Medical Forums Research Articles

Analysis of Machine Learning and Deep Learning Frameworks for Opinion Mining on Drug Reviews

AbstractOpinion mining from medical forums such as health check-ups is sparking growing interest and a stimulating area for natural language processing. This allows for a better understanding of patient health status and drug reactions while generating new knowledge for health care professionals and drug manufacturers, which helps improve the quality of service and produce more effective treatments. In this paper, the researchers present a framework of opinions classification of drug reviews. The objective of this work is to find the best model for analyzing patients’ emotions about drugs. In this sense, the researchers oppose classical text vectorization methods (bag of words, term frequency-inverse document frequency) and word embedding methods (Word2vec, GloVe) for classical opinion mining face to modern machine learning tools with the Convolutional Neural Network (CNN), the Recurrent Neural Networks (Long Short-term Memory and Bidirectional Long Short-Term Memory). Experiments results show that the best model for drug reviews was achieved by CNN based on the Skip-gram model (85% accuracy). Experiments have led to conclude that the performance of a given model will depend on the type of dataset used, on feature representation and better collaboration between classifiers and feature extraction methods.

Does management reasoning constitute the backbone of the clinical learning environment?: Conceptual analysis of the existing notions.

Management reasoning is a paradigm whereby learning occurs in a context bound fashion on analysing the biophysical factors existing in the clinical learning environment. In the contemporary medical education forums, much importance is being laid on clinical reasoning and this warrants the appropriate usage of the biomedical knowledge in arriving at the diagnosis. We perceive that clinical reasoning, in pure sense, often doesn’t solve the purpose of rendering the best management plan to the patient. This holds stronger when the case is non-linear and highly complex in nature. Management reasoning fills the gap between hypotheses generation, i.e. accomplishing diagnosis and devising management plan. Indeed, it is a complex activity relying on several factors including the physician’s perceptual abilities and situated cognition derived from formal and informal learning experiences. In contrast to clinical reasoning, which can be taught using structured scenarios, management reasoning necessitates analysing multitude of factors revolving around a patient and prioritizing those in order to titrate the best possible management plan. This commentary spotlights different dimensions of management reasoning, emphasizes the need of teaching it in the current scenarios, enlists the ways it can be taught, and opens the platform for discussing further on this underemphasized topic.

Fertility and pregnancy outcomes following hysteroscopic metroplasty of uterine septa.

The septate uterus is still a matter of debate and discussion today in different medical forums. The controversy is divided between 2 different quite divergent opinions: on the one hand, those who promote its surgical correction; and on the other hand, those who advocate expectant management with no recommended surgical intervention. It is extremely difficult to determine the association between septum and infertility. Infertility is a multifactorial condition; furthermore, the mechanism by which the septate uterus is associated with infertility is still unknown, although the most probable causes seem to be related to implantation alterations. There is lack of high-quality randomized controlled studies with an adequate number of patients demonstrating the efficacy of the surgical treatment of patients with septated uterus. However, the limited available data based on observational studies, support minimally-invasive treatment using hysteroscopy as the method of choice for the treatment of patients with septated uterus and history of infertility or poor obstetrical outcomes.

Fertility and pregnancy outcomes following hysteroscopic metroplasty of uterine septa: an update

The septate uterus is still a matter of debate and discussion today in different medical forums. The controversy is divided between 2 different quite divergent opinions, on the one hand that of those who promote its surgical correction and on the other that of those who advocate expectant management with no recommended surgical intervention. It is extremely difficult to determine the association between septum and infertility. Infertility is a multifactorial condition, furthermore, the mechanism by which the septate uterus is associated with infertility is still unknown, although the most probable causes seem to be related to implantation alterations. There is lack of high quality randomized controlled studies with an adequate number of patients demonstrating the efficacy of the surgical treatment of patients with septated uterus. However, the limited available data based on observational studies, support minimally invasive treatment using hysteroscopy as the method of choice for the treatment of patients with septated uterus and history of infertility or poor obstetrical outcomes.

