Tokushima University / Educator and Researcher Directory --- Wakata, Yoshifumi

Field of Study

○	Medical Informatics

Subject of Study

○	medical process analysis and acquirement of knowledge from clinical information data

Book / Paper

Academic Paper (Judged Full Paper):

1.	Ken-ichi Suga, Yoshifumi Wakata, Manami Suzuki, Shunsuke Takeuchi, Masashi Suzue, Ryuji Nakagawa and Maki Urushihara : Neonatal hyperkalemia associated with maternal ritodrine: A retrospective study, Pediatrics International, Vol.65, No.1, e15604, 2023. (Summary) We recently reported on a late preterm infant born at 36 weeks' gestation with serious arrhythmia due to hyperkalemia associated with long-term maternal ritodrine administration. It is unknown whether ritodrine alone increases the risk of neonatal hyperkalemia in infants born at 34-36 weeks' gestation. This single-center, retrospective, cohort study enrolled late preterm infants (34-36 gestational weeks) born between 2004 and 2018. Cases with maternal magnesium sulfate use were not sufficient for statistical analysis and so were excluded from the study. Risk factors for the occurrence of hyperkalemia were determined based on clinical relevance and previous reports. In all, 212 late preterm infants with maternal ritodrine use and 400 infants without tocolysis were included in the study. Neonatal hyperkalemia occurred in 5.7% (12/212) in the ritodrine group and 1.8% (7/400) in the control group. The risk of neonatal hyperkalemia was significantly increased by maternal ritodrine administration with a crude odds ratio (OR) of 3.37 (95% confidence interval [CI]: 1.30-8.69; p < 0.01) and an adjusted OR of 3.71 (95% CI: 1.41-9.74; p < 0.01) on multivariable analysis. Long-term tocolysis (≥28 days) with ritodrine increased the risk of neonatal hyperkalemia with 9.3% (11/118) of infants developing hyperkalemia (adjusted OR 4.86; 95% CI: 1.59-14.83; p < 0.01). Neonatal hyperkalemia was not found within 2 weeks of ritodrine administration. This research suggests that late preterm infants born after long-term ritodrine administration are at risk of neonatal hyperkalemia and require special attention. (Keyword) Pregnancy / infant / female / Infant, Newborn / Humans / Ritodrine / Obstetric Labor, Premature / Retrospective Studies / Cohort Studies / Hyperkalemia / Infant, Premature (Link to Publication Site) ● Publication site (DOI): 10.1111/ped.15604 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 37551666 ● Summary page in Scopus @ Elsevier: 2-s2.0-85166784295 (DOI: 10.1111/ped.15604, PubMed: 37551666, Elsevier: Scopus)
2.	Masato Tagi, Mari Tajiri, Yasuhiro Hamada, Yoshifumi Wakata, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki and Jun Hirose : Accuracy of an Artificial Intelligence-Based Model for Estimating Leftover Liquid Food in Hospitals: Validation Study., JMIR Formative Research, Vol.6, No.5, e35991, 2022. (Summary) An accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable. The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation. The accuracy of the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. The mean absolute error, root mean squared error, and coefficient of determination (R) were used as metrics for determining the accuracy of the evaluation process. Welch t test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation. The mean absolute errors obtained through the AI estimation approach were 0.63 for fermented milk, 0.25 for peach juice, and 0.85 for the total. These were significantly smaller than those obtained using the