Objective:To explore the application of flipped classroom teaching in the standardized residency training of students in department of hepatic surgery from the perspective of production-oriented approach (POA) theory.Methods:Based on the implementation time of flipped classroom teaching (January 2017) from the perspective of POA theory in West China Hospital, 51 nursing students who were routinely trained in department of hepatic surgery before the implementation (January 2015 to January 2017) were included in the control group. After the implementation (January 2017 to January 2019), 51 nursing students under the residency training were included in the observation group. The changes of test scores, independent learning ability and professional awareness of the two groups of nursing students before and 6 months after the training were compared, and the teaching satisfaction after the training was recorded. SPSS 19.0 was performed for t test and chi-square test. Results:The total scores of the theoretical examination scores of the two groups of nursing students, the total scores of the clinical practice assessment scores, and the self-learning ability assessment scale for nursing college students (learning motivation, self-management ability, learning cooperation ability, information literacy) scores and total scores in each dimension were significantly higher than those before training, and the observation group was significantly higher than the control group ( P<0.05); the two groups of professional maturity scales (career goals, professional self-confidence, professional autonomy, professional value, dependence on relatives and friends, occupation reference) scores of all dimensions were significantly improved compared with those before the training, and the scores of the observation group (career goals, professional self-confidence, professional autonomy, professional value) were significantly higher than those of the control group during the same period ( P<0.05). There was no statistical significance in the comparison between the two groups' dependence on relatives and friends and occupational reference scores ( P>0.05); the teaching satisfaction rate of nursing students in the observation group was 96.08%, which was significantly higher than that of the control group (82.35%). Conclusion:The application of flipped classroom teaching from the perspective of POA theory in the standardized residency training of department of hepatic surgery can significantly improve the autonomous learning ability of nursing students, which is conducive to the improvement of test scores and professional awareness, with high teaching satisfaction.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.