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Supporting the early detection of disease onset and change using document vector analysis of nursing observation records

Authors: Komaki, Syuotarou;

Supporting the early detection of disease onset and change using document vector analysis of nursing observation records

Abstract

鹿児島大学 博士(医学) Doctor of Philosophy in Medical Science 博士論文全文, 博士論文要旨, 最終試験結果の要旨, 論文審査の要旨 Nursing records are an account of patient condition and treatment during their hospital stay. In this study, we developed a system that can automatically analyze nursing records to predict the occurrence of diseases and incidents (e.g., falls). Text vectorization was performed for nursing records and compared with past case data on aspiration pneumonia, to develop an onset prediction system. Nursing records for a patient group that developed aspiration pneumonia during hospitalization and a non-onset control group were randomly assigned to definitive diagnostic (for learning), preliminary survey, and test datasets. Data from the preliminary survey were used to adjust parameters and influencing factors. The final verification used the test data and revealed the highest compatibility to predict the onset of aspiration pneumonia (sensitivity = 90.9%, specificity = 60.3%) with the parameter values of size = 80 (number of dimensions of the sentence vector), window = 13 (number of words before and after the learned word), and min_count = 2 (threshold of wordcount for word to be included). This method represents the foundation for a discovery/warning system using machine-based automated monitoring to predict the onset of diseases and prevent adverse incidents such as falls. Shotaro Komaki, Fuminori Muranaga, Yumiko Uto, Takashi Iwaanakuchi, Ichiro Kumamoto Supporting the Early Detection of Disease Onset and Change Using Document Vector Analysis of Nursing Observation Records Evaluation & the Health Professions 2021 https://doi.org/10.1177/01632787211014270 doctoral thesis

Country
Japan
Related Organizations
Keywords

Machine Learning, Aspiration Pneumonia, 490, Narrative Medicine, Patient Safety, Natural Language Processing

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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