A quantitative quality control method of big data in cancer patients using artificial neural network

Nonstandard treatments for cancer patients are commonly seen in hospitals of developing countries like China. So it is crucial to standardize the treatments for cancer with technological means in order to supervise the process of treatments. Widespread of electronic health records (EHRs) has generated massive data sets which are far beyond the capability of traditional computing model. Although there are process and measures about quality control, but automatic computerized Quantitative Control (QC) and quantization methods are still lack. In this paper, we propose a quantitative quality control method of radiotherapy and chemotherapy based on artificial neural network to automatically analysis and rate the compliance with standard treatment process. The quantitative QC items are established and the artificial neural network is constructed accordingly. Then the selected cases are evaluated by experts for corresponding QC grades to train the artificial neural network. After that, the trained artificial neural network can be used to grade new cases for their QC score. To meet the high requirement of computation and accommodate massive data sets, we adopt our proposal in the cloud. With massive data distributed on computing nodes in the cloud, computing capability of nodes are dynamically allocated to homogenization and ANN computing, each node work both homogenization medical records and ANN computing according to system load balance resulting the quantitative QC process perform in high parallelism.