Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jul 10;14(1):15958.
doi: 10.1038/s41598-024-66607-8.

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Nasibeh Rady Raz  1 , Ali Arash Anoushirvani  2   3 , Neda Rahimian  4 , Maryam Ghoerishi  4 , Nazanin Alibeik  4 , Masoumeh Sajadi Rad  5
Affiliations

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Nasibeh Rady Raz et al. Sci Rep. .

Abstract

Periodic blood transfusion is a need in cancer patients in which the disease process as well as the chemotherapy can disrupt the natural production of blood cells. However, there are concerns about blood transfusion side effects, the cost, and the availability of donated blood. Therefore, predicting the timely requirement for blood transfusion considering patient variability is a need, and here for the first-time deal with this issue in blood cancer using in vivo data. First, a data set of 98 samples of blood cancer patients including 61 features of demographic, clinical, and laboratory data are collected. After performing multivariate analysis and the approval of an expert, effective parameters are derived. Then using a deep recurrent neural network, a system is presented to predict a need for packed red blood cell transfusion. Here, we use a Long Short-Term Memory (LSTM) neural network for modeling and the cross-validation technique with 5 layers for validation of the model along with comparing the result with networking and non-networking machine learning algorithms including bidirectional LSTM, AdaBoost, bagging decision tree based, bagging KNeighbors, and Multi-Layer Perceptron (MLP). Results show the LSTM outperforms the other methods. Then, using the swarm of fuzzy bioinspired nanomachines and the most effective parameters of Hgb, PaO2, and pH, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirements. Alarming decisions using the Internet of Things (IoT) gateway are delivered to the physician for performing medical actions. Also, NFABT is considered a real-time non-invasive AI-based hemoglobin monitoring and alarming method. Results show the merits of the proposed method.

Keywords: Artificial intelligence; Blood transfusion; Cancer; Deep learning; Fuzzy system; Nanonetworks.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Pearson’s correlation matrix for geographical data.
Figure 2
Figure 2
Pearson’s correlation matrix for clinical data.
Figure 3
Figure 3
Pearson’s correlation matrix for the first set of laboratory data.
Figure 4
Figure 4
Pearson’s correlation matrix for the second set of laboratory data.
Figure 5
Figure 5
Pearson’s correlation matrix for third sets of laboratory data.
Figure 6
Figure 6
Scatterplot of clinical data for the two classes of blood transfusion.
Figure 7
Figure 7
Scatterplot of laboratory data for the two classes of blood transfusion.
Figure 8
Figure 8
Scatterplot of second sets of laboratory data for the two classes of blood transfusion.
Figure 9
Figure 9
Scatterplot of third sets of laboratory data for the two classes of blood transfusion.
Figure 10
Figure 10
Proposed nano fuzzy alarming system (NFABT) for blood transfusion requirement.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Ddungu H, et al. Transfusion challenges in patients with hematological malignancies in Sub-Saharan Africa: A prospective observational study from the Uganda Cancer Institute. Sci. Rep. 2020;10(1):2825. doi: 10.1038/s41598-020-59773-y. - DOI - PMC - PubMed
    1. Shung D, et al. Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit. Sci. Rep. 2021;11(1):8827. doi: 10.1038/s41598-021-88226-3. - DOI - PMC - PubMed
    1. Wu H-L, Tai Y-H, Lin S-P, Chan M-Y, Chen H-H, Chang K-Y. The impact of blood transfusion on recurrence and mortality following colorectal cancer resection: A propensity score analysis of 4,030 patients. Sci. Rep. 2018;8(1):13345. doi: 10.1038/s41598-018-31662-5. - DOI - PMC - PubMed
    1. Cata, J. P., Gottumukkala, V. Blood transfusion practices in cancer surgery. no. 0019-5049 (Print) (in eng). - PMC - PubMed
    1. Suzuki G, et al. Association of red blood cell and platelet transfusions with persistent inflammation, immunosuppression, and catabolism syndrome in critically ill patients. Sci. Rep. 2022;12(1):629. doi: 10.1038/s41598-021-04327-z. - DOI - PMC - PubMed

LinkOut - more resources