15,084 research outputs found
A sentence classification framework to identify geometric errors in radiation therapy from relevant literature
The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors
Behavior change interventions: the potential of ontologies for advancing science and practice
A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science
Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach
According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions.
This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating.
In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models
An overview of the features of chatbots in mental health: A scoping review
Background:
Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual languages. Chatbots have the potential to be useful tools for individuals with mental disorders, especially those who are reluctant to seek mental health advice due to stigmatization. While numerous studies have been conducted about using chatbots for mental health, there is a need to systematically bring this evidence together in order to inform mental health providers and potential users about the main features of chatbots and their potential uses, and to inform future research about the main gaps of the previous literature.
Objective:
We aimed to provide an overview of the features of chatbots used by individuals for their mental health as reported in the empirical literature.
Methods:
Seven bibliographic databases (Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar) were used in our search. In addition, backward and forward reference list checking of the included studies and relevant reviews was conducted. Study selection and data extraction were carried out by two reviewers independently. Extracted data were synthesised using a narrative approach. Chatbots were classified according to their purposes, platforms, response generation, dialogue initiative, input and output modalities, embodiment, and targeted disorders.
Results:
Of 1039 citations retrieved, 53 unique studies were included in this review. Those studies assessed 41 different chatbots. Common uses of chatbots were: therapy (n = 17), training (n = 12), and screening (n = 10). Chatbots in most studies were rule-based (n = 49) and implemented in stand-alone software (n = 37). In 46 studies, chatbots controlled and led the conversations. While the most frequently used input modality was writing language only (n = 26), the most frequently used output modality was a combination of written, spoken and visual languages (n = 28). In the majority of studies, chatbots included virtual representations (n = 44). The most common focus of chatbots was depression (n = 16) or autism (n = 10).
Conclusion:
Research regarding chatbots in mental health is nascent. There are numerous chatbots that are used for various mental disorders and purposes. Healthcare providers should compare chatbots found in this review to help guide potential users to the most appropriate chatbot to support their mental health needs. More reviews are needed to summarise the evidence regarding the effectiveness and acceptability of chatbots in mental health
The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression
Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses
a significant health and economic burden across the individual and community. Not all
occurrences of depression require the same level of treatment. However, identifying
patients in need of advanced care has been challenging and presents a significant bottleneck
in providing care. We developed a knowledge-driven depression taxonomy comprised of
features representing clinical, behavioral, and social determinants of health (SDH) that
inform the onset, progression, and outcome of depression. We leveraged the depression
taxonomy to build decision models that predicted need for referrals across: (a) the overall
patient population and (b) various high-risk populations. Decision models were built using
longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients
seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded
significantly high predictive performance. However, models predicting need of treatment
across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models
representing the overall patient population (ROC of 78.87%). Next, we assessed the value
of adding SDH into each model. For each patient population under study, we built
additional decision models that incorporated a wide range of patient and aggregate-level
SDH and compared their performance against the original models. Models that
incorporated SDH yielded high predictive performance. However, use of SDH did not yield
statistically significant performance improvements. Our efforts present significant
potential to identify patients in need of advanced care using a limited number of clinical
and behavioral features. However, we found no benefit to incorporating additional SDH
into these models. Our methods can also be applied across other datasets in response to a
wide variety of healthcare challenges
Social media mental health analysis framework through applied computational approaches
Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div
When Infodemic Meets Epidemic: a Systematic Literature Review
Epidemics and outbreaks present arduous challenges requiring both individual
and communal efforts. Social media offer significant amounts of data that can
be leveraged for bio-surveillance. They also provide a platform to quickly and
efficiently reach a sizeable percentage of the population, hence their
potential impact on various aspects of epidemic mitigation. The general
objective of this systematic literature review is to provide a methodical
overview of the integration of social media in different epidemic-related
contexts. Three research questions were conceptualized for this review,
resulting in over 10000 publications collected in the first PRISMA stage, 129
of which were selected for inclusion. A thematic method-oriented synthesis was
undertaken and identified 5 main themes related to social media enabled
epidemic surveillance, misinformation management, and mental health. Findings
uncover a need for more robust applications of the lessons learned from
epidemic post-mortem documentation. A vast gap exists between retrospective
analysis of epidemic management and result integration in prospective studies.
Harnessing the full potential of social media in epidemic related tasks
requires streamlining the results of epidemic forecasting, public opinion
understanding and misinformation propagation, all while keeping abreast of
potential mental health implications. Pro-active prevention has thus become
vital for epidemic curtailment and containment
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