visual estimation approach, which were 1.40 (P<.001) for fermented milk, 0.90 (P<.001) for peach juice, and 1.03 (P=.009) for the total. By contrast, the mean absolute error for thin rice gruel obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for thin rice gruel showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers. The mean squared error for all liquid foods tended to be smaller for the AI estimation than for the visual estimation. Additionally, the coefficient of determination (R) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R value for the total was equal in terms of accuracy between the AI and visual estimations. The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R) than the visual estimation approach for the side dishes. Additionally, the AI estimation approach achieved a smaller mean absolute error and root mean squared error compared to the visual estimation method, and the coefficient of determination (R) was similar to that of the visual estimation method for the total. AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement. (Tokushima University Institutional Repository) ● Metadata: 117555 (Link to Publication Site) ● Publication site (DOI): 10.2196/35991 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 35536638 ● Search Scopus @ Elsevier (PMID): 35536638 ● Search Scopus @ Elsevier (DOI): 10.2196/35991 (Tokushima University Institutional Repository: 117555, DOI: 10.2196/35991, PubMed: 35536638)
3.	Kotaro Matsumoto, Yasunobu Nohara, Yoshifumi Wakata, Takanori Yamashita and Yukio Kozuma : Impact of a learning health system on acute care and medical complications after intracerebral hemorrhage, Learning Health Systems, 2020. (Keyword) learning health system / oral care / pneumonia / stroke (Tokushima University Institutional Repository) ● Metadata: 115850 (Link to Publication Site) ● Publication site (DOI): 10.1002/lrh2.10223 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081204523 (Tokushima University Institutional Repository: 115850, DOI: 10.1002/lrh2.10223, Elsevier: Scopus)
4.	Jun Hirose, Yoshifumi Wakata, Masato Tagi and Yuu Tamaki : The role of medical informatics in the management of medical information, The Journal of Medical Investigation : JMI, Vol.67, No.1,2, 27-29, 2020. (Summary) With progress in information and communication technology, medical information has been converted to digital formats and stored and managed using computer systems. The construction, management, and operation of medical information systems and regional medical liaison systems are the main components of the clinical tasks of medical informatics departments. Research using medical information accumulated in these systems is also a task for medical informatics department. Recently, medical real-world data (RWD) accumulated in medical information systems has become a focus not only for primary use but also for secondary uses of medical information. However, there are many problems, such as standardization, collection, cleaning, and analysis of them. The internet of things and artificial intelligence are also being applied in the collection and analysis of RWD and in resolving the above problems. Using these new technologies, progress in medical care and clinical research is about to enter a new era. J. Med. Invest. 67 : 27-29, February, 2020. (Tokushima University Institutional Repository) ● Metadata: 114749 (Link to Publication Site) ● Publication site (DOI): 10.2152/jmi.67.27 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 32378614 ● Summary page in Scopus @ Elsevier: 2-s2.0-85084327048 (Tokushima University Institutional Repository: 114749, DOI: 10.2152/jmi.67.27, PubMed: 32378614, Elsevier: Scopus)
