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Anemic Status Prediction using Multilayer Perceptron Neural Network Model

8 pagesPublished: October 19, 2017


Artificial Neural Network (ANN) has been well recognized as an effective tool in medical science. Medical data is complex, and currently collected data does not flow in a standardized way. Data complexity makes the outcomes associated with intra- operative blood management difficult to assess. Reliable evidence is needed to selectively define areas that can be improved and establish standard protocols across healthcare service lines to substantiate best practice in blood product utilization. The ANN is able to provide this evidence using automatic learning techniques to mine the hidden information under the medical data and come to conclusions.
Blood transfusions can be lifesaving and are used commonly in complex surgical cases. Blood transfusions come with associated risks and are costly. Anemia and clinical symptoms are currently used to determine whether a packed red blood cell transfusion is necessary. In this paper, we worked with unique datasets of intra- operative blood management collected from the electronic medical record of the Keck Medical Center of USC. We apply Multilayer Perceptron Neural Network to estimate missing values and predict the degree of post-operative anemia. Successful predictions of postoperative anemia may help inform medical practitioners whether there is a need for a further packed red blood cell transfusion.

Keyphrases: Artificial Neural Networks, blood transfusion, machine learning, Multilayer Perceptron, prediction

In: Christoph Benzmüller, Christine Lisetti and Martin Theobald (editors). GCAI 2017. 3rd Global Conference on Artificial Intelligence, vol 50, pages 213--220

BibTeX entry
  author    = {Ching Hao Yu and Manas Bhatnagar and Rachel Hogen and Dilin Mao and Atefeh Farzindar and Kiran Dhanireddy},
  title     = {Anemic Status Prediction using Multilayer Perceptron Neural Network Model},
  booktitle = {GCAI 2017. 3rd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzm\textbackslash{}"uller and Christine Lisetti and Martin Theobald},
  series    = {EPiC Series in Computing},
  volume    = {50},
  pages     = {213--220},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/8bh6}}
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