ObjectiveTo systematically review the correlation between E-cadherin expression and prostate cancer, as well as its clinicopathologic features in Chinese population. MethodsSuch databases as PubMed, EMbase, CBM, CNKI, VIP and WanFang Data were electronically searched from their inception to December, 2015 to collect case-control studies about the correlation between E-cadherin expression and prostate cancer, as well as its clinically pathologic features in Chinese population. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed using RevMan 5.3 software. ResultsA total of 21 studies were included, involving 920 prostate cancer cases, 415 benign prostatic hyperplasia cases, and 48 controls. The results of meta-analysis showed that the prostate cancer group had a lower E-cadherin expression level when compared with the benign prostatic hyperplasia group (OR=0.07, 95%CI 0.05 to 0.11, P<0.00001) or the control group (OR=0.04, 95%CI 0.01 to 0.18, P<0.00001). Moreover, the expression level of E-cadherin was lower in the low and medium differentiation group than in the high differentiation group (OR=0.13, 95%CI 0.08 to 0.23, P<0.00001), lower in the stage of C+D than in the stage of A+B (OR=0.23, 95%CI 0.15 to 0.34, P<0.00001), and lower in the prostate cancer with metastasis (OR=0.46, 95%CI 0.27 to 0.79, P=0.005) and it was decreased gradually with the increment of pathological differentiation and clinical stage of prostate cancer and with the decrement of lymph node or bone metastasis and serum PSA level. ConclusionCurrent evidence indicates that the expression level of E-cadherin is significantly correlated with prostate cancer and its clinicopathologic features in Chinese population. Due to limited sample size and quality of included studies, the conclusion needs to be verified by conducting more high quality studies.
ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.