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. 2015 Jun 25;10(6):e0131197.
doi: 10.1371/journal.pone.0131197. eCollection 2015.

Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy

Tetsuro Takayama  1 , Susumu Okamoto  2 , Tadakazu Hisamatsu  3 , Makoto Naganuma  4 , Katsuyoshi Matsuoka  5 , Shinta Mizuno  6 , Rieko Bessho  3 , Toshifumi Hibi  7 , Takanori Kanai  3
Affiliations

Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy

Tetsuro Takayama et al. PLoS One. .

Abstract

Cytoapheresis (CAP) therapy is widely used in ulcerative colitis (UC) patients with moderate to severe activity in Japan. The aim of this study is to predict the need of operation after CAP therapy of UC patients on an individual level using an artificial neural network system (ANN). Ninety UC patients with moderate to severe activity were treated with CAP. Data on the patients' demographics, medication, clinical activity index (CAI) and efficacy of CAP were collected. Clinical data were divided into training data group and validation data group and analyzed using ANN to predict individual outcomes. The sensitivity and specificity of predictive expression by ANN were 0.96 and 0.97, respectively. Events of admission, operation, and use of immunomodulator, and efficacy of CAP were significantly correlated to the outcome. Requirement of operation after CAP therapy was successfully predicted by using ANN. This newly established ANN strategy would be used as powerful support of physicians in the clinical practice.

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Conflict of interest statement

Competing Interests: The authors received funding from Otsuka Pharmaceutical, Inc., though this does not alter their adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Relative weights of input factors for ANNs.
Data are expressed as the mean±SD for member networks.
See this image and copyright information in PMC

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