Objective:To investigate the effect of soft channel minimally invasive treatment on hypertensive intracerebral hemorrhage (HICH) and its influence on serum interleukin-18 (IL-18), vascular endothelial growth factor (VEGF), C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α).Methods:From April 2017 to April 2019, 82 patients with HICH admitted to Taizhou Hospital of Traditional Chinese Medicine were randomly divided into observation group (41 cases) and control group (41 cases) according to random number table method.The control group was treated with hard channel minimally invasive treatment, while the observation group was treated with soft channel minimally invasive treatment.The therapeutic effect, intracranial hematoma volume, serum levels of IL-18, VEGF, CRP and TNF-α before and 7 days after operation, neurological deficit degree (NIHSS) scores before and 3 months after operation, and complications after operation were compared between the two groups.Results:The total effective rate of the observation group (92.68%) was higher than that of the control group (70.73%) (χ 2=6.609, P<0.05). The amount of intracranial hematoma in the observation group [(4.03±1.10)mL] was lower than that in the control group [(7.17±1.36)mL] ( t=11.495, P<0.05). At 7 d after operation, the serum levels of IL-18[(123.74±10.27)ng/L], VEGF[(113.28±12.10)ng/L], CRP[(17.83±3.20)mg/L] and TNF-α[(0.65±0.12)ng/L] in the observation group were lower than those in the control group [(150.38±13.21)ng/L, (141.63±16.87)ng/L, (29.96±4.53)mg/L and (1.09±0.17)ng/L] ( t=11.638, 9.101, 13.831, 5.569, all P<0.05). The NIHSS score of the observation group[(16.53±3.19)points] was lower than that of the control group[(23.43±4.65)points] at 3 months after operation ( t=7.824, P<0.05). The incidence of complications in the observation group (12.20%) was lower than that in the control group (34.15%) (χ 2=5.549, P<0.05). Conclusion:Soft channel minimally invasive treatment for HICH has good effect and can reduce the changes of serum levels of IL-18, VEGF, CRP and TNF-α.

Effective referral management of health and clinical services in maternal and child health hospitals plays an important role in enhancing patients′ medical experience, improving the efficiency and quality of maternal and child health services. A tertiary grade A maternal and child health hospital has carried out a practice of health and clinical service referral management based on information technology construction. A referral information module embedded in the hospital information system has been designed and constructed, and started to be applied in outpatient clinics in July 2021. At the same time, corresponding system and process construction, as well as quality control management and continuous improvement, have been carried out. The outpatient referral rate from July to December 2021 was 2.8% (11 466/412 808), from January to June 2022 it was 5.6% (22 705/402 586), from July to December 2022 it was 5.5% (22 233/402 959), and from January to June 2023 it was 6.7% (23 373/347 898). The referral rate has continued to improve and can provide reference for the referral management of other maternal and child health institutions.

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.