Discursive Practices of Doctor-Patient Online-Communication (the Case of “Health.mail.ru”)

Introduction. Nowadays medical online services are evolving rapidly and become more and more popular. Telemedicine is developed as a part of the Russian government program “The medical system 4P”. However, doctor-patient online communication has rarely been the subject of research in modern sociology. Nevertheless, online-services are valuable resource for researchers to study and analyze information that is difficult to obtain in other ways.Methodology and sources. The paper is devoted to the research of doctor-patient discursive practices on the internet medical advice forums by the example of “Health.mail.ru”. The general method of research is critical discourse analysis, based on the theory and methodology of N. Fairclough, which gives an opportunity to make suggestions about social practices using text analysis and discursive practices analysis. A non-reactive strategy was used in the empirical research. During the research 28 800 messages, which include 14 400 messages from patients and 14 400 messages from doctors, were analyzed. Data analysis was performed in the “R” program.Results and discussion. As a result, some assumptions about social practices and discursive practices on the online forum were formulated. The main patient’s goal on online forums is avoiding face-to-face interaction with doctors in a hospital. One of doctor’s goals is promoting their services. Interaction is carried out in a form of request from the patient and expert assessment from the doctor in response. Usually, patients send their messages anonymously. Doctors, by contrast, emphasize their identity. It helps them to maintain their image and to legitimate their expertise. Doctor’s and patient’s discourses are using special medical terminology, and they are not interpreting it. It demands from the patients being experts.Conclusion. Online communication is a new type of doctor-patient communication, which is more democratic, more complementary and more impersonal than face-to-face communication. Usually doctor-patient online communication on the internet medical advice forums does not involve thorough analysis of individual patient’s situation and maintaining sustainable interpersonal relationships.

Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.

Sentiment Analysis System for Classification of Patient-Generated Health Reviews Using Rough Set Theory and Machine Learning Technique

Advancements in social media domain have led to a prominent progress in the number of online communities. Sites, such as Twitter and Facebook, provide an avenue for the unrestricted generation, communication, and distribution of messages as well as information. In this work, we propose a sentiment classification system from patient-generated content posted by users on medical forums and social media sites. The rough set theory is a numerical rule-based technique employed for categorizing and examining doubtful, partial or indistinct data. The emphasis of this study is on the employment of the rough set theory technique for sentiment classification of patient-generated health reviews. We investigated four rough set theory-based algorithms, namely: Genetic, Learning from Examples Module version 2 (LEM2), Exhaustive and Covering, to generate rules for sentiment classification of patient-generated health reviews text. The Rough Set Exploration System (RSES 2.0) software is utilized to conduct experiments. Additionally, we applied SVM classifier to classify emotions. The experimental results show that the Genetic algorithm outperforms the comparing algorithms with an accuracy of 84.2% and Support Vector Machine outperforms other classifiers with an accuracy of 80.5%.

Empowered patient autonomy in the network in the selected area of theoretical reflection and empirical analysis

The aim of the article is to perceive the understanding of autonomy by conscious, empowered patients through an awareness of responsibility for their own health and knowledge of their rights. This perception was made by analysing their statements published in 16 online medical forums and portals, which referred to the components of autonomy. One hundred and twenty-seven posts qualified for qualitative analysis. Based on the research findings, it was concluded that the authors of the posts (intuitively) properly identified autonomy (although they did not use this notion as such), paying attention to principal, formative qualities and inherent rights. They identify these qualities based on the course and characteristics of interactions between patients and healthcare practitioners (exposing their deficits), within the context of participants’ rights and duties as well as their opinions about physicians’ qualifications and desired (mainly soft) competences.

Individuals with back and neck pain on medical forums: What do they mention? What do they fear?

The use of online medical forums is on the rise globally. Data scraping is a method of extracting website content using an automated computer program. We scraped users' questions regarding back and neck pain (BNP) from popular Israeli online medical forums. We aimed to identify the sort of questions being asked about BNP, and to analyse explicit themes that characterize their questions. Six leading Israeli BNP forums were identified. In phase 1, Python scripts scraped 12,418 questions into a data set. In phase 2 - five themes were identified: Surgery (n=2,957); health care professions (n=2,361); Sports (n=2,304); drugs (n=1,419) and interpretation of imaging (n=845). Phase 3 - included the categorization of explicit fear-related words by the authors. Phase 4 - analysis of explicit fear-related themes yielded 402 questions. Gender was identified for 394 users, and age was identified for 181 users. A total of 248 users (61.6%) were women and 146 men (36.3%). Mean age 36.3±16.15 for women and 35.5±16.1 for men. The most commonly expressed fears were related to: invasive procedures, 30.9% (131 questions); fear of serious condition or misdiagnosis, 17.0% (72 questions); General concerns, 13.7% (58 questions); fear of worsening or relapse, 12.3% (52 questions); adverse effects of oral drugs or radiation, 10.8% (46 questions) and concerns related to lifestyle, 9.7% (41 questions). Web scraping is a feasible strategy with which to explore medical forums and the above-mentioned themes, all of which are of potential clinical significance. Using automated algorithms, a total of 12,369 questions from online back and neck medical forums were scraped and analysed. Secondary analysis categorized fear-related themes that were mentioned by users. Identifying and addressing patients' fear has potential to improve communication and therapeutic outcome. For example, questions regarding surgery were typically asked after the option was mentioned by a physician. This insight should encourage physicians to devote extra time explaining the possible implications of surgery, should they consider it as an option.