5.	Atsushi Kimura, Yoshihiro Matsumoto, Yoshifumi Wakata, Akiko Oyamada, Masanobu Ohishi, Toshifumi Fujiwara, Ko Ikuta, Kuniyoshi Tsuchiya, Naohisa Tayama, Shinji Tomari, Hisaaki Miyahara, Takao Mae, Toshihiko Hara, Taichi Saito, Takeshi Arizono, Kozo Kaji, Taro Mawatari, Masami Fujiwara, Riku Sakimura, Kunichika Shin, Kenichi Ninomiya, Kazutoshi Nakaie, Yasuaki Antoku, Shoji Tokunaga, Naoki Nakashima, Yukihide Iwamoto and Yasuharu Nakashima : Predictive factors of mortality of patients with fragility hip fractures at 1 year after discharge: A multicenter, retrospective study in the northern Kyushu district of Japan., Journal of Orthopaedic Surgery (Hong Kong), Vol.27, No.3, 2019. (Summary) , TCCI 5, smoking history, LOS <14 days, and BI <30 were those for females. Decreased BI is one of the independent and preventable risk factors. A comprehensive therapeutic approach should be considered to prevent deterioration of activities of daily living and a higher risk of mortality. (Tokushima University Institutional Repository) ● Metadata: 116197 (Link to Publication Site) ● Publication site (DOI): 10.1177/2309499019866965 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31466509 ● Search Scopus @ Elsevier (PMID): 31466509 ● Search Scopus @ Elsevier (DOI): 10.1177/2309499019866965 (Tokushima University Institutional Repository: 116197, DOI: 10.1177/2309499019866965, PubMed: 31466509)
6.	Hiroaki Kurata, Masayuki Ochiai, Hirosuke Inoue, Takeshi Kusuda, Junko Fujiyoshi, Masako Ichiyama, Yoshifumi Wakata and Hidetoshi Takada : Inflammation in the neonatal period and intrauterine growth restriction aggravate bronchopulmonary dysplasia., Pediatrics and Neonatology, Vol.60, No.5, 496-503, 2019. (Summary) The aggravation of BPD was associated with both SGA at birth and inflammation during neonatal period. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.pedneo.2018.11.007 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31302038 ● Search Scopus @ Elsevier (PMID): 31302038 ● Search Scopus @ Elsevier (DOI): 10.1016/j.pedneo.2018.11.007 (DOI: 10.1016/j.pedneo.2018.11.007, PubMed: 31302038)
7.	Rieko Izukura, Tadashi Kandabashi, Yoshifumi Wakata, Chinatsu Nojiri, Yasunobu Nohara, Takanori Yamashita, Atsushi Takada, Jinsang Park, Yoshiaki Uyama and Naoki Nakashima : The Development of an Electronic Phenotyping Algorithm for Identifying Rhabdomyolysis Patients in the MID-NET Database., Studies in Health Technology and Informatics, Vol.264, 1498-1499, 2019. (Summary) We aimed to develop rhabdomyolysis (RB) phenotyping algorithms using machine learning techniques and to create subphenotyping algorithms to identify RB patients who lack RB diagnosis. Two pattern algorithms, one with a focus on improving predictive value and one focused on improving sensitivity, were finally created and had a high area under the curve value of 0.846. Although we were unable to create subphenotyping algorithms, an attempt to detect unknown RB patients is important for epidemiological studies. (Keyword) Algorithms / Databases, Factual / Electronic Health Records / Humans / Machine Learning / Rhabdomyolysis (Link to Publication Site) ● Publication site (DOI): 10.3233/SHTI190503 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31438200 ● Search Scopus @ Elsevier (PMID): 31438200 ● Search Scopus @ Elsevier (DOI): 10.3233/SHTI190503 (DOI: 10.3233/SHTI190503, PubMed: 31438200)

