Online Healthcare Forums Research Articles

A risk identification model for detection of patients at risk of antidepressant discontinuation.

Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources. We analyzed 891 patient narratives from the online healthcare forum, "askapatient.com," utilizing content analysis to create PsyRisk-a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation. From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants. The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at risk of prematurely discontinuing their antidepressant treatment.

Disease and Adverse Drug Reaction Prediction using Machine Learning

The aim of the project is to use machine learning techniques for disease prediction, risk prediction and prediction of adverse drug reactions. The project is divided into two modules, an android app and a web app. The android app is to predict possible diseases based on the symptoms the person is showing. Along with that the reviews of common drugs from online healthcare forums such as medications.com are extracted and tf-idf is used to find out the possible adverse drug reactions the drugs may have. The web app does disease risk prediction based on phenotypic details and lab reports. As an addition to the project, location based medical help and health tips are also implemented.

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach.

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.

Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients' opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients' behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues.

A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis.

BackgroundPatient narrative data in online health care forums (communities) are receiving increasing attention from the scientific community for implementing patient-centered care. Natural language processing (NLP) methods are gaining more and more attention because of the enormous data volume. However, state-of-the-art NLP still cannot meet the need of high-resolution analysis of patients’ narratives. Manual qualitative analysis still plays a pivotal role in answering complicated research questions from analyzing patient narratives.ObjectiveThis study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums.MethodsOur systematic framework consists of 4 phases: (1) data collection, (2) data preparation, (3) content analysis, and (4) interpretation of the results. Data collection and data preparation phases are constructed based on text mining methods for identifying appropriate online health forums for data collection, differentiating posts of patients from other stakeholders, protecting patients’ privacy, sampling, and choosing the unit of analysis. Content analysis phase is built on the framework method, which facilitates and accelerates the identification of patterns and themes by an interdisciplinary research team. In the end, the focus of interpretation of the results phase is to measure the data quality and interpret the findings regarding the dimensions and aspects of patients’ experiences and concerns in their original contexts.ResultsWe demonstrated the usability of the proposed systematic framework using 2 case studies: one on determining factors affecting patients’ attitudes toward antidepressants and another on identifying the disease management strategies in patient with diabetes facing financial difficulties. The framework provides a clear step-by-step process for systematic content analysis of patient narratives and produces high-quality structured results that can be used for describing patterns or regularities in patients’ experiences, generating and testing hypotheses, and identifying areas of improvement in the health care systems.ConclusionsThe systematic framework is a rigorous and standardized method for qualitative analysis of patient narratives. Findings obtained through such a process indicate authentic dimensions and aspects of patient experiences and shed light on patients’ concerns, needs, preferences, and values, which are the core of patient-centered care.International Registered Report Identifier (IRRID)RR1-10.2196/13914

Opinion Analysis and Statistics about the Success Rates of Treatments Extracted From Online Healthcare Forums for the Disease Psoriasis using a Pipeline of LDA, NLP and Artificial Neural Network

This paper describes how the authors have applied NLP and text processing techniques to mine the messages related to the disease Psoriasis available online and posted by users who have undergone various types of treatments for the disease. The authors discuss the various classification techniques applied to classify the extracted messages as solution for the disease or non-solution. The authors also provide statistics regarding the success rates of the various types of treatments and medications in a consolidated form. A comparison study of the different types of treatments and their success rates is performed and statistics provided to the end users.

Exploring the Experiences of Cancer Patients With Chemotherapy-Induced Ototoxicity: Qualitative Study Using Online Health Care Forums.

BackgroundMany cancer patients and survivors experience permanent and life-debilitating effects, such as ototoxicity, from treatment. Ototoxicity manifests as high-frequency hearing loss and tinnitus, which can have a detrimental effect on the quality of life (QoL) of those affected. Currently, there is little information and support offered to these patients who experience ototoxicity, potentially leading to many being undiagnosed and untreated.ObjectiveThe aim of this study was to explore the extent of ototoxic side effects, such as hearing loss and tinnitus, and their impact on cancer patients following chemotherapy treatment. Secondary objectives included detecting the time periods of onset and duration of the ototoxicity and identifying what support was available to this population.MethodsPosts from publicly available online forums were thematically analyzed using the guidelines by Braun and Clarke. A coding manual was iteratively developed to create a framework for the analysis of the ototoxicity experience among the cancer population.ResultsA total of 9 relevant online forums were identified, consisting of 86 threads and 570 posts from 377 members. Following the bottom-up thematic analysis, 6 major themes were identified: nature of ototoxicity, time of experienced ototoxicity, information on ototoxicity, quality of life, therapies, and online social support.ConclusionsThere was a significant number of reports expressing concerns about the lack of information on the risk of ototoxicity. More support for those suffering is needed; for example, improved interdepartmental communication between oncology and audiology services could optimize patient care. Patients should also be encouraged to communicate with their health care professionals about their ototoxicity and relay how their QoL is impacted by ototoxicity when accessing support. Tinnitus was the most common concern and was associated with distress. Hearing loss was less common; however, it was associated with fear and employment issues. Those who reported preexisting conditions were fearful about worsening their condition as their QoL was already impacted.

An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums.

Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients. To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: medication, symptom, and background. Each sentence is projected into an interpretable feature space consisting of labeled sequential patterns, UMLS semantic types, and other heuristic features. A forest-based model is developed for categorizing OHF posts. An interpretation method is also developed, where the decision rules can be explicitly extracted to gain an insight of useful information in texts. Experimental results on real-world OHF data demonstrate the effectiveness of our proposed computational framework.

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

BackgroundAdverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance.MethodsWe treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.comis used in the training and validation of the HMM based Text Mining system.ResultsA 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.comand http://www.steadyhealth.comwere found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified.ConclusionsThe results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

An Exploratory Study of Canadian Aboriginal Online Health Care Forums

Internet-based discussion forums provide access to health information and social support, and serve as a resource for others. This investigation analyzed health-oriented Aboriginal Internet discussion forum (Forum A; Forum B) conversations. The findings were framed with Nutbeam's model of health literacy. Discussions within Forum B were centralized around issues of political activism and advocacy regarding Aboriginal health care. Activity in Forum A encouraged the development of “virtual” social capital, with health care discussions providing a “just-in-time” model for health education. Members of Forum A functioned as a smoking cessation self-help group and as health educators. The Internet provided a venue for the dissemination of health information and also served as a virtual voice for lifestyle coaching, political action, community building, and advocacy.