Utilizing Consumer Health Posts for Pharmacovigilance: Identifying Underlying Factors Associated with Patients’ Attitudes Towards Antidepressants

Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in increased risk of relapse, emergency visits, and significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. The traditional methods for identifying the key dimensions of patients’ attitudes towards antidepressants are associated with some methodological limitations, such as concern about confidentiality of personal information. In this study, attempts have been made to address the limitations by utilizing patients’ self report experiences in online healthcare forums to identify underlying factors affecting patients attitudes towards antidepressants. The data source of the study was a healthcare forum called “askapatients.com”. 892 patients’ reviews were randomly collected from the forum for the four most commonly prescribed antidepressants including Sertraline (Zoloft) and Escitalopram (Lexapro) from SSRI class, and Venlafaxine (Effexor) and duloxetine (Cymbalta) from SNRI class. Methodology of this study is composed of two main phases: I) generating structured data from unstructured patients’ drug reviews and testing hypotheses concerning attitude, II) identification and normalization of Adverse Drug Reactions (ADRs), Withdrawal Symptoms (WDs) and Drug Indications (DIs) from the posts, and mapping them to both The UMLS and SNOMED CT concepts. Phase II also includes testing the association between ADRs and attitude. The result of the first phase of this study showed that “experience of adverse drug reactions”, “perceived distress received from ADRs”, “lack of knowledge about medication’s mechanism”, “withdrawal experience”, “duration of usage”, and “drug effectiveness” are strongly associated with patients attitudes. However, demographic variables including “age” and “gender” are not associated with attitude. Analysis of the data in second phase of the study showed that from 6,534 identified entities, 73% are ADRs, 12% are WDs, and 15 % are drug indications. In addition, psychological and cognitive expressions have higher variability than physiological expressions. All three types of entities were mapped to 811 UMLS and SNOMED CT concepts. Testing the association between ADRs and attitude showed that from twenty-one physiological ADRs specified in the ASEC questionnaire, “dry mouth”, “increased appetite”, “disorientation”, “yawning”, “weight gain”, and “problem with sexual dysfunction” are associated with attitude. A set of psychological and cognitive ADRs, such as “emotional indifference” and “memory problem were also tested that showed significance association between these types of ADRs and attitude. The findings of this study have important implications for designing clinical interventions aiming to improve patients\u27 adherence towards antidepressants. In addition, the dataset generated in this study has significant implications for improving performance of text-mining algorithms aiming to identify health related information from consumer health posts. Moreover, the dataset can be used for generating and testing hypotheses related to ADRs associated with psychiatric mediations, and identifying factors associated with discontinuation of antidepressants. The dataset and guidelines of this study are available at https://sites.google.com/view/pharmacovigilanceinpsychiatry/hom

Case-Based-Reasoning System for Feature Selection and Diagnosing Disease; Case Study: Asthma

Asthma is a chronic informatory disease of the respiratory canals in which it has not become obvious what is the reason for the reports argumentation on the ground of asthma prevalence. In the present research, the purpose would be to design a case-based-reasoning (CBR) model in order to assist a physician to diagnose the type of disease and also the needed therapy. At first for designing this system, the disease variables were discriminated and were at the patients' disposal as a questionnaire, and after gathering the relevant data (CBR) algorithm was rendered on the data which led to the asthma diagnosis. The system was tested on 325 asthmatic and non asthmatic adult cases and was accessed with eighty percent accuracy. The consequences were promising. With regard to the fact that the factors of the disease are different in various countries, This study was performed in order to determine risk factors for asthma in Iranian society and the results of research showed that the most important variables of asthma disease in Iran are symptoms heperresponsivity, frequency of cough, cough. Key words: data mining, case based reasoning, asthma, diagnosis

Building a Portal to Health Resources for Cancer Survivors

Cancer patients and their relatives access the WWW health resources when they covertly question the diagnosis and treatment, often with a positive impact on health outcomes. Thus, "cancer survivors" (post treatment) may continue to use the WWW as an information source. Little research exists related to the information needs of cancer survivors, their caregivers and how these needs can be met

Association between choline supplementation and Alzheimer’s disease risk: a systematic review protocol

Background and aimsThere is growing evidence suggesting choline intake might have beneficial effects on cognitive function in the elderly. However, some studies report no relationship between choline intake and cognitive function or improvement in Alzheimer’s disease patients. This protocol is for a systematic review of choline intake and Alzheimer’s disease that aims to assess the comparative clinical effectiveness of choline supplementation on Alzheimer’s disease risk.Methods and analysisliterature search will be performed in PubMed, MEDLINE, EMBASE, CINAHL, Scopus, Cochrane, and the Web of Science electronic databases from inception until October 2023. We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies will be included if they compared two different time points of choline biomarkers measures in men or women (65+) with Alzheimer’s Disease. The risk of bias in the included studies will be assessed within the Covidence data-management software.ResultsThis review will summarize the clinical trial and quasi-experimental evidence of choline intake on Alzheimer’s disease risk for adults aged 65+. The results from all eligible studies included in the analysis will be presented in tables, text, and figures. A descriptive synthesis will present the characteristics of included studies (e.g., age, sex of participants, type, length of intervention and comparator, and outcome measures), critical appraisal results, and descriptions of the main findings.DiscussionThis systematic review will summarize the existing evidence on the association between Choline intake and AD and to make recommendations if appropriate. The results of this review will be considered with respect to whether there is enough evidence of benefit to merit a more definitive randomized controlled trial. The results will be disseminated through peer-reviewed journals population.ConclusionThis protocol outlines the methodology for a systematic review of choline intake and AD. The resulting systematic review from this protocol will form an evidence-based foundation to advance nutrition care for individuals with AD or poor cognitive function.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO, identifier CRD42023395004

