodes

Cellular networks have become an essential part of our lives. With increasing demands on its available bandwidth, we are seeing failures and performance degradations for data and voice traffic on the rise. In this paper, we propose the view that fog computing, integrated in the edge components of cellular networks, can partially alleviate this situation. In our vision, some data gathering and data analytics capability will be developed at the edge of the cellular network and client devices and the network using this edge capability will coordinate to reduce failures and performance degradations. We also envisage proactive management of disruptions including prediction of impending events of interest (such as, congestion or call drop) and deployment of appropriate mitigation actions. We show that a simple streaming media pre-caching service built using such device-fog cooperation significantly expands the number of streaming video users that can be supported in a nominal cellular network of today

Improving Failure Management Through Cooperation Between Mobile Devices and Cellular Network

Mobile devices have become an integral part of our lives. As people rely more on them, the traffic demand has increased rapidly, outpacing the growth of the capacity of cellular networks. As a result, connectivity problems such as congestions are becoming more common. In a similar manner, users\u27 increasing demand for storage space on mobile devices leads to major inconvenience when available space runs out. In this dissertation, we present a novel method of mitigating or preventing the negative effects of such connectivity issues in multimedia streaming applications, as well as a technique for reducing storage requirements of mobile applications. Mobile streaming applications usually limit the download rate in some way, in order to conserve user\u27s bandwidth. However, when connectivity is degraded, playback can easily be disrupted. To prevent this, we propose a novel framework called TANGO, where real-time network conditions combined with the user\u27s location prediction are used to give the application an early notification of network degradation. This allows the application to change its buffering strategy proactively in order to prevent playback disruption. We next focus on reducing storage requirements of mobile applications, especially games, through predictive streaming. The size of mobile applications and the users\u27 demand for storage have been outpacing the growth of storage capacity of mobile devices. This leads to the users frequently having to uninstall some applications or remove personal files in order to free up storage for new applications. We propose a technique called AppStreamer, which predicts applications\u27 file accesses and use this information to cache the applications\u27 resource files in a smart way. We implement AppStreamer on Android and evaluate the effects it has on user experience using user studies. The results indicate that most people notice no degradation of user experience, while the storage requirements of the application can be reduced by more than 85%

Development and external validation of automated ICD-10 coding from discharge summaries using deep learning approaches

Objectives: To develop an automated international classification of diseases (ICD) coding tool using natural language processing (NLP) and discharge summary texts from Thailand. Materials and methods: The development phase included 15,329 discharge summaries from Ramathibodi Hospital from January 2015 to December 2020. The external validation phase included Medical Information Mart for Intensive Care III (MIMIC-III) data. Three algorithms were developed: naïve Bayes with term frequency-inverse document frequency (NB-TF-IDF), convolutional neural network with neural word embedding (CNN-NWE), and CNN with PubMedBERT (CNN-PubMedBERT). In addition, two state-of-the-art models were also considered; convolutional attention for multi-label classification (CAML) and pretrained language models for automatic ICD coding (PLM-ICD). Results: The CNN-PubMedBERT model provided average micro- and macro-area under precision-recall curve (AUPRC) of 0.6605 and 0.5538, which outperformed CNN-NWE (0.6528 and 0.5564), NB-TF-IDF (0.4441 and 0.3562), and CAML (0.6257 and 0.4964), with corresponding differences of (0.0077 and −0.0026), (0.2164 and 0.1976), and (0.0348 and 0.0574), respectively. However, CNN-PubMedBERT performed less well relative to PLM-ICD, with corresponding AUPRCs of 0.7202 and 0.5865. The CNN-PubMedBERT model was externally validated using two subsets of MIMIC-III; MIMIC-ICD-10, and MIMIC-ICD-9 datasets, which contained 40,923 and 31,196 discharge summaries. The average micro-AUPRCs were 0.3745, 0.6878, and 0.6699, corresponding to directly predictive MIMIC-ICD-10, MIMIC-ICD-10 fine-tuning, and MIMIC-ICD-9 fine-tuning approaches; the average macro-AUPRCs for the corresponding models were 0.2819, 0.4219 and 0.5377, respectively. Discussion: CNN-PubMedBERT performed second-best to PLM-ICD, with considerable variation observed between average micro- and macro-AUPRC, especially for external validation, generally indicating good overall prediction but limited predictive value for small sample sizes. External validation in a US cohort demonstrated a higher level of model prediction performance. Conclusion: Both PLM-ICD and CNN-PubMedBERT models may provide useful tools for automated ICD-10 coding. Nevertheless, further evaluation and validation within Thai and Asian healthcare systems may prove more informative for clinical application

Systematic review of natural language processing for recurrent cancer detection from electronic medical records

This systematic review was conducted to explore natural language processing (NLP) focusing on text representation techniques and algorithms used previously to identify recurrent cancer diagnoses from electronic medical records (EMR), and an assessment of their detection performance. Relevant studies were identified from PubMed, Scopus, ACM Digital Library, and IEEE databases since inception to August 18, 2022. Data, including text representation methods, model algorithms and performance, and type of clinical notes, were extracted from individual studies by two independent reviewers. Study risk of bias was assessed using the prediction model risk of bias assessment tool. Of the 412 studies identified, 17 were eligible for inclusion, with 15 representing models that were not externally validated. Three text representations were used: statistical, context-free, and contextual representations (bidirectional encoder representations from transformers (BERT) and its variants), from 12, 6, and 3 studies, respectively. The corresponding median harmonic precision and recall means (F1 scores) for these representations were 0.43, 0.87, and 0.72, respectively. The algorithms applied included rule-based, machine learning, and deep learning approaches with median F1 scores of 0.71, 0.43, and 0.76, respectively. In conclusion, this systematic review suggests that deep learning models that use PubMedBERT as a text representation perform best. These findings are clinically informative for the selection of appropriate approaches for the detection of recurrent cancer from electronic medical records

Informatics education in low-resource settings

Developing countries have the burden of acute and chronic diseases with the greatest health disparities. There is also a shortfall of more than four million healthcare workers worldwide, and the proportion is higher in less economically viable countries where the lack of proper trained healthcare workers is also compromised by the migration and departure of skilled personnel together with a frail infrastructure and a shortage of resources that cannot provide a proper scenario for an adequate healthcare system that will fulfill the population needs. The need for both technology infrastructure and individuals who have the skills to develop these systems is great, but so are the challenges in developing the needed workforce who are well-trained in informatics. This chapter describes the current informatics education efforts in three regions: Latin America, Sub-Saharan Africa and the Asia-Pacific region. The description of specific healthcare informatics education programs, the educational methods used and the challenges encountered are explored