Natural Language Processing for Medical Texts – A Taxonomy to Inform Integration Decisions into Clinical Practice

2023 | International Conference on Information Systems | Citations: 0

Authors: Braun, Marvin; Kolbe, Lutz; Neumann, Caspar

Abstract: Electronic health records (EHR) have significantly amplified the volume of info ... Expand

Abstract: Electronic health records (EHR) have significantly amplified the volume of information accessible in the healthcare sector. Nevertheless, this information load also translates into elevated workloads for clinicians engaged in extracting and generating patient information. Natural Language Process (NLP) aims to overcome this problem by automatically extracting and structuring relevant information from medical texts. While other methods related to artificial intelligence have been implemented successfully in healthcare (e.g., computer vision in radiology), NLP still lacks commercial success in this domain. The lack of a structured overview of NLP systems is exacerbating the problem, especially with the emergence of new technologies like generative pre-trained transformers. Against this background, this paper presents a taxonomy to inform integration decisions of NLP systems into healthcare IT landscapes. We contribute to a better understanding of how NLP systems can be integrated into daily clinical contexts. In total, we reviewed 29 papers and 36 commercial NLP products. Collapse

Topics: health information system artificial intelligence database system healthcare data information technology infrastructure

Methods: natural language processing computational algorithm information extraction literature sample NLP application

A taxonomy for deriving business insights from user-generated content

2023 | European Conference On Information Systems | Citations: 0

Authors: Weingart, Pablo; Wambsganss, Thiemo; Soellner, Matthias

Abstract: Deriving business insights from user-generated content (UGC) is a widely invest ... Expand

Abstract: Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing natural language processing (NLP) applications in the realm of IS. Collapse

Topics: database system social network online review social media user-generated content

Methods: natural language processing structured literature research qualitative interview deep learning NLP application

Read the News, Not the Books: Forecasting Firms’ Long-term Financial Performance via Deep Text Mining

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

Authors: Zhai, Shuang (Sophie); Zhang, Zhu (Drew)

Abstract: In this paper, we show textual data from firm-related events in news articles ca ... Expand

Abstract: In this paper, we show textual data from firm-related events in news articles can effectively predict various firm financial ratios, with or without historical financial ratios. We exploit state-of-the-art neural architectures, including pseudo-event embeddings, Long Short-Term Memory Networks, and attention mechanisms. Our news-powered deep learning models are shown to outperform standard econometric models operating on precise accounting historical data. We also observe forecasting quality improvement when integrating textual and numerical data streams. In addition, we provide in-depth case studies for model explainability and transparency. Our forecasting models, model attention maps, and firm embeddings benefit various stakeholders with quality predictions and explainable insights. Our proposed models can be applied both when numerically historical data is or is not available. Collapse

Topics: missing data accounting financial performance sensemaking organizational productivity

Methods: deep learning autoregressive integrated moving average longitudinal research time series analysis case study

Unleashing the Potential of Argument Mining for IS Research: A Systematic Review and Research Agenda

2022 | International Conference on Information Systems | Citations: 0

Authors: Weingart, Pablo; Wambsganss, Thiemo; Söllner, Matthias

Abstract: Argument mining (AM) represents the unique use of natural language processing ( ... Expand

Abstract: Argument mining (AM) represents the unique use of natural language processing (NLP) techniques to extract arguments from unstructured data automatically. Despite expanding on commonly used NLP techniques, such as sentiment analysis, AM has hardly been applied in information systems (IS) research yet. Consequentially, knowledge about the potentials for the usage of AM on IS use cases appears to be still limited. First, we introduce AM and its current usage in fields beyond IS. To address this research gap, we conducted a systematic literature review on IS literature to identify IS use cases that can potentially be extended with AM. We develop eleven text-based IS research topics that provide structure and context to the use cases and their AM potentials. Finally, we formulate a novel research agenda to guide both researchers and practitioners to design, compare and evaluate the use of AM for text-based applications and research streams in IS. Collapse

