Tetsuo Ishikawa 研究室

主宰者：Tetsuo Ishikawa

東京大学

兼任：慶應義塾大学／理化学研究所・RIKEN Center for Integrative Medical Sciences

AI 要約（直近 5 年の研究成果）

本研究室は、医療・生命現象における複雑で多様なデータを理解し、患者や個人の予後や状態変化を予測することを主要な研究課題としています。脳卒中後の日常生活動作の回復、集中治療室での血糖異常、心不全患者の層別化、周産期における発達特性など、様々な臨床場面でのリスク評価や個人差の同定が対象となります。研究手法として、機械学習やデータ駆動型アプローチを中核に据えています。単純な予測精度だけでなく、なぜそのような予測結果になったのかを医療者が理解できる「説明可能な機械学習」の開発に注力しており、電子カルテデータや大規模レジストリ、さらには顔画像や単一細胞の遺伝子発現データなど多様なデータ形式を扱います。統計学的厳密性と機械学習を組み合わせた新しい分析方法の提案も特徴です。これらの研究を通じて、患者集団の潜在的な異質性を明らかにし、個々人の多様な病態経過に応じた層別化や、より正確で解釈可能な予測モデルの構築を目指しています。同時に、生成型人工知能の信頼性評価や生物学的システムの理論的フレームワーク構築なども進め、医療データ分析の基盤となる方法論の発展に貢献しています。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（30 件）

[2026] Explainable machine learning prediction of functional independence measure scores and gain in subacute stroke survivors
DOI: https://doi.org/10.1186/s12984-026-01963-2
[2026] Predicting coronary artery abnormalities in Kawasaki disease: Model development and validation
DOI: https://doi.org/10.1007/s00431-026-06884-9
[2026] Metacognition of ChatGPT in confidence judgements
DOI: https://doi.org/10.3389/frai.2026.1694192
[2026] Deciphering unmet medical needs in atopic dermatitis through natural language processing and digital biomarkers: A cluster-transition analysis
DOI: https://doi.org/10.1016/j.jaci.2025.12.574
[2026] 2383-P: Machine Learning–Based Clustering to Predict Functional Beta-Cell Depletion in Type 1 Diabetes
DOI: https://doi.org/10.2337/db26-2383-p
[2025] Accurate prediction of hypoglycemia and hyperglycemia using machine learning in critically ill patients
DOI: https://doi.org/10.1038/s41598-025-29860-z
[2025] Graph topology reframes the coherence of cell-state manifold inference under heterogeneous single-cell observations
DOI: https://doi.org/10.34133/csbj.0087
[2025] Richness of emotion in people with dementia
DOI: https://doi.org/10.1016/j.biosystems.2025.105601
[2025] Elucidating synergistic risk factors among comorbidity combinations for nephrotic syndrome based on time series statistical evaluation
DOI: https://doi.org/10.1016/j.dmpk.2025.101170
[2025] Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study
DOI: https://doi.org/10.2196/65585

続きを表示（残り 20 件）

[2025] Rigorous multiple statistical test unveils combinations of preceding diseases at risk for the development of adult nephrotic syndrome
DOI: https://doi.org/10.1016/j.compbiomed.2025.110360
[2025] Multilevel Factors and Indicators of Atypical Neurodevelopment During Early Infancy in Japan: Prospective, Longitudinal, Observational Study
DOI: https://doi.org/10.2196/58337
[2025] Prediction of oral food challenge outcome using machine learning methods
DOI: https://doi.org/10.1016/j.jaci.2024.12.798
[2025] Multifactorial Assessment of Complication Risks in Embolization for Ruptured Cerebral Aneurysm
DOI: https://doi.org/10.5797/jnet.oa.2024-0095
[2025] Design-of-Experiments for Nonlinear, Multivariate Biology: Rethinking Experimental Design through Perturb-seq
DOI: https://doi.org/10.64898/2025.12.28.696309
[2024] Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning
DOI: https://doi.org/10.1111/jdv.19909
[2024] Synthesis of causal and surrogate models by non-equilibrium thermodynamics in biological systems
DOI: https://doi.org/10.1038/s41598-024-51426-8
[2024] Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning
DOI: https://doi.org/10.1111/jdv.19909
[2024] Generative artificial intelligence and non-pharmacological bias: an experimental study on cancer patient sexual health communications
DOI: https://doi.org/10.1136/bmjhci-2023-100924
[2023] Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study
DOI: https://doi.org/10.2196/47372
[2023] Monitoring of blood biochemical markers for periprosthetic joint infection using ensemble machine learning and UMAP embedding
DOI: https://doi.org/10.1007/s00402-023-04898-8
[2023] Machine Learning Approach to Stratify Complex Heterogeneity of Chronic Heart Failure: A Report from the CHART-2 Study
DOI: https://doi.org/10.1002/ehf2.14288
[2023] Machine Learning Approach to Stratify Complex Heterogeneity of Chronic Heart Failure: A Report from the CHART-2 Study
DOI: https://doi.org/10.1002/ehf2.14288
[2023] Monitoring of blood biochemical markers for periprosthetic joint infection using ensemble machine learning and UMAP embedding
DOI: https://doi.org/10.1007/s00402-023-04898-8
[2022] Antenatal screening timeline and cutoff scores of the Edinburgh Postnatal Depression Scale for predicting postpartum depressive symptoms in healthy women: a prospective cohort study
DOI: https://doi.org/10.1186/s12884-022-04740-w
[2022] Development and validation of machine learning prediction model for post-rehabilitation functional outcome after intracerebral hemorrhage
DOI: https://doi.org/10.1016/j.inat.2022.101560
[2022] Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort
DOI: https://doi.org/10.1093/ofid/ofac638
[2022] Antenatal screening timeline and cutoff scores of the Edinburgh Postnatal Depression Scale for predicting postpartum depressive symptoms in healthy women: a prospective cohort study
DOI: https://doi.org/10.1186/s12884-022-04740-w
[2022] Proteogenomic landscape and clinical characterization of GH-producing pituitary adenomas/somatotroph pituitary neuroendocrine tumors
DOI: https://doi.org/10.1038/s42003-022-04272-1
[2022] Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort
DOI: https://doi.org/10.1093/ofid/ofac638

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AI 要約（直近 5 年の研究成果）

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関連研究室(8 件)

研究成果（30 件）

科研費（0 件）

所属学会・役職（0 件）