CoChat
Sign Up
Cited by 67Hemoglobinopathies and Related DisordersArtificial Intelligence in HealthcareIron Metabolism and Disorders

ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia

Open access
Laengsri, V., Shoombuatong, W., Adirojananon, W., Nantasenamat, C., Prachayasittikul, V., Nuchnoi, P.·2019·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-019-0929-2PMC PMC6836478
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: The 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. Methods: We 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. Results: The 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. Conclusion: ThalPred 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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12911-019-0929-2

Cites

69 of 69 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The majority of existing ML models or rule-based formulas specifically differentiate one distinct thalassemia (α or β) from IDA.

    “The majority of these formulas specifically differentiate one distinct thalassemia (α or β) from IDA (e.g., [14][15][16][17][18][19].”

    From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [15]

    The Methods (Models construction; page unavailable) describe ML models that distinguish thalassemia trait from IDA, so this paper supports that part of the claim as an example. The Methods (Data collection; page unavailable) classify patients as having thalassemia trait without specifying a single α- or β-thalassemia subtype. The paper also does not establish that a majority of existing formulas are subtype-specific. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Existing models are primarily designed using small single-center data sets lacking independent validation.

    “Moreover, these models are primarily designed using small single-center data sets lacking independent validation (15,16,18,(33)(34)(35)(36).”

    From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [15]

    The Data collection section (page unavailable) describes a single laboratory source and a final dataset of 186 subjects; the Discussion calls the iron-deficiency-anemia sample small. But the Methods and Results report external validation and testing on an independent dataset, so this paper does not support the claim that its model lacked independent validation. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The majority of existing rule-based formulas and ML models are tailored to specific subcategories or subgoals, such as identifying one specific thalassemia or distinguishing these from IDA.

    “The majority of these rule-based formulas and ML models are tailored to specific subcategories or subgoals, such as identifying one specific thalassemia (19,(23)(24)(25)(26) and distinguishing these from IDA (15,17,(27)(28)(29)(30)(31)(32).”

    From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [15]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

2 of the 71 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. [34]Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees Taylor & Francis Group: CRC Press; 1984.
  2. [54]Piplani S, Madaan M, Mannan R, Manjari M, Singh T, Lalit M. Evaluation of various discrimination indices in differentiating iron deficiency anemia and beta thalassemia trait: A practical low cost solution. Annal Pathol Lab Med. 2016;3(6):A551–59.

Cited by

Sorted by citations

Related

Authors
Laengsri, V., Shoombuatong, W., Adirojananon, W., Nantasenamat, C., Prachayasittikul, V., & Nuchnoi, P.
Affiliations
Mahidol University (TH)
Corresponding author
Nuchnoi, P.
ORCID
Venue
BMC Medical Informatics and Decision Making
Publisher
Springer Science and Business Media LLC
Published
2019-11-07
Volume
19
Issue
1
Pages
212-212
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-019-0929-2
PMC
PMC6836478
OpenAlex
W2982945214
Cited by
66
References
71
Citations by year
2026: 10 · 2025: 18 · 2024: 11 · 2023: 9 · 2022: 5 · 2021: 6 · 2020: 5 · 2019: 2
FWCI
2.47
Type
journal article
Language
en
Update notices
correction (2019-11-19)
Open access
Licence
Locations
  • BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation
  • Ministère de l'Europe et des Affaires Étrangères
  • Mahidol University
  • Thailand Research Fund (MRG6180226)
  • Office of the Higher Education Commission
Topics
  • Health Sciences › Medicine › Genetics › Hemoglobinopathies and Related Disorders
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Hematology › Iron Metabolism and Disorders
Keywords
thalassemia trait, iron deficiency anemia, microcytic hypochromic anemia, differential diagnosis, red blood cell indices, web-based prediction tool, support vector machine, random forest, interpretable rules, Thai adults
MeSH terms
Machine Learning, Adolescent, Adult, Anemia, Hypochromic, Decision Trees, Diagnosis, Differential, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, ROC Curve, Thailand, Cluster Analysis, Neural Networks, Computer, beta-Thalassemia, Anemia, Iron-Deficiency, Internet, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review