Topics: information system use database system data security IT security electronic commerce

Methods: natural language processing structured literature research literature sample literature study sentiment analysis

Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning

2021 | Decision Support Systems | Citations: 0

Authors: Heinrich, Kai; Zschech, Patrick; Janiesch, Christian; Bonin, Markus

Abstract: Predicting next events in predictive process monitoring enables companies to man ... Expand

Abstract: Predicting next events in predictive process monitoring enables companies to manage and control processes at an early stage and reduce their action distance. In recent years, approaches have steadily moved from classical statistical methods towards the application of deep neural network architectures, which outperform the former and enable analysis without explicit knowledge of the underlying process model. While the focus of prior research was on the long short-term memory network architecture, more deep learning architectures offer promising extensions that have proven useful for other applications of sequential data. In our work, we introduce a gated convolutional neural network and a key-value-predict attention network to the task of next event pre­ diction. In a comprehensive evaluation study on 11 real-life benchmark datasets, we show that these two novel architectures surpass prior work in 34 out of 44 metric-dataset combinations. For our evaluation, we consider the effects of process data properties, such as sparsity, variation, and repetitiveness, and discuss their impact on the prediction quality of the different deep learning architectures. Similarly, we evaluate their classification prop­ erties in terms of generalization and handling class imbalance. Our results provide guidance for researchers and practitioners alike on how to select, validate, and comprehensively benchmark (novel) predictive process monitoring models. In particular, we highlight the importance of sufficiently diverse process data properties in event logs and the comprehensive reporting of multiple performance indicators to achieve meaningful results. Collapse

Topics: source code high-performance computing research and development project management innovation management

Methods: event log literature study machine learning experiment computational algorithm

From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research

2021 | European Journal of Information Systems | Citations: 0

Authors: Li, Yan; Thomas, Manoj a; Liu, Dapeng

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile a ... Expand

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between humans and computers. Although there is a large body of NLP studies published in Information Systems (IS), a comprehensive review of how NLP research is conceptualised and realised in the context of IS has not been conducted. To assess the current state of NLP research in IS, we use a variety of techniques to analyse a literature corpus comprising 356 NLP research articles published in IS journals between 2004 and 2018. Our analysis indicates the need to move from semantics to pragmatics. More importantly, our findings unpack the challenges and assumptions underlying current research trends in NLP. We argue that overcoming these challenges will require a renewed disciplinary IS focus. By proposing a roadmap of NLP research in IS, we draw attention to three NLP research perspectives and present future directions that IS researchers are uniquely positioned to address. Collapse

Topics: database system knowledge representation social media analytics social media information retrieval

Methods: natural language processing qualitative content analysis literature study computational algorithm literature sample

Improving Explainability and Accuracy through Feature Engineering: A Taxonomy of Features in NLP-based Machine Learning

2021 | International Conference on Information Systems | Citations: 0

Authors: Wambsganss, Thiemo; Engel, Christian; Fromm, Hansjörg

Abstract: Natural Language Processing (NLP)-based machine learning receives continuous at ... Expand

Abstract: Natural Language Processing (NLP)-based machine learning receives continuous attention in Information System (IS) research and practice. Despite the success of deep learning models, NLP feature engineering still plays a vital role in contexts where only little annotated data is available, and in which explainability is a precondition for productive deployment. However, NLP feature engineering is a labor-intensive and timeconsuming endeavor, and there is still limited shared knowledge about the distinctive characteristics of NLP features from an interdisciplinary perspective. To address this gap, we draw on a systematic literature review and develop a five-dimensional NLP feature taxonomy based on 133 unique features from 211 scientific studies. This helps IS researchers and practitioners to classify, compare, and evaluate their NLP studies. Moreover, we used cluster heat mapping analysis to derive three clusters and several white spots to provide further assistance for designing new NLP solutions in IS. Collapse

Topics: database system information retrieval deception detection fake news Python