The Need for Vascular Laboratory Accreditation Within the Veterans Administration

Vascular imaging accreditation is a significant platform for clinical diagnostic evaluation and correlation. There is a notifiable disconnect between the private imaging facilities and the federal imaging laboratories. Online medical forums have increased patient expectations which give a visual appearance of underutilization of qualified talent, within the federal imaging field. Encouraging federal leadership to pursue a patient quality centered approach that will increase patient advocacy and elevate the expectations of imaging personnel, for the Veteran patient.

Social Media Analytics: Impact & Implications in Healthcare

Sharing of ideas, opinions or any information with the help of the medium of virtual networks, online communities and computer-based innovations is known as social media. Blogs, social networking sites like Facebook, WhatsApp, YouTube etc, Microblogging mediums, Photos etc. that run with the help of internet, provides the end-users with swift communication and aids in providing awareness, by influencing large audiences contributes to social media. Healthcare sector is rapidly adapting social media by providing a medium to the patients, physicans and HCP’s to discuss and communicate medical information and knowledge outside the vicinity of a hospital or clinic. These days, patients, their relatives, friends or family use the social media to provide knowledge to the world and educate them incase they have similar ailments. There are several blogs and forums related to medical knowledge where users tend to ask their problems to the physicians and get a reply back to their problems.Medical specialists join such online medical forums that helps them to research in a better manner, refer colleagues, increase their network and read health related articles and updates. With every good thing, comes an attached risk which is related to the use of social mediums, where the image of a Healthcare professional could be at stake due to the unprofessional content against them. There are various limitations to the HCP’s in case they seek to share patient stories online as it could be a case of breach & privacy. Nevertheless, this research study highlights the role played by social media in digitally redefining and empowering healthcare sector from the perspective of clinical and patient centric health literacy and health guides.

Using cognition to resolve duplicacy issues in socially connected healthcare for smart cities

Social web has transformed healthcare communication as patients reach out to seek support and advice by connecting with other patients, caregivers and healthcare professionals. The influx of health-related queries and the volume of answers within the medical forums is a testimony to this adaption. The scalability, natural interaction and dynamism of the continuously collected and connected user-generated social big data can support health assessment, intervention and provisioning to produce the best kind of cognitive smart city. On the flip side the use of social media for healthcare communication suffers from data deluge, lack of reliability and quality, confidentiality and privacy (location/personal) issues. Duplicate questions, that is, queries with similar semantics (meaning) corrupt the filtering mechanism, increase the response time and compromise with the quality of the answer too. This research puts forward solutions to resolve the key challenge of duplicacy within the medical community Question-Answering sites (Medical CQAs). We propose to solve the semantic question matching problem for duplicate question pair detection, using a hybrid deep learning model, which combines a Co-attention based Bi-Directional Long Short-Term Memory (Bi-LSTM) Siamese neural network and a Multi-layer perceptron classifier to output the probability of a similarity match between the two questions. Euclidean distance function is then used to compute the similarity between questions. The proposed model is validated on 100 question pairs which are scrapped from three featured groups, namely, ‘Irritable Bowel Syndrome’, ‘Anxiety Disorder’ and ‘Menopause’ of Patient.info community forum and an accuracy of 86.375% is observed. The results obtained are comparable to that of the Quora’s state-of-the-art results for duplicate detection.

The social phenotype: Extracting a patient-centered perspective of diabetes from health-related blogs.

