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Efficacy prediction based on attribute and multi-source data collaborative for auxiliary medical system in developing countries

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Abstract

Non-small cell lung cancer is one of the acute diseases threatening human life. In many developing countries, there are medical problems such as large populations, underdeveloped technologies, and lack of resources. It is difficult for the "fragmented" approach to medical treatment to provide patients with complete life cycle treatment services. Research on the medical system of personalized adjuvant therapy can improve medical resources and the survival rate of patients in developing countries. This paper establishes a predictive framework for adjuvant therapy's medication based on the collaborative filtering of a patient and drug attributes and multi-source data. The framework is divided into the feature extraction module of patients and drugs about the treatment stage and the efficacy evaluation prediction module. We have proposed a quantitative method for efficacy evaluation. The proposed method can assist doctors in providing patients with personalized treatment plans based on efficacy evaluation analysis and prediction. By comparing and analyzing with other methods, the framework can effectively learn from expert experience and provide doctors with auxiliary treatment analysis and medication evaluation. The prediction accuracy of the model can reach 0.86.

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Funding

This work was supported in The National Natural Science Foundation of China(61672540); Fundamental Research Funds for the Central Universities of Central South University (2021zzts0204).

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Authors and Affiliations

  1. The School of Computer Science and Engineering, Central South University, Changsha, 410075, China

    Genghua Yu & Jia Wu

  2. Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, 410083, China

    Genghua Yu & Jia Wu

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  1. Genghua Yu
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  2. Jia Wu
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Correspondence to Jia Wu.

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The authors declare that there is no conflict of interest regarding the publication of this paper. The funding is no conflict of interest. All data sources are the Second Xiang-ya Hospital of Central South University and the Ministry of Education Mobile Health Information-China Mobile Joint Laboratory.

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Yu, G., Wu, J. Efficacy prediction based on attribute and multi-source data collaborative for auxiliary medical system in developing countries. Neural Comput & Applic 34, 5497–5512 (2022). https://doi.org/10.1007/s00521-021-06713-0

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