Browsing by Author "Kulanthaivel, Anand"
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Item An answer recommendation framework for an online cancer community forum(Springer Nature, 2023-05-15) Athira, B.; Idicula, Sumam Mary; Jones, Josette; Kulanthaivel, Anand; BioHealth Informatics, School of Informatics and ComputingHealth community forums are a kind of online platform to discuss various matters related to management of illness. People are increasingly searching for answers online, particularly when they are diagnosed with cancer like life-threatening diseases. People seek suggestions or advice through these platforms to make decisions during their treatments. However, locating the correct information or similar people is often a great challenge for them. In this scenario, this paper proposes an answer recommendation system in an online breast cancer community forum that provide guidance and valuable references to users while making decisions. The answer is the summary of already discussed topic in the forum, so that they do not need to go through all the answer posts which spans over multiple pages or initiate a thread once again. There are three phases for the answer recommendation system, including query similarity model to retrieve the past similar query, query-answer pair generation and answer recommendation. Query similarity model is employed by a Siamese network with Bi-LSTM architecture which could achieve an F1-score of 85.5%. Also, the paper shows the efficacy of transfer learning technique to generalize the model well in our breast cancer query-query pair data set. The query-answer pairs are generated by an extractive summarization technique that is based on an optimization algorithm. The effectiveness of the generated summary is evaluated based on a manually generated summary, and the result shows a ROUGE-1 score of 49%.Item Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study(Research Square, 2021) B., Athira; Jones, Josette; Idicula, Sumam Mary; Kulanthaivel, Anand; Zhang, Enming; BioHealth Informatics, School of Informatics and ComputingThe widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.Item AZEBRA (Almost Zero Error Basepair-based Record Alert): A genomic clinical decision support system(2017) Kulanthaivel, Anand; Kshirsagar, Madhura M.; Alarifi, Mohammad; Oki, Mark N.; Jones, Josette F.The idea of the United States's Precision Medicine Initiative (PMI) was to allow providers (and patients) to leverage large amounts of information (including patient genomic data) in order to create actionable knowledge that increases patient well-being. To this end, we propose a system called AZEBRA; the acronym stands for Almost Zero Error Basepair-based Record Alerts. Zebra, in addition to being a well-known wild animal, is a common medical slang term for the clinician's fallacy of mistakenly corning to a rare and sometimes dire diagnosis (the rare zebra diagnosis) due to having missed more common causes of patient symptoms (the common horse diagnosis); conversely, patients with rare conditions would be better thought of as zebras and not horses. AZEBRA is intended to leverage the principles of genetically-enhanced precision medicine in order to alert clinicians to the presence of patients with five (four rare, one common) genetic pathologies that are ordinarily sources of unnecessary morbidity and mortality in clinical settings.Item Digital Cohorts Within the Social Mediome: An Approach to Circumvent Conventional Research Challenges?(Elsevier, 2017-05) Kulanthaivel, Anand; Fogel, Rachel; Jones, Josette; Lammert, Craig; Biohealth Informatics, School of Informatics and ComputingItem An Expandable Drug Information Retrieval System for Oncology (EDIRS-FO)Jain, Lakshika; Jones, Josette; Kulanthaivel, AnandBreast cancer was life threating a decade ago, however, now with the improvement in treatment the survival rate has increased considerably. Avoidable adverse effects accompany these treatments. The risk of acute and chronic adverse effects caused due to treatment greatly influence the quality of life in breast cancer survivors. An information system can reduce the avoidable ADR, thus, improving the decision making during patient encounters. It also makes the access to the information easier for the clinicians.Item Help: defining the usability requirements of a breast cancer long-term survivorship (LTS) navigator(2017-08) Al-Abdulmunem, Monirah; Jones, Josette; Kulanthaivel, AnandLong-term survivors (LTSs) of breast cancer are defined as patients who have been in remission for a year or longer. Even after being declared breast-cancer-free, many LTSs have questions that were not answered by clinicians. Although online resources provide some content for LTSs, none, or very little, provide immediate answers to specific questions. Thus, the aim involves proposing specifications for a system, the Health Electronic Learning Platform (HELP), that can assist survivors by becoming an all-inclusive resource for LTSs of breast cancer. To achieve this, relevant information from the literature was used to assess the needs of LTSs. Also, data from a study involving the breast cancer survivor’s forum project that had been filtered to include posts with mentions of features to be added to the website and usability issues encountered. To complete the actual design of the system, a synthesis of the results obtained from these two sources was performed. HELP is simple in terms of its layout and consists of a main search-bar, where LTSs are able to ask questions using their own terms and language. This navigator should not be taken as definitive solution, but instead, should be used as a starting point toward better patient-centered care.Item Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure(Elsevier, 2017-12) Holden, Richard J.; Kulanthaivel, Anand; Purkayastha, Saptarshi; Kripalani, Sunil; BioHealth Informatics, School of Informatics and ComputingBACKGROUND: Personas are a canonical user-centered design method increasingly used in health informatics research. Personas-empirically-derived user archetypes-can be used by eHealth designers to gain a robust understanding of their target end users such as patients. OBJECTIVE: To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data. METHOD: Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n=30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables. RESULTS: Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers. DISCUSSION: Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.Item Neurological Disorders and Publication Abstracts Follow Elements of Social Network Patterns when Indexed Using Ontology Tree-Based Key Term Search(Springer, 2014) Kulanthaivel, Anand; Light, Robert P.; Börner, Katy; Kong, Chin Hua; Jones, Josette F.; BioHealth Informatics, School of Informatics and ComputingDisorders of the Central Nervous System (CNS) are worldwide causes of morbidity and mortality. In order to further investigate the nature of the CNS research, we generate from an initial reference a controlled vocabulary of CNS disorder-related terms and ontological tree structure for this vocabulary, and then apply the vocabulary in an analysis of the past ten years of abstracts (N = 10,488) from a major neuroscience journal. Using literal search methodology with our terminology tree, we find over 5,200 relationships between abstracts and clinical diagnostic topics. After generating a network graph of these document-topic relationships, we find that this network graph contains characteristics of document-author and other human social networks, including evidence of scale-free and power law-like node distributions. However, we also found qualitative evidence for Z-normal-type (albeit logarithmically skewed) distributions within disorder popularity. Lastly, we discuss potential consumer-centered as well as clinic-centered uses for our ontology and search methodology.Item Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum(JMIR, 2018) Jones, Josette; Pradhan, Meeta; Hosseini, Masoud; Kulanthaivel, Anand; Hosseini, Mahmood; Biohealth Informatics, School of Informatics and ComputingBackground: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life. [JMIR Med Inform 2018;6(4):e45]Item A Novel Approach Using Social Media to Investigate Patient-Centric Data in Autoimmune Hepatitis(2018) Kulanthaivel, Anand; Lammert, Craig S.; Jones, Josette F.Autoimmune hepatitis (AIH) is a rare liver disease characterized by immune-mediated destruction of hepatocytes. Despite adequate treatment, most patients experience severe extrahepatic symptoms that may reduce quality of life to date. The extent and impact of these symptoms remain poorly characterized as focused investigation in this realm has been deficient. In rare diseases, such as AIH, online support groups may provide the only environment where a sufficiently sized sample of patients can be examined for such issues. We aimed to electronically survey the text content of an AIH-affiliated online support group with over 1,000 users making over 18,000 communications. HYPOTHESIS: We hypothesized that we could use AIH-online support group text to identify key topics of clinically-related discussion and classify demographics and correlate these features to clinical topics, medications, and symptoms.