研究活動情報

• Development of a Type 2 Diabetes Prediction Model Using Specific Health Checkup Data and Extraction of Predictive Factors
  Kenichiro Shimai; Kazuki Ohashi; Teppei Suzuki; Ryota Konno; Ryuichiro Ueda; Masami Mukai; Katsuhiko Ogasawara
  Bioengineering, 13, 2, 194, 194, MDPI AG, 2026年02月09日, ［査読有り］
  英語, 研究論文（学術雑誌）, Background: Specific health checkups in Japan aim to prevent and detect non-communicable diseases (NCDs). Lifestyle information and non-invasive measurements obtained during these checkups are valuable for population health monitoring. This study aimed to develop a predictive model for type 2 diabetes mellitus (T2DM) using only non-invasive measurements and to identify key predictors. Methods: A retrospective observational study was conducted using linked health checkup records and medical claims from a city in Japan. Logistic regression was performed to predict a T2DM diagnosis. Results: A total of 409 of the 1363 participants were diagnosed with T2DM, including 285 of the 950 participants aged 40–74 years and 124 of the 413 participants aged ≥75 years. The model achieved an area under the receiver operating characteristic curve of 0.680 for those aged 40–74 years and 0.665 for those aged ≥75 years, indicating moderate discrimination. Key predictors included male sex, use of antihypertensive drugs, walking speed, and eating habits within 2 h before bedtime. In particular, male sex, having a slower walking speed, and not eating within 2 h before bedtime were positively associated with T2DM diagnosis. Conversely, the absence of antihypertensive or lipid-lowering medications was negatively associated with T2DM diagnosis. Conclusion: A model based solely on non-invasive measurements moderately identified individuals at risk for T2DM in this community-based Japanese population. Routinely collected health checkup data may support early identification and targeted preventive strategies.
• Identifying Terminologies Used Prior to the Onset of Interstitial Lung Disease in Patients With Lung Cancer: Descriptive Analysis of Electronic Medical Record Data
  Masami Mukai; Hiroki Adachi; Tomohiro Yamaguchi; Ryunosuke Tanabe; Yasuo Sugitani; Yoshimasa Hanada; Noriaki Nakajima; Naoki Mihara
  JMIR Cancer, 11, e70603, e70603, JMIR Publications Inc., 2025年11月03日, ［査読有り］, ［筆頭著者］
  英語, 研究論文（学術雑誌）, Abstract
  
  Background
  
  The growing importance of real-world data (RWD) as a source of evidence for drug effects has led to increased interest in clinical research utilizing secondary use data from electronic medical record systems. Although immune checkpoint inhibitors and targeted therapies have advanced lung cancer treatment, managing complications such as interstitial lung disease (ILD) remains challenging. Early detection and prevention of ILD are crucial for improving patient prognosis and quality of life; however, predictive biomarkers have yet to be established. Therefore, methods to identify ILD risk factors and enable early detection using RWD are needed.
  
  Objective
  
  This exploratory study aimed to identify associated factors and prodromal symptoms of ILD onset using clinical data stored in a hospital information system.
  
  Methods
  
  Clinical data of patients diagnosed with stage IV lung cancer between November 2011 and December 2018 were extracted from the hospital information system of the National Cancer Center Hospital in Japan. A total of 3 patient groups were defined: the ILD Set, based on laboratory test results and radiological records; the ILD-GC Set, which added glucocorticoid treatment to the ILD Set; and the No ILD Set, for patients without ILD. The primary endpoint was the frequency of Japanese words extracted from electronic medical records, specifically from notes in the Problem-Oriented System/Subjective, Objective, Assessment and Plan format. Noun frequencies were compared between the ILD or ILD-GC Sets and the No ILD Set. Free-text data were processed using morphological analysis, and terms were categorized using the Patient Disease Expression Dictionary or the World Health Organization Drug Dictionary. Key terms were extracted from physician and nurse records based on the descending order of ranking differences to identify associated factors and prodromal symptoms.
  
  Results
  
  The analysis included 674 cases (105 in the ILD Set [including 12 in the ILD-GC Set] and 569 in the No ILD Set). Baseline characteristics showed no apparent differences across groups. In the 30 days prior to ILD onset, notable differences in word frequencies per 1000 notes between the ILD-GC Set and No ILD Set were observed in the following term categories: respiratory symptoms (eg, breathlessness, shortness of breath, oxygen), ranging from 170.59 to 46.51; pain or analgesics (eg, Lyrica [pregabalin], soreness, precordial pain, opioids), ranging from 462.88 to 45.16; and appetite-related terms (eg, inappetence, food intake, queasiness, Novamin [prochlorperazine]), ranging from 102.23 to 51.90.
  
