ACM Transactions on Management Information Systems

Journal: ACM Transactions on Management Information Systems

Abbreviation

ACM trans. manag. inf. syst.

Publisher

Association for Computing Machinery

ISSN

2158-656X
2158-6578

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Publications 1 - 6 of 6

Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Kratzwald, Bernhard; Feuerriegel, Stefan (2019)
ACM Transactions on Management Information Systems
Classification Models for RFID-based Real-Time Detection of Process Events in the Supply Chain
Keller, Thorben; Thiesse, Frédéric; Fleisch, Elgar (2014)
ACM Transactions on Management Information Systems
Eliciting a Sense of Virtual Community among Knowledge Contributors
Sutanto, Juliana; Kankanhalli, Atreyi; Tan, Bernard Cheng Yian (2011)
ACM Transactions on Management Information Systems
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling
Yang, Jing; Eickhoff, Carsten (2018)
ACM Transactions on Management Information Systems
Explaining U.S. Consumer Behavior with News Sentiment
Uhl, Matthias W. (2011)
ACM Transactions on Management Information Systems
MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs
van der Linden, Sanne; Sevastjanova, Rita; Funk, Mathias; et al. (2023)
ACM Transactions on Management Information Systems
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.

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Journal: ACM Transactions on Management Information Systems

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