Xiao Chen

Objective: To compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.Methods: In this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan–Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not.Results: A total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003).Conclusion: Deep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer.

OBJECTIVE: The rapid onset of pediatric sepsis and the short optimal time for resuscitation pose a severe threat to children's health in the ICU. Timely diagnosis and intervention are essential to curing sepsis. but there is a lack of research on the prediction of sepsis at shorter time intervals. This study proposes a predictive model towards real-time diagnosis of sepsis to help reduce the time to first antibiotic treatment. PATIENTS AND METHODS: The dataset used in this paper was obtained from the pediatric intensive care unit of Shanghai Children's Medical Center and consisted of the initial examination records of patients admitted to the hospital. The data included six groups of laboratory tests: medical history, physical examination. blood gas analysis. routine blood tests, serological tests, and coagulation tests. We divided the admission examination into three stages and proposed a sepsis prediction model towards real-time diagnosis based on local information to shorten waiting time for treatment. The model extracts homogeneous features from patient groups in real-time using a graph neural network and uses the deep forest to learn from homogeneous features and laboratory data to give a comprehensive prediction at the current stage. Discriminative features of each stage are used as augmented information for the next phase. finally achieving self-optimization of global judgment, assisting in pre-allocation of medical resources and providing timely medical assistance to sepsis patients. RESULTS: Based on the first stage, second stage, and full test, the AUCs of our model were 93.63%, 96.73%, and 97.58%, respectively, and the F1-scores were 77.35%, 85.71%, and 86.48%. respectively. The models gave relatively accurate predictions at each stage. CONCLUSIONS: The prediction model toward a real-time diagnosis of sepsis shows more accurate predictions at each stage compared to other control methods. When the first two stages of data are obtained as input, the model accuracy is close to using complete test data, which can help compress the time to diagnosis to about an hour after the test and significantly reduce waiting time.