  Conclusions
  
  Terms related to respiratory symptoms, pain or analgesics, and appetite were identified as associated factors for ILD onset in patients with stage IV lung cancer using RWD from acute care institutions for malignant tumors. These findings may support the early detection of ILD and underscore the potential of RWD to generate real-world evidence that informs drug discovery and pharmaceutical development.
• Prediction recurrence in stage I epidermal growth factor receptor-mutated non-small cell lung cancer using multi-modal data
  Akiko Tateishi; Hidehito Horinouchi; Nobuji Kouno; Katsuji Takeda; Ken Takasawa; Takaaki Mizuno; Yu Okubo; Yukihiro Yoshida; Mototaka Miyake; Masahiko Kusumoto; Koji Inaba; Hiroshi Igaki; Yasushi Yatabe; Masami Mukai; Naoki Mihara; Jo Nishino; Aya Kuchiba; Taro Shibata; Kouya Shiraishi; Shun-ichi Watanabe; Masaaki Komatsu; Takashi Kohno; Yuichiro Ohe; Ryuji Hamamoto
  Lung Cancer, 207, 108727, 108727, Elsevier BV, 2025年09月, ［査読有り］
  英語, 研究論文（学術雑誌）
• Development of Motion Detection Algorithm Using 3d Sensors for Patient Monitoring Support Service System
  Masami Mukai; Yukihiro Yoshida; Masaya Yotsukura; Mieko Machida; Miyuki Kanemitsu; Yoshiaki Miura; Katsuya Nagase; Yota Ozeki; Tomohisa Saito; Masato Kataoka; Shun-Ichi Watanabe
  Studies in Health Technology and Informatics, IOS Press, 2025年08月07日, ［査読有り］, ［筆頭著者］
  英語, 研究論文（国際会議プロシーディングス）, With the aging of the overall patient population, the incidence of patients developing delirium during hospitalization is increasing. This study aims to improve post-operative safety management and reduce the workload of nurses related to patient care. We have developed a monitoring system that uses 3D sensors to detect specific behaviors and motions that require attention in cases where patients exhibit abnormal behaviors, such as falls and self-removal of IV lines, and trigger alerts. In this paper, we present an algorithm for detecting dangerous motions. We use the point cloud data generated by the 3D sensors that collect 3D information. We analyze the motions of subjects based on changes in the point cloud data and tag specific human body motions and behaviors. When there are no obstacles in the imaging direction of the 3D sensors, we detect human body movements (supine position on the bed, half sitting up, and separated from the bed) with an F-measure of 98.33% and motions (thrashing limbs, touching mouth/neck/arms, no action) with an F-measure of 98.23%. We detect the basic motions that trigger alert notifications. However, the detection accuracy decreases depending on the imaging conditions and subject movements. We use invisible and safe near-infrared light for motion detection and recognition to perform imaging even after lights are turned off, without disturbing patients’ sleep. Motion recognition using point cloud data is a privacy-friendly monitoring method with a low risk of acquiring personally identifiable information. In the future, we plan to verify the algorithm using actual patients and investigate the detection of motions in addition to those considered in this study.
• A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records
  Toshiki Takeuchi; Hidehito Horinouchi; Ken Takasawa; Masami Mukai; Ken Masuda; Yuki Shinno; Yusuke Okuma; Tatsuya Yoshida; Yasushi Goto; Noboru Yamamoto; Yuichiro Ohe; Mototaka Miyake; Hirokazu Watanabe; Masahiko Kusumoto; Takashi Aoki; Kunihiro Nishimura; Ryuji Hamamoto
  BMC Medical Informatics and Decision Making, 25, 1, Springer Science and Business Media LLC, 2025年02月17日, ［査読有り］
  英語, 研究論文（学術雑誌）
• Chronological evolution in liver resection for hepatocellular carcinoma: Prognostic trends across three decades in early to advanced stages
  Takeshi Takamoto; Satoshi Nara; Daisuke Ban; Takahiro Mizui; Masami Mukai; Minoru Esaki; Kazuaki Shimada
  European Journal of Surgical Oncology, 51, 2, 109461, 109461, Elsevier BV, 2025年02月, ［査読有り］
  英語, 研究論文（学術雑誌）

• Evaluating the Applicability of HL7 mCODE in European Oncology Registries: A Mapping Study with SPECTA
  M. Mukai; T. Mizuno; M. Seki; K. Mizuno; N. Yamamoto; S. Yoshimoto, ECR2026, 2026年03月, ［査読有り］, ［筆頭著者］
  英語, 記事・総説・解説・論説等（国際会議プロシーディングズ）
• がん領域における医療機関間連携を目的とした HL7 FHIR mCODEを用いた ゲートウェイ機能の構築
  向井 まさみ; 中山 恭明; 水野 孝昭; 望月 秀昭; 吉本 世一, 第45回医療情報学連合大会/第26回日本医療情報学会学術大会, 2025年11月, ［査読有り］, ［筆頭著者］
  日本語, 研究発表ペーパー・要旨（全国大会，その他学術会議）
• Development of a Motion Detection Algorithm Using 3D Sensors for the Purpose of Building a Patient Monitoring Support Service System
  Mukai M.; Yoshida Y.; Yotsukura M.; Machida M.; Kanemitsu M.; Miura Y.; Nagase K.; Ozeki Y.; Saito T.; Kataoka M.; Watanabe SI, medinfo2025, 2025年08月, ［査読有り］, ［筆頭著者］
  英語, 研究発表ペーパー・要旨（国際会議）

• 医療情報第８版医療情報システム編
  日本医療情報学会医療情報技師育成部会, 第3章9.7項「画像診断・検査部門業務に関するシステム」, 第4章4項「仕様書の構成と書き方」, 第5章5項「医学研究のための情報システム」, 第7章4項「医療情報システムの安全管理に関するガイドライン」,
  日本医療情報学会医療情報技師育成部会, 2025年03月, 9784867058251, xvii, 476p, 日本語, ［共著］
• 医療情報第８編医学医療編
  日本医療情報学会医療情報技師育成部会, 第11章1.4項「がん登録」
  日本医療情報学会医療情報技師育成部会,篠原出版新社, 2025年03月, 9784867058237, xvi, 525p, 日本語, ［共著］