Patient-Centered Decision Support for Pediatric Asthma Screening: A Web-based Interface for Parents

Digitized for IUPUI ScholarWorks inclusion in 2021.Asthma in children is a global health crisis. Differential diagnosis of asthma is a complex process. Significant disconnects exist between disease prevalence vs. diagnoses. Families ignore or misunderstand asthma signs and symptoms. Families experience barriers to screening and diagnosis: health literacy, costs, travel and time, limited access to expert care, etc. Under-diagnosis results in significant individual and societal burdens. Early diagnosis leads to more effective disease management

Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media

BackgroundMedication-assisted treatment (MAT) is an effective method for treating opioid use disorder (OUD), which combines behavioral therapies with one of three Food and Drug Administration–approved medications: methadone, buprenorphine, and naloxone. While MAT has been shown to be effective initially, there is a need for more information from the patient perspective about the satisfaction with medications. Existing research focuses on patient satisfaction with the entirety of the treatment, making it difficult to determine the unique role of medication and overlooking the views of those who may lack access to treatment due to being uninsured or concerns over stigma. Studies focusing on patients’ perspectives are also limited by the lack of scales that can efficiently collect self-reports across domains of concerns. ObjectiveA broad survey of patients’ viewpoints can be obtained through social media and drug review forums, which are then assessed using automated methods to discover factors associated with medication satisfaction. Because the text is unstructured, it may contain a mix of formal and informal language. The primary aim of this study was to use natural language processing methods on text posted on health-related social media to detect patients’ satisfaction with two well-studied OUD medications: methadone and buprenorphine/naloxone. MethodsWe collected 4353 patient reviews of methadone and buprenorphine/naloxone from 2008 to 2021 posted on WebMD and Drugs.com. To build our predictive models for detecting patient satisfaction, we first employed different analyses to build four input feature sets using the vectorized text, topic models, duration of treatment, and biomedical concepts by applying MetaMap. We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients’ satisfaction. Lastly, we compared the prediction models’ performance over different feature sets. ResultsTopics discovered included oral sensation, side effects, insurance, and doctor visits. Biomedical concepts included symptoms, drugs, and illnesses. The F-score of the predictive models across all methods ranged from 89.9% to 90.8%. The Ridge classifier model, a regression-based method, outperformed the other models. ConclusionsAssessment of patients’ satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors’ visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence

U.S. Hospitals' Web-Based Patient Engagement Activities

Digitized for IUPUI ScholarWorks inclusion in 2021.The purpose of this poster is to describe how U.S. Hospitals use their websites to meet the National e-Health Collaborative (NeHC) patient engagement criteria and to explore trends, challenges, opportunities for hospitals when it comes to leveraging websites for patient engagement

Development of an Adverse Drug Reaction Corpus from Consumer Health Posts

UWM-Adverse Drug Events Corpus (UWM-ADEC) is an annotated corpus that has been developed from consumer drug review posts in social media. In this corpus, we identified four types of Adverse Drug Reactions (ADRs) including physiological, psychological, cognitive, and functional problems. Additionally, we mapped the ADRs to corresponding concepts in Unified medical language Systems (UMLS). The quality of the corpus was measured using well-defined guidelines, double coding, high inter-annotator agreement, and final reviews by pharmacists and clinical terminologists. This corpus is a valuable source for research in the area of text mining and machine learning for ADRs identifications from consumer health posts, specifically for psychiatric medication

Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study

BackgroundPatients’ spontaneous speech can act as a biomarker for identifying pathological entities, such as mental illness. Despite this potential, audio recording patients’ spontaneous speech is not part of clinical workflows, and health care organizations often do not have dedicated policies regarding the audio recording of clinical encounters. No previous studies have investigated the best practical approach for integrating audio recording of patient-clinician encounters into clinical workflows, particularly in the home health care (HHC) setting. ObjectiveThis study aimed to evaluate the functionality and usability of several audio-recording devices for the audio recording of patient-nurse verbal communications in the HHC settings and elicit HHC stakeholder (patients and nurses) perspectives about the facilitators of and barriers to integrating audio recordings into clinical workflows. MethodsThis study was conducted at a large urban HHC agency located in New York, United States. We evaluated the usability and functionality of 7 audio-recording devices in a laboratory (controlled) setting. A total of 3 devices—Saramonic Blink500, Sony ICD-TX6, and Black Vox 365—were further evaluated in a clinical setting (patients’ homes) by HHC nurses who completed the System Usability Scale questionnaire and participated in a short, structured interview to elicit feedback about each device. We also evaluated the accuracy of the automatic transcription of audio-recorded encounters for the 3 devices using the Amazon Web Service Transcribe. Word error rate was used to measure the accuracy of automated speech transcription. To understand the facilitators of and barriers to integrating audio recording of encounters into clinical workflows, we conducted semistructured interviews with 3 HHC nurses and 10 HHC patients. Thematic analysis was used to analyze the transcribed interviews. ResultsSaramonic Blink500 received the best overall evaluation score. The System Usability Scale score and word error rate for Saramonic Blink500 were 65% and 26%, respectively, and nurses found it easier to approach patients using this device than with the other 2 devices. Overall, patients found the process of audio recording to be satisfactory and convenient, with minimal impact on their communication with nurses. Although, in general, nurses also found the process easy to learn and satisfactory, they suggested that the audio recording of HHC encounters can affect their communication patterns. In addition, nurses were not aware of the potential to use audio-recorded encounters to improve health care services. Nurses also indicated that they would need to involve their managers to determine how audio recordings could be integrated into their clinical workflows and for any ongoing use of audio recordings during patient care management. ConclusionsThis study established the feasibility of audio recording HHC patient-nurse encounters. Training HHC nurses about the importance of the audio-recording process and the support of clinical managers are essential factors for successful implementation