Search

Abstract Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that incorporates extreme multi-label classification tasks to enhance International Classification of Diseases (ICD) coding. The model utilizes deformable convolutional neural networks to fuse representations from hidden layer outputs of pre-trained language models and external medical knowledge embeddings fused using a multimodal approach to provide rich semantic encodings for each code. A probabilistic label tree is constructed based on the hierarchical structure existing in ICD labels to incorporate ontological relationships between ICD codes and enable structured output prediction. Experiments on medical code prediction on the MIMIC-III database demonstrate competitive performance, highlighting the benefits of this technique for robust clinical code assignment.

Data Documentation Initiative-Lifecycle (DDI-L) introduced a robust metadata model to support the capture of questionnaire content and flow, and encouraged through support for versioning and provenancing, objects such as BasedOn for the reuse of existing question items. However, the dearth of questionnaire banks including both question text and response domains has meant that an ecosystem to support the development of DDI ready Computer Assisted Interviewing (CAI) tools has been limited. Archives hold the information in PDFs associated with surveys but extracting that in an efficient manner into DDI-Lifecycle is a significant challenge. While CLOSER Discovery has been championing the provision of high-quality questionnaire metadata in DDI-Lifecycle, this has primarily been done manually. More automated methods need to be explored to ensure scalable metadata annotation and uplift. This paper presents initial results in engineering a machine learning (ML) pipeline to automate the extraction of questions from survey questionnaires as PDFs. Using CLOSER Discovery as a ‘training and test dataset’, a number of machine learning approaches have been explored to classify parsed text from questionnaires to be output as valid DDI items for inclusion in a DDI-L compliant repository. The developed ML pipeline adopts a continuous build and integrate approach, with processes in place to keep track of various combinations of the structured DDI-L input metadata, ML models and model parameters against the defined evaluation metrics, thus enabling reproducibility and comparative analysis of the experiments. Tangible outputs include a map of the various metadata and model parameters with the corresponding evaluation metrics’ values, which enable model tuning as well as transparent management of data and experiments.

Advances made in the Internet of Things (IoT) and other disruptive technological trends, including big data analytics and edge computing methods, have contributed enabling solutions to the numerous challenges affecting modern communities [...]

Background: The incidence of thyroid cancer has been increasing worldwide. Thyroid imaging reporting and data system (TIRADS) has been proposed for risk stratification of thyroid nodules to improve categorical management. Fine needle aspiration cytology based on Bethesda system for reporting of thyroid cytopathology (BSRTC) plays a fundamental role in the evaluation of thyroid nodule microscopically. Both the systems, the TIRADS and the latest revised BSRTC 2017, are widely recommended and practiced all over the world, but the correlation between the two systems has not been established. Aims and Objectives: This study was conducted to assess the risk of malignancy (ROM) in the intermediate Bethesda categories of thyroid lesions and their correlation with the corresponding TIRADS categories. Materials and Method: It was a prospective cross-sectional study over 1 year including 69 patients aged 18 years or older having solitary thyroid nodules. All cases were triaged using both TIRADS and BSRTC 2017 and the diagnostic performances were compared with subsequent paraffin sections to evaluate ROM. Correlation between TIRADS and BSRTC systems was expressed as kappa value. Result: Good concordance was observed between TIRADS and BSRTC systems in the evaluation of benign thyroid nodule lesions (category 2-II). There was discordance in follicular lesions (category 4-IV). The kappa value generated (0.411) revealed moderate agreement between the two risk stratification systems. Conclusion: Careful application of both grading systems is essential for the proper segregation of thyroid nodules to facilitate effective clinical and surgical management. However, universally acceptable protocols need to be developed to avoid the heterogeneous approach.

The economic and social impact of poor air quality in towns and cities is increasingly being recognized, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the air quality index, using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel non-linear autoregressive neural network with exogenous input model, especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning-based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

Mobile sensing techniques have been increasingly deployed in many Internet of Things-based applications because of their cost efficiency, wide coverage, and flexibility. However, these techniques are unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy. As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data, which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing estimation models and techniques developed for static sensing do not work well in the mobile sensing scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors. More importantly, the proposed model is tolerant to datasets with extremely high-missing data rates. Training with the proposed model is also efficient, which makes it suitable for deployment on computationally constrained devices and platforms that need to process massive amounts of data in real time.

In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods—FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products’ lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence.

Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.