Medical Natural Language Processing Enhancing Drug Ordering and Coding

Santos, Mariana Casella dos; Montyne, Frank; Dhaen, Christoffel

doi:10.1007/0-387-26559-7_10

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Although there is a large body of work on automatic biomedical information extraction from clinical texts [20] and on dialogue systems for health care [21], [22], work about automatic processing of oral medical prescriptions are rare. In fact, the only product we were able to find was FreePharma™ cited in a chapter in 2006 [23]. It was described as being able to extract medical prescriptions from speech captured from a PDA.…”

Section: Related Workmentioning

confidence: 99%

Towards Spoken Medical Prescription Understanding

Kocabiyikoglu¹,

Portet

Blanchon

et al. 2019

2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

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Prescription Management Systems (PMS) have appeared in health institutions to reduce medication errors which affect several million people worldwide each year. However, practitioners must enter information manually into PMS which decreases the time devoted to care. In this paper, we propose to provide a Natural Language interface to the PMS so that practitioners can record their prescriptions orally through mobile devices at the point of care. We briefly describe the overall approach and focus on the Natural Language Understanding process which was approached through slot-filling. To deal with the paucity of data and the imbalanced class problem, we present a method to artificially generate medical prescriptions. Experiments on the artificial and a realistic dataset with several state-of-the-art NLU systems show that the method makes it possible to learn competitive NLU models and opens the way to experiments on speech corpora.

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Section: Related Workmentioning

confidence: 99%

Towards Spoken Medical Prescription Understanding

Kocabiyikoglu¹,

Portet

Blanchon

et al. 2019

2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

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“…For example, LinKBase [20] is an expansive medical knowledge base that enables cross-mappings from one classification system to another. It is based on the medical natural language processing.…”

Section: Medical Knowledge Acquisitionmentioning

confidence: 99%

Knowledge Extractions, Visualizations, and Inference from the big Data in Healthcare and Medical

Kim¹

2013

Journal of Korean Institute of Intelligent Systems

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The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based onData Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extractslogical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refinesand manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using theDT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, inInference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

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Medical Natural Language Processing Enhancing Drug Ordering and Coding

Cited by 2 publications

References 4 publications

Towards Spoken Medical Prescription Understanding

Towards Spoken Medical Prescription Understanding

Knowledge Extractions, Visualizations, and Inference from the big Data in Healthcare and Medical

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