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Machine learning-based clinical decision support using laboratory data
HC Çubukçu, Dİ Topcu, S Yenice - Clinical Chemistry and Laboratory …, 2024 - degruyter.com
Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory
medicine and the broader context of healthcare. In this review article, we summarized the …
medicine and the broader context of healthcare. In this review article, we summarized the …
Automated schizophrenia detection model using blood sample scattergram images and local binary pattern
The main goal of this paper is to advance the field of automated Schizophrenia (SZ)
detection methods by presenting a pioneering feature engineering technique that achieves …
detection methods by presenting a pioneering feature engineering technique that achieves …
A convolutional neural network‐based, quantitative complete blood count scattergram‐map** framework promptly screens acute promyelocytic leukemia with high …
H Liao, Y Xu, Q Meng, Z Mao, Y Qiao, Y Liu, Q Zheng - Cancer, 2023 - Wiley Online Library
Background Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia
(AML) characterized by its rapidly progressive and fatal clinical course if untreated, although …
(AML) characterized by its rapidly progressive and fatal clinical course if untreated, although …
A physician-in-the-loop approach by means of machine learning for the diagnosis of lymphocytosis in the clinical laboratory
L Bigorra, I Larriba… - Archives of Pathology & …, 2022 - meridian.allenpress.com
Context.—The goal of the lymphocytosis diagnosis approach is its classification into benign
or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that …
or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that …