Cough is a sign of numerous respiratory infections and is often quantified by cough frequency. Although the need for accurate and objective cough detection in ambulatory settings is widely acknowledged in the medical literature, little research has been done on automating the classification using a single microphone in an open, real-world setting. This study examined the feasibility of applying artificial intelligence to recognize and categorize coughs by patients wearing or not wearing masks in a waiting room of a primary care institution with a single microphone and varying degrees of background noise. A sequential convolutional neural network (CNN) consisting of two 2D convolutional layers with 3x3 kernels and four filters were used with varying parameters. The best performing classification model used three layers with 64, 32 and 16 filters. It achieved an overall accuracy of 98.5% with a sensitivity of 98.2% and specificity of 98.8%. The findings imply that detection using artificial intelligence and a single microphone in a waiting room might be feasible to use in certain scenarios.

Along with the diagnostic process for sleep disorders, a diversity of clinical assessments are required to diagnose the concrete type of sleep disorder. For an accurate diagnosis, a variety of medical examinations are conducted, whose results are documented in clinical records. The textual records form the basis for disease categorization and cohort creation using the International Classification for Sleep Disorder (ICSD-III). However, textual records generated through patient-physician interaction may contain different types of biases caused by the heterogeneous nature of medical specialties and the used vocabulary in the documentation. In this work, we will analyze different types of semantic biases and driftings in the context of sleep disorder diagnosis using the Bern Sleep Database. The database contains documents describing the clinical history, referring reasons and results from different assessments (polysomnography (PSG), multiple sleep latency test (MSLT), maintenance of wakefulness test (MWT), actigraphy (Wrist, PLMS)) from more than 6000 patients. An enhanced text-based ICSD-III classification (Multichannel CNN and Hierachical attentive networks) using a standardized concept representation will be proposed. We will analyze the influence of semantic bias on the accuracy of this automatic classification.

A clinical sentiment is a judgment, thought or attitude promoted by an observation with respect to the health of an individual. Sentiment analysis has drawn attention in the healthcare domain for secondary use of data from clinical narratives, with a variety of applications including predicting the likelihood of emerging mental illnesses or clinical outcomes. The current state of research has not yet been summarized. This study presents results from a scoping review aiming at providing an overview of sentiment analysis of clinical narratives in order to summarize existing research and identify open research gaps. The scoping review was carried out in line with the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guideline. Studies were identified by searching 4 electronic databases (e.g., PubMed, IEEE Xplore) in addition to conducting backward and forward reference list checking of the included studies. We extracted information on use cases, methods and tools applied, used datasets and performance of the sentiment analysis approach. Of 1,200 citations retrieved, 29 unique studies were included in the review covering a period of 8 years. Most studies apply general domain tools (e.g. TextBlob) and sentiment lexicons (e.g. SentiWordNet) for realizing use cases such as prediction of clinical outcomes; others proposed new domain-specific sentiment analysis approaches based on machine learning. Accuracy values between 71.5-88.2% are reported. Data used for evaluation and test are often retrieved from MIMIC databases or i2b2 challenges. Latest developments related to artificial neural networks are not yet fully considered in this domain. We conclude that future research should focus on developing a gold standard sentiment lexicon, adapted to the specific characteristics of clinical narratives. Efforts have to be made to either augment existing or create new high-quality labeled data sets of clinical narratives. Last, the suitability of state-of-the-art machine learning methods for natural language processing and in particular transformer-based models should be investigated for their application for sentiment analysis of clinical narratives.

INTRODUCTION: Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. MATERIALS AND METHODS: We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. RESULTS: Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. DISCUSSION: Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. CONCLUSIONS: Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques.

The postoperative health status of an obesity patient indicates the outcome of the surgical treatment. By each postoperative revisit, physicians need to go through the previous patient records to recall the patient status and to evaluate the postoperative risk of readmission. In order to support in this process, we develop a method to extract indicators and to analyse weight changes, so that potential complications and risks of clinical readmission can be recognized timely. In this paper, we will compare two approaches that are based on traditional machine learning and neural networks. Relevant aspects referring to a health status change or treatment-relevant aspects are extracted from the outpatient medical records as they are generated for each postoperative revisit. The performance of traditional machine learning on the task of obesity-related entity extraction is compared with one variation of attentive recurrent neural networks. The ensemble classifier of binary attentive bi-LSTM with the data balancing using conditional generative adversarial networks (CGAN) has achieved F1 measure of 86.5% on the task of classification of eight classes of obesity-related entities. We conclude that for processing a small data set using neural networks, a data balancing method should firstly be applied to achieve an extended corpus and a general representation, which can apparently increase the differentiability of the input data. A fine-tuning in the networks can provide further enhancement of the performance.

The amount of social media data dealing with medical and health issues increased significantly in the last couple of years. Medical social media data now provides a new source of information within information gaining contexts. Facts, experiences, opinions or information on behavior can be found in the Medicine 2.0 or Health 2.0 and could support a broad range of applications. This workshop is devoted to the technologies for dealing with social- and multi media for medical information gathering and exchange. This specific data and the processes of information gathering poses many challenges given the increasing content on the Web and the trade off of filtering noise at the cost of losing information which is potentially relevant.

Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose a game-changing approach where public health events are detected in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 66% and a recall of 81% when evaluated using manually annotated, real-world data. This shows promising results for the use of such techniques in this new problem setting.

Our study addresses the problem of large-scale contradic-tion detection and management, from data extracted from the Web. We describe the first systematic solution to the problem, based on a novel statistical measure for contra-dictions, which exploits first- and second-order moments of sentiments. Our approach enables the interactive analysis and online identification of contradictions under multiple levels of time granularity. The proposed algorithm can be used to analyze and track opinion evolution over time and to identify interesting trends and patterns. It uses an incre-mentally updatable data structure to achieve computational efficiency and scalability. Experiments with real datasets show promising time performance and accuracy.