[HTML][HTML] Clinlabomics: leveraging clinical laboratory data by data mining strategies

X Wen, P Leng, J Wang, G Yang, R Zu, X Jia… - BMC …, 2022 - Springer
The recent global focus on big data in medicine has been associated with the rise of artificial
intelligence (AI) in diagnosis and decision-making following recent advances in computer …

Applications of machine learning in routine laboratory medicine: Current state and future directions

N Rabbani, GYE Kim, CJ Suarez, JH Chen - Clinical biochemistry, 2022 - Elsevier
Abstract Machine learning is able to leverage large amounts of data to infer complex
patterns that are otherwise beyond the capabilities of rule-based systems and human …

Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification

S Kilicarslan, M Celik, Ş Sahin - Biomedical Signal Processing and Control, 2021 - Elsevier
Deep learning algorithms are an important part of disease prediction and diagnosis by
analyzing health data. If not diagnosed and treated early, symptoms of nutritional anemia …

[HTML][HTML] Using artificial intelligence to improve body iron quantification: A scoping review

AJ Nashwan, IM Alkhawaldeh, N Shaheen, I Albalkhi… - Blood Reviews, 2023 - Elsevier
This scoping review explores the potential of artificial intelligence (AI) in enhancing the
screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic …

[HTML][HTML] Classifying anemia types using artificial learning methods

TK Yıldız, N Yurtay, B Öneç - Engineering Science and Technology, an …, 2021 - Elsevier
The most common blood disease worldwide is anemia, defined by the World Health
Organization as a condition in which the red blood cell count or oxygen-carrying capacity is …

Artificial intelligence and machine learning in patient blood management: a scoping review

JM Meier, T Tschoellitsch - Anesthesia & Analgesia, 2022 - journals.lww.com
Abstract Machine learning (ML) and artificial intelligence (AI) are widely used in many
different fields of modern medicine. This narrative review gives, in the first part, a brief …

Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support …

B Çil, H Ayyıldız, T Tuncer - Medical hypotheses, 2020 - Elsevier
Abstract The symptoms of Iron Deficiency Anemia (IDA) and β-thalassemia (β-TT) disease
are similar and the distinction between them is time consuming and costly. There are several …

Using classification techniques for statistical analysis of Anemia

K Meena, DK Tayal, V Gupta, A Fatima - Artificial intelligence in medicine, 2019 - Elsevier
Anemia in children is becoming a worldwide problem owing to the unawareness among
people regarding the disease, its causes and preventive measures. This study develops a …

[HTML][HTML] A computer-assisted system for early mortality risk prediction in patients with traumatic brain injury using artificial intelligence algorithms in emergency room …

KC Tu, TT Eric Nyam, CC Wang, NC Chen, KT Chen… - Brain sciences, 2022 - mdpi.com
Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have
found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to …

Determination of the effect of red blood cell parameters in the discrimination of iron deficiency anemia and beta thalassemia via Neighborhood Component Analysis …

H Ayyıldız, SA Tuncer - Chemometrics and Intelligent Laboratory Systems, 2020 - Elsevier
Differential diagnosis of iron deficiency anemia (IDA) and β-thalassemia is a time-taking and
costly procedure. Complete blood count (CBC) is a quick, inexpensive, and easily …