Influence of Twitter on Hydroxychloroquine Medication Prescriptions for COVID-19 Patients

2023 | International Conference on Information Systems | Citations: 0

Authors: Gorthi, Sai Soundarya; Ganju, Kartik; Pinsonneault, Alain

Abstract: Social media is proposed to play a crucial role in healthcare providers' care d ... Expand

Abstract: Social media is proposed to play a crucial role in healthcare providers' care decisions. However, social media information may not always be reliable as false information is prone to virality, and emotional cues may drive information sharing. Hence, in our study, we strive to understand the influence of social media on healthcare providers' care decisions by empirically examining the influence of Twitter discourse regarding Hydroxychloroquine (HCQ) on the actual prescribing rates of the drug in the USA for treating Collapse

Topics: Twitter social media application programming interface Python missing data

Methods: longitudinal research machine learning qualitative coding autocorrelation analysis mixed method

Theories: elaboration likelihood model

Metaverse Knowledge Graph Construction: An Unsupervised Relation Extraction Approach based on Semantic Mining

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Li, Litao; Xu, Ruiyun; Zhao, J. Leon

Abstract: Metaverse has gained widespread attention in both academia and industry. As a r ... Expand

Abstract: Metaverse has gained widespread attention in both academia and industry. As a result, many new concepts, methodologies, and applications are emerging in recent years. Constructing a Metaverse knowledge graph can help with knowledge management and facilitate various intelligent tasks such as recommendations, information retrieval, and chatbots in this emerging field. However, existing knowledge graph construction approaches are not directly applicable in the Metaverse domain due to a lack of labeled dataset, making existing supervised relation extraction approaches impracticable. In this study, we develop a novel graph construction framework by integrating the deep learning approach (i.e., BERT-BiLSTM-CRF) with an unsupervised relation extraction approach based on semantic mining. The framework we propose in this paper can automatically construct knowledge graphs while significantly reducing labeling efforts. Further, we plan to implement the proposed framework on a Metaverse corpus to construct a Metaverse knowledge graph. To test our approach, we will employ an expert-survey method to evaluate the quality of triple sets. Collapse

Topics: virtual reality knowledge graph mobile application blog artificial intelligence

Methods: BERT survey word2vec deep learning named entity recognition

Mapping the Emotional Journey of STD patients in Online Forums

2023 | Americas Conference on Information Systems | Citations: 0

Authors: THIMMANAYAKANAPALYA, Sagarika Suresh; Sharman, Raj; Mulgund, Pavankumar; Parthiban, Lakshmi Prithika; Samant, Harshada

Abstract: People inflicted with diseases such as STDs experience a tremendous number of v ... Expand

Abstract: People inflicted with diseases such as STDs experience a tremendous number of varying emotions from the onset of symptoms to being cured/living with it. Leveraging the power of bidirectional encoder representation transformers our study builds a custom-named entity recognition model which identifies uniquely associated emotions at various stages of the disease. Our study uses over 1 million posts derived from online communities regarding STDs. We further provide analytical insights into varying emotions at different stages based on contextual reasonings. Finally, we utilize the resulting insights to produce prescriptive measures for future online recommendation systems on online communities, and for stakeholders hoping to build devices that address problems about stigmatized disorders such as STDs. Collapse

Topics: online community recommender system misinformation website Reddit

Methods: BERT computational algorithm named entity recognition machine learning language model

Transformer-Based Multi-Task Learning for Crisis Actionability Extraction

2023 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Yuhao; Lo, Siaw Ling; Myint, Phyo Yi Win

Abstract: Social media has become a valuable information source for crisis informatics. W ... Expand

Abstract: Social media has become a valuable information source for crisis informatics. While various methods were proposed to extract relevant information during a crisis, their adoption by field practitioners remains low. In recent fieldwork, actionable information was identified as the primary information need for crisis responders and a key component in bridging the significant gap in existing crisis management tools. In this paper, we proposed a Crisis Actionability Extraction System for filtering, classification, phrase extraction, severity estimation, localization, and aggregation of actionable information altogether. We examined the effectiveness of transformer-based LSTM-CRF architecture in Twitter-related sequence tagging tasks and simultaneously extracted actionable information such as situational details and crisis impact via Multi-Task Learning. We demonstrated the system's practical value in a case study of a real-world crisis and showed its effectiveness in aiding crisis responders with making well-informed decisions, mitigating risks, and navigating the complexities of the crisis. Collapse

Topics: Twitter social media systems design hurricane knowledge base

Methods: BERT case study GloVe machine learning experiment

MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: van der Linden, Sanne; Sevastjanova, Rita; Funk, Mathias; El-Assady, Mennatallah

Abstract: Healthcare systems are under pressure from an aging population, rising costs, an ... Expand

Abstract: Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated by five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients. Collapse

Topics: decision support knowledge base visual analytics knowledge representation healthcare data

Methods: qualitative interview qualitative content analysis exploratory data analysis ontological modelling usability testing

Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

2022 | Information Systems Research | Citations: 1

Authors: Bauman, Konstantin; Tuzhilin, Alexander

Abstract: In this paper, we study an important problem of parsing contextual information f ... Expand

Abstract: In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that relies on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations. Collapse

Topics: online review recommender system logistics management user experience user-generated content

Methods: word2vec hierarchical clustering machine learning product review experiment

PNRank: Unsupervised ranking of person name entities from noisy OCR text

2022 | Decision Support Systems | Citations: 0

Authors: Dutta, Haimonti; Gupta, Aayushee

Abstract: Text databases have grown tremendously in number, size, and volume over the last ... Expand

