Journal: ACM Transactions on Management Information Systems
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Abbreviation
ACM trans. manag. inf. syst.
Publisher
Association for Computing Machinery
6 results
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Publications 1 - 6 of 6
- Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain CustomizationItem type: Journal Article
ACM Transactions on Management Information SystemsKratzwald, Bernhard; Feuerriegel, Stefan (2019) - Classification Models for RFID-based Real-Time Detection of Process Events in the Supply ChainItem type: Journal Article
ACM Transactions on Management Information SystemsKeller, Thorben; Thiesse, Frédéric; Fleisch, Elgar (2014) - Eliciting a Sense of Virtual Community among Knowledge ContributorsItem type: Journal Article
ACM Transactions on Management Information SystemsSutanto, Juliana; Kankanhalli, Atreyi; Tan, Bernard Cheng Yian (2011) - Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location ModelingItem type: Journal Article
ACM Transactions on Management Information SystemsYang, Jing; Eickhoff, Carsten (2018) - Explaining U.S. Consumer Behavior with News SentimentItem type: Journal Article
ACM Transactions on Management Information SystemsUhl, Matthias W. (2011) - MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRsItem type: Journal Article
ACM Transactions on Management Information Systemsvan der Linden, Sanne; Sevastjanova, Rita; Funk, Mathias; et al. (2023)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.
Publications 1 - 6 of 6