Evidence map›Paper›PMID 37798639›Full record

ArticleBMC medical informatics and decision making2023

Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning.

Nanae Tanemura, Tsuyoshi Sasaki, Ryotaro Miyamoto, Jin Watanabe, Michihiro Araki, Junko Sato, Tsuyoshi Chiba

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Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Nanae TanemuraNational Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senriokashinmachi, Settsu, Osaka, 566-0002, Japan. n-tanemura@nibiohn.go.jp.
Tsuyoshi SasakiDepartment of Child Psychiatry and Psychiatry, Chiba University Hospital, Chiba, Japan.
Ryotaro MiyamotoKimura Information Technology Co., Ltd, Saga, Japan.
Jin WatanabeKimura Information Technology Co., Ltd, Saga, Japan.
Michihiro ArakiNational Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senriokashinmachi, Settsu, Osaka, 566-0002, Japan.
Junko SatoOffice of International Programs, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan.
Tsuyoshi ChibaNational Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senriokashinmachi, Settsu, Osaka, 566-0002, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven the increasing number of dementia patients worldwide, a new method was developed for machine learning models to identify the 'latent needs' of patients and caregivers to facilitate patient/public involvement in societal decision making.

methodsJapanese transcribed interviews with 53 dementia patients and caregivers were used. A new morpheme selection method using Z-scores was developed to identify trends in describing the latent needs. F-measures with and without the new method were compared using three machine learning models.

resultsThe F-measures with the new method were higher for the support vector machine (SVM) (F-measure of 0.81 with the new method and F-measure of 0.79 without the new method for patients) and Naive Bayes (F-measure of 0.69 with the new method and F-measure of 0.67 without the new method for caregivers and F-measure of 0.75 with the new method and F-measure of 0.73 without the new method for patients).

conclusionA new scheme based on Z-score adaptation for machine learning models was developed to predict the latent needs of dementia patients and their caregivers by extracting data from interviews in Japanese. However, this study alone cannot be used to assign significance to the adaptation of the new method because of no enough size of sample dataset. Such pre-selection with Z-score adaptation from text data in machine learning models should be considered with more modified suitable methods in the near future.

Indexed as

CaregiversDementiaNeeds AssessmentBayes TheoremEast Asian PeopleHealth Services Needs and DemandHumansMachine LearningCaregiversDementia patientsLatent needsMachine learning modelMorphemePatient public involvementZ-scores

Identifiers

PMID37798639
PMCPMC10557300

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.