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 …

Automated schizophrenia detection model using blood sample scattergram images and local binary pattern

B Tasci, G Tasci, H Ayyildiz, AP Kamath… - Multimedia Tools and …, 2024 - Springer
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 …

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 …

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 …