It has recently been argued [1] that the effectiveness of a cure depends on the doctor-patient shared understanding of an illness and its treatment. Although a better communication between doctor and patient can be pursued through dedicated training programs, or by collecting patients' experiences and symptoms by means of questionnaires, the impact of these actions is limited by time and resources. In this paper we suggest that a patient-centered view of a disease - as well as potential misalignment between patient and doctor focuses - can be inferred at a larger scale through automated textual analysis of health-related forums. People are generating an enormous amount of social data to describe their health care experiences, and continuously search information about diseases, symptoms, diagnoses, doctors, treatment options and medicines. By automatically collecting, analyzing and exploiting this information, it is possible to obtain a more detailed and nuanced vision of patients' experience, that we call the "social phenotype" of diseases. As a use-case for our analysis, we consider diabetes, a widespread disease in most industrialized countries. We create a high quality data sample of diabetic patients' messages in Italy, extracted from popular medical forums during more than 10 years. Next, we use a state-of-the-art topic extraction technique based on generative statistical models improved with word embeddings, to identify the main complications, the frequently reported symptoms and the common concerns of these patients. Finally, in order to detect differences in focus, we compare the results of our analysis with available quality of life (QoL) assessments obtained with standard methodologies, such as questionnaires and survey studies. We show that patients with diabetes, when accessing on-line forums, express a perception of their disease in a way that might be noticeably different from what is inferred from published QoL assessments on diabetes. In our study, we found that issues reported to have a daily impact on these patients are diet, glycemic control, drugs and clinical tests. These problems are not commonly considered in QoL assessments, since they are not perceived by doctors as representing severe limitations. Although limited to the case of Italian diabetic patients, we suggest that the methodology described in this paper, which is language and disease agnostic, could be applied to other diseases and countries, since misalignment between doctor and patients, and the importance of collecting unbiased patient perceptions, has been emphasized in many studies ([2,3]inter alia). Extracting the social phenotype of a disease might help acquiring patient-centered information on health care experiences on a much wider scale.

Implementation of Software Agents and Advanced AoA for Disease Data Analysis.

To eliminate the possibilities of getting various contradicting solutions to a single problem during diagnosis, a single regular Agent oriented Approach (AoA) is replaced by Intelligent Artificial Agents that act like human and even dynamically decide in any situations known as Intelligent Searching Approach (ISA) is proposed. These agents are used to analyse the medical forums and results or findings are derived accurately than any manual approach. Multiple Agents have been used to analyse the blogs by dividing the work areas and communicating themselves using Agent Communication Language (ACL) and FIPA. The local solutions thus formed are forwarded to a global agent. This Global Agent controls all operations and makes the decision about the best solution. As the Global Agent controls all other agents, it eradicates unwanted and ineffective communication between the various local agents and hence keeping the time taken for communication at the minimum level. Based on these solutions a prioritization matrix is formed using advanced clustering techniques to create a prioritized content of suggested best solutions. Once the decision is made, the refining process runs several times recursively checking for all possible better solutions solving the input. On completion of this process, the Global Agent returns the exact result of the discussion. This process saves time rather than researching the entire blog for result data. This advanced approach lights a different way of obtaining solution keeping the time taken for discussion and intercommunication between the agents to the minimal level but not compromising on the perfection of the solution at the same time.

Potential benefits and harms of the use of UV radiation in transmission of tuberculosis in South African health facilities.

The incidence and prevalence of transmitted Mycobacterium tuberculosis have risen very rapidly in modern society. Environmental control measure such as ultraviolet radiation has been introduced in various health care facilities. This preventative measure has been extensively explored in the medical, legislative and public forums. However, the guidelines and manufacturer’s claims have created controversies, in terms of prevention of cross-transmission of M. tuberculosis in health care facilities. In this article, the authors reviewed the overall benefits and harms associated with the use of ultraviolet radiation in the prevention of M. tuberculosis transmission. The author concluded that there are still existing gaps in proving beyond any reasonable doubt that ultraviolet radiations absolutely prevent the spread of M. tuberculosis in South African health facilities.

Communicating Elective Sterilization: A Feminist Perspective

Patient-OBGYN (obstetrics and gynecology) communication about contraception and reproduction can be fraught with ideological pressures, cultural assumptions, and emotion-based claims and concerns. Specifically, the topic of elective sterilization for women often invokes preconceived notions of femininity and mothering. Based on medical pamphlets and online discussion forums, our analysis reveals how gendered discrepancies exist in medical information about elective sterilization. This persuasion brief aims to invite OBGYNs to understand how cultural and traditional views of gender inform medical decisions and oppress women’s reproductive autonomy. It offers suggestions for OBGYNs, women seeking sterilization, and scholars in the rhetoric of health and medicine.

I-ACT: Integrated study on effect of Activity on ComplicaTions in pregnancy: study protocol of a multiethnic prospective cohort study

IntroductionPhysical activity (PA) during first 20 weeks of pregnancy may lower risks of gestational diabetes mellitus (GDM) and gestational hypertension (GH), though evidence of association remains inconclusive. Current studies rely...

Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum

Sentiment analysis is the most common text classification tool that analyzes incoming messages and tells whether the underlying sentiment is positive, negative, or neutral. We can use this technique to understand people by gender, especially people who are suffering from a sensitive disease. People use health-related web forums to easily access health information written by and for non-experts and also to get comfort from people who are in a similar situation. The government operates medical web forums to provide medical information, manage patients’ needs and feelings, and boost information-sharing among patients. If we can classify people’s emotional or information needs by gender, age, or location, it is possible to establish a detailed health policy specialized into patient segments. However, people with sensitive illness such as AIDS tend to hide their information. Especially, in the case of sexually transmitted AIDS, we can detect problems and needs according to gender. In this work, we present a gender detection model using sentiment analysis and machine learning including deep learning. Through the experiment, we found that sentiment features generate low accuracy. However, senti-words give better results with SVM. Overall, traditional machine learning algorithms have a high misclassification rate for the female category. The deep learning algorithm overcomes this drawback with over 90% accuracy.

Impact of Emotional Support, Informational Support, and Norms of Reciprocity on Trust Toward the Medical Aesthetic Community: The Moderating Effect of Core Self-Evaluations.

BackgroundThe consumption of medical aesthetic services has become popular in recent years. Many people have purchased medical aesthetic services and treatments in pursuit of self-beauty. When members of online medical aesthetic communities actively participate in discussions and encourage and support one another, there is an increase in community commitment, trust toward each other, and trust toward the community, ultimately promoting social sharing in an environment of positive feedback.ObjectiveThis study aimed to explore via the theory of social support—grounded in a deeper social capabilities framework developed by Khan following the Nobel laureate Amartya Sen’s groundbreaking work—whether emotional support, informational support, and norms of reciprocity in online communities impact group members in terms of creating trust toward other members. This enhances trust toward the community and generates a sense of community commitment, ultimately impacting social buying intention and social sharing intention.MethodsThis study used IBM SPSS and AMOS to analyze data. Data were collected through online questionnaires in online medical aesthetic community forums, thereby producing samples that were both representative and accurate. To understand whether core self-evaluation (CSE) is a moderator in the relationship between social sharing intention and social buying intention, this study averaged the point of CSEs in the sample after statistical analysis, dividing the sample into 2 groups.ResultsThe results showed that emotional support and norms of reciprocity positively impact trust toward members, and trust toward members positively impact trust toward the community. This generates trust transfer, which positively impacts social buying intention and social sharing intention. At the same time, CSE is a moderator variable between trust toward the community and social buying intention, but CSE is not a moderator variable between trust toward the community and social sharing intention.ConclusionsThis study revealed that when members of online medical aesthetic communities actively participate in discussions and encourage and support one another, community commitment, trust toward each other, and trust toward the community increases, ultimately promoting social sharing and buying intentions.

Disaster communication

PurposeThe purpose of this paper is to understand the crisis communication strategies used by the Caribbean medical tourism industry in the 2017 hurricane season, and also evaluate the quality of the disaster communication messages delivered via digital mediums.Design/methodology/approachThis study includes a comprehensive, qualitative content analysis of 149 risk and crisis messages from 51 healthcare organizations distributed through digital media. The medical tourism providers (MTPs) include hospitals, medical tourism facilitators, practitioners/private physicians, specialty clinics, and dental and cosmetic providers.FindingsNearly half of the MTPs included in the data set delivered no post-disaster information to external audiences. The most prominent post-disaster message strategy utilized was conveying operational messages. Furthermore, an unexpected finding was the sheer magnitude of unrelated health-oriented and promotional destination marketing content disseminated before, during and after these events.Research limitations/implicationsThis analysis excludes internal organizational channels of communication which may have been used to communicate risk and crisis messages during these events (i.e. employee e-mails, announcements made through intercom systems, etc.). Our analysis does not include content disseminated through medical tourism forums (i.e. Realself.com, Health Traveler’s Forum, FlyerTalk Forum).Practical implicationsSmall-scale MTPs can improve on any weaknesses through proactive planning and preparation by creating organizational goals to complete basic crisis communication training courses and in doing so support the applied professional development of disaster and crisis responders in the Caribbean region. Second, MTPs exposed to similar risks of natural disasters may use these findings for comparative analysis purposes to support their own organizational planning. Finally, this study supports the continued utility of the National Center for Food Protection & Defense guidelines for analyzing and evaluating organizational performance.Originality/valueCurrently much of the academic scholarship of applied disaster communication narrowly focuses on the response strategies of one organization, or analyzes one social media platform at a time (i.e. Twitter). A strength of this analysis is the inclusion of an organizational sector (i.e. Caribbean medical tourism providers) and the range of platforms from which the content was captured (e.g. websites, org. blogs and social media networks).