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Academic Journal

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.

Authors : Tutsoy O; Adana Alparslan Turkes Science and Technology University, Adana, Turkey. .; Koç GG

Subjects: Algorithms* ; Machine Learning*; Supervised Machine Learning

Source: BMC bioinformatics [BMC Bioinformatics] 2024 Mar 08; Vol. 25 (1), pp. 103. Date of Electronic Publication: 2024 Mar 08.Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105

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Academic Journal

MLpronto: A tool for democratizing machine learning.

Authors : Tjaden J; Computer Science Department, Colby College, Waterville, ME, United States of America.; Tjaden B

Subjects: Algorithms* ; Machine Learning*; Supervised Machine Learning

Source: PloS one [PLoS One] 2023 Nov 30; Vol. 18 (11), pp. e0294924. Date of Electronic Publication: 2023 Nov 30 (Print Publication: 2023).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

Authors : Mo Z; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.; School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.

Subjects: Machine Learning* ; Neural Networks, Computer*; Supervised Machine Learning

Source: PLoS genetics [PLoS Genet] 2023 Nov 07; Vol. 19 (11), pp. e1011032. Date of Electronic Publication: 2023 Nov 07 (Print Publication: 2023).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101239074 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer.

Authors : Wang F; Department of Gynaecology, Xuzhou Medical University Affiliated Hospital of Lianyungang, No. 6, Zhenhua East Road, Lianyungang, 222061, Jiangsu Province, China.; Pang R

Subjects: Machine Learning* ; Endometrial Neoplasms*/Endometrial Neoplasms*/Endometrial Neoplasms*/pathology ; Endometrial Neoplasms*/Endometrial Neoplasms*/Endometrial Neoplasms*/diagnosis

Source: Scientific reports [Sci Rep] 2024 Jun 01; Vol. 14 (1), pp. 12624. Date of Electronic Publication: 2024 Jun 01.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.

Authors : Xu Y; Department of Endocrine Metabolism, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.; Qiu S

Subjects: Machine Learning* ; Muscle Strength* ; Diabetes Mellitus*/Diabetes Mellitus*/Diabetes Mellitus*/diagnosis

Source: Journal of diabetes investigation [J Diabetes Investig] 2024 Jun; Vol. 15 (6), pp. 743-750. Date of Electronic Publication: 2024 Mar 04.Publisher: Asian Association for the Study of Diabetes and Blackwell Pub. Asia Country of Publication: Japan NLM ID: 101520702 Publication Model:

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Academic Journal

Developing a Machine-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Patients Undergoing Total Knee Arthroplasty.

Authors : Chen L; Arthritis Clinical and Research Center, Peking University People's Hospital, Peking University, Beijing, China.; Zhang L

Subjects: Arthroplasty, Replacement, Knee*/Arthroplasty, Replacement, Knee*/Arthroplasty, Replacement, Knee*/methods ; Posterior Cruciate Ligament*/Posterior Cruciate Ligament*/Posterior Cruciate Ligament*/surgery ; Machine Learning*

Source: Orthopaedic surgery [Orthop Surg] 2024 Jun; Vol. 16 (6), pp. 1381-1389. Date of Electronic Publication: 2024 May 01.Publisher: Wiley on behalf of the Chinese Orthopedic Association Country of Publication: Australia NLM ID: 101501666 Publication Model: Print-Electronic

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Academic Journal

Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.

Authors : Chen M; Department of Neurosurgery, Deyang People's Hospital, Deyang, 618000, China.; Li Y

Subjects: Pituitary Neoplasms*/Pituitary Neoplasms*/Pituitary Neoplasms*/surgery ; Pituitary Neoplasms*/Pituitary Neoplasms*/Pituitary Neoplasms*/complications ; Olfaction Disorders*/Olfaction Disorders*/Olfaction Disorders*/etiology

Source: Scientific reports [Sci Rep] 2024 May 31; Vol. 14 (1), pp. 12514. Date of Electronic Publication: 2024 May 31.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

Authors : Li X; Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.; Wang P

Subjects: Acute Kidney Injury*/Acute Kidney Injury*/Acute Kidney Injury*/diagnosis ; Machine Learning* ; Critical Illness*

Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 May 31; Vol. 24 (1), pp. 148. Date of Electronic Publication: 2024 May 31.Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947

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Academic Journal

Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.

Authors : Noda R; Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan. .; Ichikawa D

Subjects: Glomerulonephritis, IGA*/Glomerulonephritis, IGA*/Glomerulonephritis, IGA*/diagnosis ; Glomerulonephritis, IGA*/Glomerulonephritis, IGA*/Glomerulonephritis, IGA*/urine ; Glomerulonephritis, IGA*/Glomerulonephritis, IGA*/Glomerulonephritis, IGA*/pathology

Source: Scientific reports [Sci Rep] 2024 May 30; Vol. 14 (1), pp. 12426. Date of Electronic Publication: 2024 May 30.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.

Authors : Liu L; Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, China.; Zhang R

Subjects: Gastrointestinal Stromal Tumors*/Gastrointestinal Stromal Tumors*/Gastrointestinal Stromal Tumors*/pathology ; Liver Neoplasms*/Liver Neoplasms*/Liver Neoplasms*/secondary ; Machine Learning*

Source: Scientific reports [Sci Rep] 2024 May 30; Vol. 14 (1), pp. 12415. Date of Electronic Publication: 2024 May 30.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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نتائج البحث

فلترة النتائج

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.

MLpronto: A tool for democratizing machine learning.

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer.

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.

Developing a Machine-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Patients Undergoing Total Knee Arthroplasty.

Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.