ArticleBMC medical informatics and decision making2019
ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 62 citations in OpenAlex.
- Comparison of the diagnostic performance of machine learning algorithms for differentiating iron deficiency anemia and thalassemia.Annals of hematology · 2026Article
- Machine learning and AI-Assisted red blood cell morphology analysis for effective thalassemia screening.Annals of hematology · 2026Article
- AI-assisted haematology: machine learning-based prediction of iron-deficiency anaemia from reticulocyte maturation indices.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Assessment of β-Thalassemia Trait Occurrence in an Outpatient Sample from Southern Chile: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning improves detection of alpha thalassemia carriers compared to clinical features.Scientific reports · 2025Article
- Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning.Scientific reports · 2025Article
- Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.Scientific reports · 2025Article
- A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets.Scientific reports · 2025Article
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
- Diagnostic Performance of Red Blood Cell Indices in the Differential Diagnosis of Iron Deficiency Anemia and the Thalassemia Trait in Chile: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence-driven diagnosis of β-thalassemia minor & iron deficiency anemia using machine learning models.Journal of medical biochemistry · 2024Article
- Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia.Healthcare (Basel, Switzerland) · 2023Article
- Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting.PloS one · 2022Article
- A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits.Diagnostics (Basel, Switzerland) · 2021Article
- iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2021Article
- A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation.Scientific reports · 2021Article
- A decision support scheme for beta thalassemia and HbE carrier screening.Journal of advanced research · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.
methodsWe retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.
resultsThe data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool 'ThalPred' was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.
conclusionThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.