Methods: natural language processing feature engineering structured literature research cluster analysis qualitative interview

Catch them all: Impacts of location-based augmented reality mobile applications on local businesses

2021 | Information & Management | Citations: 0

Authors: Zhang, Yuan; Zhang, Jie

Abstract: Mobile applications implemented with location-based and augmented reality (LBAR) ... Expand

Abstract: Mobile applications implemented with location-based and augmented reality (LBAR) technologies have become a new trend. They alter the app usage patterns, expand their mobility areas, and shape their daily lives. Consequently, local businesses may experience spillover effects in terms of store visits and online reputation. Anecdotal evidence is mixed regarding whether businesses gain from the LBAR apps. There is limited research on how the penetration of LBAR apps impacts local businesses and the potential economic value. To fill the gap in the research literature, we leverage a natural experiment involving the launch of an LBAR mobile app to examine its business impacts on the online reputation of nearby restaurants. We find that, in general, restaurants near the app portals do gain indirect benefits from the entry of the LBAR application in improving their online reputation. This spillover benefit of LBAR applications changes over time, and its internalization depends on the restaurant features, location, and the agglomeration of surrounding businesses. This paper provides useful theoretical and practical insights into the business impacts of LBAR technologies. Collapse

Topics: mobile application organizational impact Google+ augmented reality organizational productivity

Methods: experimental group polynomial regression propensity score method difference in differences regression discontinuity design

Introduction to Data, Text, and Web Mining for Business Analytics Minitrack

2021 | HICSS | Citations: 0

Authors: Delen, Dursun; Zolbanin, Hamed; Davazdahemami, Behrooz

Abstract: This mini-track has a total of six papers that are about developing analytics s ... Expand

Abstract: This mini-track has a total of six papers that are about developing analytics systems for decision support by means of data, text, or web mining. The papers address the gaps from a variety of relevant methodological and practical contexts such as data labeling, anomaly detection, automatic procedure generation, ecosystem intelligence, and causality model extraction. Collapse

Topics: business process management

Methods: NLP application natural language processing deep learning Cross-Industry Standard Process for Data Mining anomaly detection

Harnessing Aspect - Based Sentiment Analysis: How Are Tweets Associated with Forecast Accuracy?

2019 | Journal of the Association for Information Systems | Citations: 0

Authors: Ho, Shuk Ying; Choi, Ka; Yang, Fan (Finn)

Abstract: For firms in the consumer sector of the economy, tweets about service quality r ... Expand

Abstract: For firms in the consumer sector of the economy, tweets about service quality reflect consumer satisfaction, which determines firms' future earnings. Our study responds to anecdotal evidence indicating that analysts have adopted opinion mining to scrutinize Twitter data in order to detect shifts in consumer behavior and make earnings forecasts. If this anecdotal evidence is accurate, certain tweet characteristics may be associated with the accuracy of earnings forecasts for these firms. Our study draws on the literature on consumer satisfaction and firm earnings to identify possible tweet characteristics and hypothesize their associative relationships with analyst forecast accuracy. We use the airline industry as the study context and extract tweets related to airline service quality from publicly available Twitter data and analyst forecast data from the Institutional Brokers' Estimate System Academic. We apply content analysis, followed by aspect-based sentiment analysis, to the downloaded tweets. Using regressions, we find that the breadth of coverage and the number of posters are associated positively with forecast accuracy. The valence of tweets differentiates their effects on forecast accuracy: negative tweets enhance forecast accuracy to a greater extent than do positive tweets. We do not detect any association between tweet subjectivity and analyst forecast accuracy. There is a marginal negative association between tweet dispersion and forecast accuracy. We conclude by discussing theoretical and practical contributions. Collapse

Topics: Twitter service quality social media database system online review

Methods: sentiment analysis aspect-based sentiment analysis regression analysis method qualitative content analysis descriptive statistic

Theories: diffusion of innovations theory lemon market theory