Abstract: Text databases have grown tremendously in number, size, and volume over the last few decades. Optical Character Recognition (OCR) software is used to scan the text and make them available in online repositories. The OCR transcription process is often not accurate resulting in large volumes of garbled text in the repositories. Spell correction and other post-processing of OCR text often prove to be very expensive and time-consuming. While it is possible to rely on the OCR model to assess the quality of text in a corpus, many natural language processing and information retrieval tasks prefer the extrinsic evaluation of the effect of noise on the task at hand. This paper examines the effect of noise on the unsupervised ranking of person name entities by first populating a list of person names using an out-of-the-box Named Entity Recognition (NER) software, extracting content-based features for the identified entities, and ranking them using a novel unsupervised Kernel Density Estimation (KDE) based ranking algorithm. This generative model has the ability to learn rankings using the data distribution and therefore requires limited manual intervention. Empirical results are presented on a carefully curated parallel corpus of OCR and clean text and “in the wild” using a large real-world corpus. Experiments on the parallel corpus reveals that even with a reasonable degree of noise in the dataset, it is possible to generate ranked lists using the KDE algorithm with a high degree of precision and recall. Furthermore, since the KDE algorithm has comparable performance to state-of-the-art unsupervised rankers, using it on real-world corpora is feasible. The paper concludes by reflecting on other methods for enhancing the performance of the unsupervised algorithm on OCR text such as cleaning entity names, disambiguating names concatenated to one another and correcting OCR errors that are statistically significant in the corpus. Collapse

Topics: information retrieval

Methods: computational algorithm machine learning descriptive statistic experiment word2vec

Dissection of AI Job Advertisements: A Text Mining-based Analysis of Employee Skills in the Disciplines Computer Vision and Natural Language Processing

2022 | HICSS | Citations: 0

Authors: Kortum, Henrik; Rebstadt, Jonas; Thomas, Oliver

Abstract: Human capital is a well discussed topic in information system research. In order ... Expand

Abstract: Human capital is a well discussed topic in information system research. In order for companies to develop and use IT artifacts, they need specialized employees. This is especially the case when complex technologies, such as artificial intelligence, are used. Two major fields of artificial intelligence are computer vision (CV) and natural language processing (NLP). In this paper skills required for CV and NLP specialists are analyzed and compared from a job market perspective. For this purpose, we utilize a text mining-based analysis pipeline to dissect job advertisements for artificial intelligence. In concrete, job advertisements of both sub-disciplines were crawled from a large international online job platform and analyzed using named entity recognition and term vectors. It could be shown that skills required differ between the two job profiles. There is no general requirement profile of an artificial intelligence specialist, and it requires a differentiated consideration. Collapse

Topics: artificial intelligence soft skill online labor market IT skill Microsoft Azure

Methods: natural language processing term frequency–inverse document frequency named entity recognition computational algorithm

Theories: human capital theory

Social Media Event Prediction using DNN with Feedback Mechanism

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ma, Wanlun; Hu, Xiangyu; Chen, Chao; Wen, Sheng; Choo, Kkwang Raymond; Xiang, Yang

Abstract: Online social networks (OSNs) are a rich source of information, and the data (in ... Expand

Abstract: Online social networks (OSNs) are a rich source of information, and the data (including user-generated content) can be mined to facilitate real-world event prediction. However, the dynamic nature of OSNs and the fast-pace nature of social events or hot topics compound the challenge of event prediction. This is a key limitation in many existing approaches. For example, our evaluations of six baseline approaches (i.e., logistic regression latent Dirichlet allocation (LDA)-based logistic regression (LR), multi-task learning (MTL), long short-term memory (LSTM) and convolutional neural networks, and transformer-based model) on three datasets collected as part of this research (two from Twitter and one from a news collection site1), reveal that the accuracy of these approaches is between 50% and 60%, and they are not capable of utilizing new events in event predictions. Hence, in this article, we develop a novel DNN-based framework (hereafter referred to as event prediction with feedback mechanism— EPFM. Specifically, EPFM makes use of a feedback mechanism based on emerging events detection to improve the performance of event prediction. The feedback mechanism ensembles three outlier detection processes and returns a list of new events. Some of the events will then be chosen by analysts to feed into the fine-tuning process to update the predictive model. To evaluate EPFM, we conduct a series of experiments on the same three datasets, whose findings show that EPFM achieves 80% accuracy in event detection and outperforms the six baseline approaches.We also validate EPFM’s capability of detecting new events by empirically analyzing the feedback mechanism under different thresholds. Collapse

Topics: Twitter logistics management social media Linux knowledge base

Methods: BERT machine learning logistic regression experiment DistilBERT

Supporting the Billing Process in Outpatient Medical Care: Automated Medical Coding Through Machine Learning

2022 | European Conference On Information Systems | Citations: 0

Authors: Oberste, Luis; Finze, Nikola; Hoffmann, Philipp; Heinzl, Armin

Abstract: Reimbursement in medical care implies significant administrative effort for med ... Expand

Abstract: Reimbursement in medical care implies significant administrative effort for medical staff. To bill the treatments or services provided, diagnosis and treatment codes must be assigned to patient records using standardized healthcare classification systems, which is a time-consuming and error-prone task. In contrast to ICD diagnosis codes used in most countries for inpatient care reimbursement, outpatient medical care often involves different reimbursement schemes. Following the Action Design Research methodology, we developed an NLP-based machine learning artifact in close collaboration with a general practitioner's office in Germany, leveraging a dataset of over 5,600 patients with more than 63,000 billing codes. For the code prediction of most problematic treatments as well as a complete code prediction task, we achieved F1-scores of 93.60 % and 78.22 %, respectively. Throughout three iterations, we derived five meta requirements leading to three design principles for an automated coding system to support the reimbursement of outpatient medical care. Collapse

Topics: health information system open source innovation management knowledge creation organizational context

Methods: design artifact design principle design requirement action design research natural language processing