Sentiment analysis of clinical narratives: A scoping review

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

User-Centered Design of a Speech-Based Application to Support Caregivers

The shortage of skilled nursing personnel is – among other reasons – due to the low attractiveness of the profession, comprising high workloads and atypical working hours. Studies show that speech-based documentation systems increase documentation efficiency and satisfaction of physicians. This paper describes the development process of a speech-based application to support nurses, according to the user-centered design approach. User requirements were collected based on interviews (n=6) as well as observations (n=6) in three institutions and were evaluated by means of qualitative content analysis. A prototype of the derived system architecture was implemented. Based on a usability test (n=3), further potentials for improvement were determined. The resulting application enables nurses to dictate personal notes, share them with colleagues and transmit notes to the existing documentation system. We conclude that the user-centered approach ensures the extensive consideration of the nursing staff’s requirements and shall be continued for further development

Designing a Digital Medical Interview Assistant for Radiology

Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient’s medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group

Designing a Digital Medical Interview Assistant for Radiology.

Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient's medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group

Large language model-based information extraction from free-text radiology reports: a scoping review protocol

Introduction Radiological imaging is one of the most frequently performed diagnostic tests worldwide. The free-text contained in radiology reports is currently only rarely used for secondary use purposes, including research and predictive analysis. However, this data might be made available by means of information extraction (IE), based on natural language processing (NLP). Recently, a new approach to NLP, large language models (LLMs), has gained momentum and continues to improve performance of IE-related tasks. The objective of this scoping review is to show the state of research regarding IE from free-text radiology reports based on LLMs, to investigate applied methods and to guide future research by showing open challenges and limitations of current approaches. To our knowledge, no systematic or scoping review of IE from radiology reports based on LLMs has been published. Existing publications are outdated and do not comprise LLM-based methods. Methods and analysis This protocol is designed based on the JBI Manual for Evidence Synthesis, chapter 11.2: ‘Development of a scoping review protocol’. Inclusion criteria and a search strategy comprising four databases (PubMed, IEEE Xplore, Web of Science Core Collection and ACM Digital Library) are defined. Furthermore, we describe the screening process, data charting, analysis and presentation of extracted data. Ethics and dissemination This protocol describes the methodology of a scoping literature review and does not comprise research on or with humans, animals or their data. Therefore, no ethical approval is required. After the publication of this protocol and the conduct of the review, its results are going to be published in an open access journal dedicated to biomedical informatics/digital health

Exploring the Evolution of Social Media in Mental Health Interventions: A Mapping Review

Background: With the rise of social media, social media use for delivering mental health interventions has become increasingly popular. However, there is no comprehensive overview available on how this field developed over time. Objectives: The objective of this paper is to provide an overview over time of the use of social media for delivering mental health interventions. Specifically, we examine which mental health conditions and target groups have been targeted, and which social media channels or tools have been used since this topic first appeared in research. Methods: To provide an overview of the use of social media for mental health interventions, we conducted a search for studies in four databases (PubMed; ACM Digital Library; PsycInfo; and CINAHL) and two trial registries (Clinicaltrials.gov; and Cochranelibrary.com). A sample of representative keywords related to mental health and social media was used for that search. Automatic text analysis methods (e.g., BERTopic analysis, word clouds) were applied to identify topics, and to extract target groups and types of social media. Results: A total of 458 studies were included in this review (n=228 articles, and n=230 registries). Anxiety and depression were the most frequently mentioned conditions in titles of both articles and registries. BERTopic analysis identified depression and anxiety as the main topics, as well as several addictions (including gambling, alcohol, and smoking). Mental health and women's research were highlighted as the main targeted topics of these studies. The most frequently targeted groups were “adults” (39.5%) and “parents” (33.4%). Facebook, WhatsApp, messenger platforms in general, Instagram, and forums were the most frequently mentioned tools in these interventions. Conclusions: We learned that research interest in social media-based interventions in mental health is increasing, particularly in the last two years. A variety of tools have been studied, and trends towards forums and Facebook show that tools allowing for more content are preferred for mental health interventions. Future research should assess which social media tools are best suited in terms of clinical outcomes. Additionally, we conclude that natural language processing tools can help in studying trends in research on a particular topic.publishedVersio

Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis

Recent developments related to tools based on artificial intelligence (AI) have raised interests in many areas, including higher education. While machine translation tools have been available and in use for many years in teaching and learning, generative AI models have sparked concerns within the academic community. The objective of this paper is to identify the strengths, weaknesses, opportunities and threats (SWOT) of using AI-based tools (ABTs) in higher education contexts. We employed a mixed methods approach to achieve our objectives; we conducted a survey and used the results to perform a SWOT analysis. For the survey, we asked lecturers and students to answer 27 questions (Likert scale, free text, etc.) on their experiences and viewpoints related to AI-based tools in higher education. A total of 305 people from different countries and with different backgrounds answered the questionnaire. The results show that a moderate to high future impact of ABTs on teaching, learning and exams is expected by the participants. ABT strengths are seen as the personalization of the learning experience or increased efficiency via automation of repetitive tasks. Several use cases are envisioned but are still not yet used in daily practice. Challenges include skills teaching, data protection and bias. We conclude that research is needed to study the unintended consequences of ABT usage in higher education in particular for developing countermeasures and to demonstrate the benefits of ABT usage in higher education. Furthermore, we suggest defining a competence model specifying the required skills that ensure the responsible and efficient use of ABTs by students and lecturers

What Do Autistic People Discuss on Twitter? An Approach Using BERTopic Modelling

Social media provide easy ways to autistic individuals to communicate and to make their voices heard. The objective of this paper is to identify the main themes that are being discussed by autistic people on Twitter. We collected a sample of tweets containing the hashtag #ActuallyAutistic during the period 10/02/2022 and 14/09/2022. To identify the most discussed topics, BERTopic modelling was applied. We manually grouped the detected topics into 6 major themes using inductive content analysis: 1) General aspects of autism and experiences of autistic individuals; 2) Autism awareness, pride and funding; 3) Interventions, mostly related to Applied Behavior Analysis; 4) Reactions and expressions; 5) Everyday life as an autistic (lifelong condition, work, housing…); and 6) Symbols and characteristics. The majority of tweets were presenting general aspects and experiences as autistic individuals; raising awareness; and about their dissatisfaction with some interventions. The identification of autistic individuals' main discussion themes could help to develop meaningful public health agendas and research involving and addressed to autistic individuals

Assessing and Improving the Usability of the Medical Data Models Portal

Case report forms (CRF) specify data definitions and encodings for data to be collected in clinical trials. To enable exchange of data definitions and in this way to avoid creation of variants of CRF for similar study designs, the Medical Data Model portal (MDM) has been developed since 2011. This work aims at studying the usability of the MDM portal. We identify issues that hamper its adoption by researchers in order to derive measurements for improving it. We selected relevant tools (e.g. Nibbler, Hotjar, SUPR-Q) for usability testing and generated a structured test protocol. More specifically, the portal was assessed by means of a static analysis, user analysis (n=10), a usability test (n=10) and statistical evaluations. Regarding accessibility and technology, the static code analysis resulted in high scores. Presentation of information and functions as well as interaction with the portal still has to be improved: The results show that only limited functions of the webpage are used regularly and some user navigation errors occur due to the portal's design. In total, six major problems were identified which will be addressed in future. A continuous evaluation using the same structured test protocol allows to continuously measure the website quality, to compare it after changes have been implemented and in this way, to realise a continuous improvement. The effort for a repeated evaluation of the same evaluation with 10 persons is estimated with 10 hours