Deep learning analysis of clinical course of primary nephrotic syndrome : Japan Nephrotic Syndrome Cohort Study (JNSCS)

• Material Type: 記事
• Title: Deep learning analysis of clinical course of primary nephrotic syndrome : Japan Nephrotic Syndrome Cohort Study (JNSCS)
• Author/Editor: Tomonori Kimura; Ryohei Yamamoto; Mitsuaki Yoshino
• Publication, Distribution, etc.: 日本 : Japanese Society of Nephrology
• Publication Date: 2022-08-12
• Publication Date (W3CDTF): 2022-08-12
• Periodical title: Clinical and experimental nephrology : official publication of the Japanese Society of Nephrology
• No. or year of volume/issue: 26(12)
• Volume: 26(12)
• ISSN (Periodical Title): 1437-7799
• ISSN-L (Periodical Title): 1437-7799
• Text Language Code: eng
• DOI: 10.1007/s10157-022-02256-3; https://doi.org/10.1007/s10157-022-02256-3
• Persistent ID (NDL): info:ndljp/pid/12686317; https://dl.ndl.go.jp/pid/12686317
• Collection: 障害者向け資料
• Collection (Materials For Handicapped People:1): テキストデータ
• Collection (particular): 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他; https://dl.ndl.go.jp/collections/B00035
• Acquisition Basis: オンライン資料収集制度
• Date Accepted (W3CDTF): 2023-03-07T17:36:34+09:00
• Date Captured (W3CDTF): 2022-12-20
• Format (IMT): application/pdf
• Access Restrictions: 国立国会図書館内限定公開
• Service for the Digitized Contents Transmission Service: 図書館・個人送信対象外
• Availability of remote photoduplication service: 可
• Periodical Title (URI): https://link.springer.com/article/10.1007/s10157-022-02256-3
• Periodical Title (Persistent ID (NDL)): info:ndljp/pid/12686314; https://dl.ndl.go.jp/pid/12686314
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

• Summary, etc.: コレクション : 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他
• DOI: 10.1007/s10157-022-02256-3; https://doi.org/10.1007/s10157-022-02256-3
• Access Restrictions: インターネット公開
• Related Material (URI): http://id.ndl.go.jp/digimeta/12686317; https://dl.ndl.go.jp/pid/12686317; https://link.springer.com/content/pdf/10.1007/s10157-022-02256-3.pdf; https://link.springer.com/article/10.1007/s10157-022-02256-3/fulltext.html; https://teikyo-u.repo.nii.ac.jp/records/2022224; https://search.jamas.or.jp/link/ui/2024035987
• Is Referenced By: Profiling of kidney involvement in systemic lupus erythematosus by deep learning using the National Database of Designated Incurable Diseases of Japan; https://cir.nii.ac.jp/crid/1360864737224427392; Demographics and treatment of patients with primary nephrotic syndrome in Japan using a national registry of clinical personal records; https://cir.nii.ac.jp/crid/1360865814752401536
• References: Premature mortality due to nephrotic syndrome and the trend in nephrotic syndrome mortality in Japan, 1995–2014; https://cir.nii.ac.jp/crid/1050282677513599104; Long-term outcome in Japanese patients with lupus nephritis; https://cir.nii.ac.jp/crid/1360004236679396864; Time Series Classification using Deep Learning for Process Planning: A Case from the Process Industry; https://cir.nii.ac.jp/crid/1360013170775997440; Multitask learning and benchmarking with clinical time series data; https://cir.nii.ac.jp/crid/1360579818420087808; Biopsy-Free Prediction of Pathologic Type of Primary Nephrotic Syndrome Using a Machine Learning Algorithm; https://cir.nii.ac.jp/crid/1360580234812968064; Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders; https://cir.nii.ac.jp/crid/1360580235835453440; A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks; https://cir.nii.ac.jp/crid/1360580237732589312; Outlier Detection for Time Series with Recurrent Autoencoder Ensembles; https://cir.nii.ac.jp/crid/1360580240162714496; Interventions for preventing infection in nephrotic syndrome; https://cir.nii.ac.jp/crid/1360861711149152768; Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention; https://cir.nii.ac.jp/crid/1360861715156566784; Chiral amino acid metabolomics for novel biomarker screening in the prognosis of chronic kidney disease; https://cir.nii.ac.jp/crid/1361418519873873152; Kidney disease and risk of venous thromboembolism: a nationwide population‐based case‐control study; https://cir.nii.ac.jp/crid/1361699996431973504; The incidence of primary glomerulonephritis worldwide: a systematic review of the literature; https://cir.nii.ac.jp/crid/1361981471477906304; Dyslipidaemia in nephrotic syndrome: mechanisms and treatment; https://cir.nii.ac.jp/crid/1362544421269929728; Pathologic classification of focal segmental glomerulosclerosis: a working proposal; https://cir.nii.ac.jp/crid/1363107368786373376; D-Serine reflects kidney function and diseases; https://cir.nii.ac.jp/crid/1363388844126015232; Prognosis and risk factors for idiopathic membranous nephropathy with nephrotic syndrome in Japan; https://cir.nii.ac.jp/crid/1363388844478132608; The increased risk of coronary heart disease associated with nephrotic syndrome; https://cir.nii.ac.jp/crid/1364233269032702720; Japan Renal Biopsy Registry : the first nationwide, web-based, and prospective registry system of renal biopsies in Japan; https://cir.nii.ac.jp/crid/2050025942143657856; Evidence-based clinical practice guidelines for nephrotic syndrome 2014; https://cir.nii.ac.jp/crid/2050025942144285952; Regional variations in immunosuppressive therapy in patients with primary nephrotic syndrome : the Japan nephrotic syndrome cohort study; https://cir.nii.ac.jp/crid/2050025942146138496; Renal disease in the elderly and the very elderly Japanese : analysis of the Japan Renal Biopsy Registry (J-RBR); https://cir.nii.ac.jp/crid/2051151842050553600; Intra-body dynamics of D-serine reflects the origin of kidney diseases; https://cir.nii.ac.jp/crid/2051996266988175104
• References (URI): https://cir.nii.ac.jp/crid/1370580229791438467
• Data Provider (Database): 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• Original Data Provider (Database): 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp; Crossref; https://www.crossref.org; 科学研究費助成事業データベース; https://kaken.nii.ac.jp/ja; 学術機関リポジトリデータベース; https://irdb.nii.ac.jp
• Bibliographic ID (NDL): 12686317; http://id.ndl.go.jp/bib/12686317