Proceeding of International Conference:

1.	Takanori Yamashita, Yoshifumi Wakata, Hideki Nakaguma, Yasunobu Nohara and Shinji Hato : Machine Learning for Classification of Postoperative Patient Status Using Standardized Medical Data, APAMI 2020, Nov. 2020.

Proceeding of Domestic Conference:

1.	Masato Tagi, 田尻真梨, Yasuhiro Hamada, Yoshifumi Wakata, Xiao Shan, Kazumi Ozaki, 久保田雅則, 天野宗佑 and Jun Hirose : Clinical validation of an AI-based system for estimating intake hospital liquid food, 第41回医療情報学連合大会, Nov. 2021.
2.	友成健, Nori Sato, 大野亜里沙, 尾形美子, Tomohiro Goto, Yoshifumi Wakata, Takeshi Iwasa and Koichi Sairyo : 理学療法士による人工股関節全置換術後のアウトカム評価の有効性, 日本クリニカルパス学会誌, Vol.23, No.2, 259, Oct. 2021. (Keyword) アウトカム評価(保健医療) クリティカルパス股関節置換術(リハビリテーション) 理学療法士ヒト
3.	Masato Tagi, 田尻真梨, Yasuhiro Hamada, Yoshifumi Wakata, Xiao Shan, Kazumi Ozaki, 久保田雅則, 天野宗佑 and Jun Hirose : Validation of an AI-based model for estimating leftover liquid food in hospitals, 日本医療情報学会春季学術大会, Vol.25, 70-71, Jun. 2021.
4.	山下貴範, 中井真也, 木下喬, Yoshifumi Wakata and 野原康伸 : AMED ePath事業のデータを用いた2型糖尿病患者における患者アウトカム解析, 第5回日本糖尿病・生活習慣病ヒューマンデータ学会年次学術集会, Dec. 2020.
5.	Yoshifumi Wakata : ePath事業及び解析基盤の概要, 第40回医療情報学連合大会, Nov. 2020.
6.	Yoshifumi Wakata : 医療情報学に基づく診療プロセス解析基盤の構築, 第34回人工知能学会全国大会, Jun. 2020.
7.	青木美和, 中島直樹, 岡田美保子, 中熊英貴, 山下貴範, Yoshifumi Wakata, 小妻幸男 and 他 : アウトカム志向型パスの標準的データ取得のための電子仕様とデータ構造モデル化, 第24回日本医療情報学会春季学術大会, Jun. 2020.
8.	山下貴範, Yoshifumi Wakata, 中熊英貴, 野原康伸 and 他 : 患者状態把握を目的とした機械学習と共起有向グラフによる診療プロセス解析, 第24回日本医療情報学会春季学術大会, Jun. 2020.
9.	Yoshifumi Wakata : 統合解析基盤の構築とその可能性, 第20回日本クリニカルパス学会学術集会, Jan. 2020.
10.	Yoshifumi Wakata : クリニカルパスシステム及びデータの標準化とデータ利活用, 第20回日本クリニカルパス学会学術集会, Jan. 2020.
11.	Yoshifumi Wakata : クリニカルパスシステム及びデータの標準化とデータ利活用, 第39回医療情報学連合大会, Nov. 2019.
12.	Masato Tagi, Yuu Tamaki, Yoshifumi Wakata and Jun Hirose : NetFlowデータを活用した院内通信の可視化と自動解析アラート機能検証によるセキュリティー対策評価, 第39回医療情報学連合大会, Nov. 2019.
13.	松本晃太郎, 野原康伸, Yoshifumi Wakata and 山下貴範他 : クリニカルパスを用いたLearning Health Systemの効果, 第23回日本医療情報学会春季学術大会, Jun. 2019.
14.	高田敦史, Yoshifumi Wakata and 山下貴範他 : 多施設共同臨床観察研究の包括的支援事業の課題に関する研究, 第38回医療情報学連合大会, Nov. 2018.
15.	Yoshifumi Wakata : クリニカルパスデータ標準化と標準データモデル∼解析基盤を中心に∼, 第38回医療情報学連合大会, Nov. 2018.
16.	北村佳代子, 川畑恵理子, 山下貴範, Yoshifumi Wakata and 中島直樹 : バリアンス分析に基づく大腸切除術パスのタスク改定, 第19回日本クリニカルパス学会学術集会, Oct. 2018.
17.	Yoshifumi Wakata : クリニカルパスデータ標準化と標準データモデル, 第19回日本クリニカルパス大会学術集会, Oct. 2018.
18.	Yoshifumi Wakata : クリニカルパスデータの標準化 ~基本アウトカムマスター(BOM)と標準データモデル~, 第19回日本医療情報学会看護学術大会, Jul. 2018.
19.	松本晃太郎, 野原康伸, Yoshifumi Wakata and 山下貴範他 : クリニカルパスを用いたLearning Health Systemの構築, 第22回日本医療情報学会春季学術大会, Jun. 2018.
20.	伊豆倉理江子, 野原康伸, Yoshifumi Wakata and 山下貴範他 : 病院情報システムの構造化データから病態を抽出するPhenotyping手法の開発, 第22回日本医療情報学会春季学術大会, Jun. 2018.

Et cetera, Workshop:

1.	Masato Tagi, Jun Hirose, Yoshifumi Wakata and Xiao Shan : Visualization of the hospital network flow and the security alert function by automatic analysis of NetFlow data : Evaluation of the usefulness of security system, 月刊新医療, Vol.47, No.11, 38-41, Nov. 2020. (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1523669555798364544 (CiNii: 1523669555798364544)
2.	Jun Hirose, Yoshifumi Wakata, Masato Tagi and Xiao Shan : With Corona時代の医療情報担当部門の医学部卒前教育・業務支援のありかた∼徳島大学の取組み∼, Sep. 2020.

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

Search by Researcher Number (90719064)

Professor : Wakata, Yoshifumi

Research

Field of Study

Subject of Study

Book / Paper

Academic Paper (Judged Full Paper):

Proceeding of International Conference:

Proceeding of Domestic Conference:

Et cetera, Workshop:

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)