This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

Background Over the recent years, machine learning methods have been increasingly explored in cancer prognosis because of the appearance of improved machine learning algorithms. These algorithms can use censored data for modeling, such as support vector machines for survival analysis and random survival forest (RSF). However, it is still debated whether traditional (Cox proportional hazard regression) or machine learning-based prognostic models have better predictive performance. Objective This study aimed to compare the performance of breast cancer prognostic prediction models based on machine learning and Cox regression. Methods This retrospective cohort study included all patients diagnosed with breast cancer and subsequently hospitalized in Fudan University Shanghai Cancer Center between January 1, 2008, and December 31, 2016. After all exclusions, a total of 22,176 cases with 21 features were eligible for model development. The data set was randomly split into a training set (15,523 cases, 70%) and a test set (6653 cases, 30%) for developing 4 models and predicting the overall survival of patients diagnosed with breast cancer. The discriminative ability of models was evaluated by the concordance index (C-index), the time-dependent area under the curve, and D-index; the calibration ability of models was evaluated by the Brier score. Results The RSF model revealed the best discriminative performance among the 4 models with 3-year, 5-year, and 10-year time-dependent area under the curve of 0.857, 0.838, and 0.781, a D-index of 7.643 (95% CI 6.542, 8.930) and a C-index of 0.827 (95% CI 0.809, 0.845). The statistical difference of the C-index was tested, and the RSF model significantly outperformed the Cox-EN (elastic net) model (C-index 0.816, 95% CI 0.796, 0.836; P=.01), the Cox model (C-index 0.814, 95% CI 0.794, 0.835; P=.003), and the support vector machine model (C-index 0.812, 95% CI 0.793, 0.832; P<.001). The 4 models’ 3-year, 5-year, and 10-year Brier scores were very close, ranging from 0.027 to 0.094 and less than 0.1, which meant all models had good calibration. In the context of feature importance, elastic net and RSF both indicated that TNM staging, neoadjuvant therapy, number of lymph node metastases, age, and tumor diameter were the top 5 important features for predicting the prognosis of breast cancer. A final online tool was developed to predict the overall survival of patients with breast cancer. Conclusions The RSF model slightly outperformed the other models on discriminative ability, revealing the potential of the RSF method as an effective approach to building prognostic prediction models in the context of survival analysis.

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BACKGROUND Over recent years, machine learning (ML) methods have been increasingly explored in cancer prognosis prediction because of the appearance of improved machine learning algorithms. These algorithms can use censored data for modeling, such as support vector machines (SVM) for survival analysis and random survival forest (RSF). However, it is still debated whether traditional (Cox proportional hazard regression) or ML-based prognostic prediction models have better predictive performance. OBJECTIVE This study aims to use the machine learning algorithms to predict the survival of breast cancer and compare the predictive performance with the traditional Cox regression. METHODS This retrospective cohort study included all patients diagnosed with breast cancer and subsequently hospitalized in Fudan University Shanghai Cancer Center (FUSCC) between January 1, 2008 and December 31, 2016. A total of 25267 cases with 21 features were eligible for model development, and the data set was randomly split into a train set (70%) and a test set (30%) for developing four models and predicting overall survival in breast cancer patients. The discriminative ability of models was evaluated by the concordance index (C-index) and the time-dependent area under the curve (AUC); the calibration ability of models was evaluated by the Brier score. RESULTS The RSF model revealed the best discriminative performance among the four models with 3-year, 5-year and 10-year time-dependent AUC of 0.857, 0.838 and 0.781, respectively and C-index of 0.827 (0.809, 0.845), which significantly outperformed the Cox-EN model (0.816, p=0.007), the Cox model (0.814, p=0.003) and the SVM model (0.812, p<0.001). The four models' 3-year, 5-year, and 10-year brier scores were very close, ranging from 0.027 to 0.094, which meant all models had good calibration. In the context of feature importance, elastic net and RSF both indicated that TNM staging, neoadjuvant therapy, number of lymph node metastases, age, and tumor diameter were the top 5 important features for predicting the prognosis of breast cancer. A final online tool was developed to predict the overall survival of breast cancer patients. CONCLUSIONS RSF model slightly outperformed the other models on discriminative ability, revealing the great potential to be used as an effective approach for survival analysis. CLINICALTRIAL ClinicalTrials. gov, registration number: NCT04996732.