Machine Learning Algorithm-Based Analysis of Efficacy of Pulmonary Surfactant Combined with Mucosolvan in Meconium Aspiration Syndrome of Newborns through Ultrasonic Images

Objective. The study aimed to explore the efficacy of pulmonary surfactant (PS) combined with Mucosolvan in the diagnosis of meconium aspiration syndrome (MAS) in newborns through ultrasonic images of lung based on machine learning. Methods. 138 cases of infants with MAS were selected as the research subjects and randomly divided into PS group (n = 46), Mucosolvan group (n = 46), and combination group (n = 46). Then, ultrasonic images based on machine learning algorithm were used for examination. On the basis of conventional treatment, the PS group accepted intratracheal PS drip treatment with 100 mg/kg. For the Mucosolvan group, 7.5 mg/kg of Mucosolvan was added with 50 g/L glucose, which was diluted to 3 mL. Then, the mixture was injected intravenously with a micropump for more than 5 min. The combination group received combined treatment of PS and Mucosolvan. If there was no relief or the symptoms aggravated after 12 h of PS treatment, the patient should be treated again. 7.5 mg/kg/d of Mucosolvan was given for 7 days. Mechanical ventilation time, hospitalization time, oxygenation index (OI) before treatment, at 3 d and at 7 d after treatment, and arterial/alveolar oxygen ratio (a/APO2) of the three groups were detected and compared. Besides, in-hospital mortality and complication rate of the three groups were statistically compared. Results. Ultrasonic image edge detection based on machine learning algorithm was more condensed and better than Sobel operator. Compared with the PS group and the Mucosolvan group, treatment efficiency, OI at 3 d and at 7 d after treatment, and a/APO2 of combination group were increased. Mechanical ventilation time and hospitalization time of the combination group were shortened, and mortality rate of the combination group was reduced ( P < 0.05). Compared with the situation before treatment, OI at 3 d and at 7 d after treatment and a/APO2 of the combination group were increased, and OI at 7 d after treatment and a/APO2 of the PS group and the Mucosolvan group were increased ( P < 0.05). Curative effect, mechanical ventilation time, hospitalization time, OI before and after treatment, a/APO2, and mortality rate during hospitalization of the PS group and the Mucosolvan group had no significant difference ( P > 0.05). There was no significant difference in the complications rates in the three groups ( P > 0.05). Conclusion. Pulmonary ultrasound based on machine learning algorithm can be used in the diagnosis of MAS in neonates. PS combined with Mucosolvan is feasible and safe in treating neonatal MAS and effectively improves the pulmonary oxygenation function. Therefore, it is worthy of clinical application.

Download Full-text

Comparison of the clinical effect between the lower sternal incision and the left parasternal fourth intercostal incision in the transthoracic closure of ventricular septal defect

10.21203/rs.3.rs-37475/v1 ◽

2020 ◽

Author(s):

Jun Ma ◽

Wenlin Shangguan ◽

Liang-wan Chen ◽

Dong-Shan Liao

Keyword(s):

Mechanical Ventilation ◽

Ventricular Septal Defect ◽

Clinical Effect ◽

Septal Defect ◽

Operation Time ◽

Ventilation Time ◽

Significant Difference ◽

Group A ◽

Mechanical Ventilation Time ◽

Group B

Abstract Background: To analyze the clinical effect of two different ways of minimally invasive transthoracic closure in children with ventricular septal defect (VSD) Methods: From January 2015 to July 2019, 294 children with VSD were enrolled in the Fujian Medical University Union Hospital, who underwent VSD closure through the left sternal fourth intercostal incision (group A: n = 95) and the lower sternal incision (group B: n = 129) Results: The operation time, bleeding volume, postoperative mechanical ventilation time, postoperative ICU monitoring time, postoperative hospitalization time and complication rate in group A were significantly lower than those in group B (P < 0.05). There was no significant difference between the two groups in the operation success rate, mechanical ventilation time and total hospitalization cost (P > 0.05). Conclusion : The transthoracic closure of ventricular septal defect through the left sternal fourth intercostal incision is feasible, safe, cosmetic, and worth popularizing.

Download Full-text

Comparison of the clinical effect between the lower sternal incision and the left parasternal fourth intercostal incision in the transthoracic closure of ventricular septal defect

Journal of Cardiothoracic Surgery ◽

10.1186/s13019-021-01543-6 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Jun Ma ◽

Wenlin Shangguan ◽

Liang-Wan Chen ◽

Dong-shan Liao

Keyword(s):

Mechanical Ventilation ◽

Ventricular Septal Defect ◽

Clinical Effect ◽

Septal Defect ◽

Operation Time ◽

Ventilation Time ◽

Significant Difference ◽

Group A ◽

Mechanical Ventilation Time ◽

Group B

Abstract Background To analyze the clinical effect of two different ways of minimally invasive transthoracic closure in children with ventricular septal defect (VSD). Methods From January 2015 to July 2019, 294 children with VSD were enrolled in the Fujian Medical University Union Hospital. Patients were divided into two groups – those who underwent VSD closure through the left sternal fourth intercostal incision (group A: n = 95) and the lower sternal incision (group B: n = 129). Results The operation time, bleeding volume, postoperative mechanical ventilation time, postoperative intensive care unit (ICU) monitoring time, postoperative hospitalization time and complication rate in group A were significantly lower than those in group B (P < 0.05). There was no significant difference between the two groups in the operation success rate, mechanical ventilation time and total hospitalization cost (P > 0.05). Conclusion The transthoracic closure of ventricular septal defect through the left sternal fourth intercostal incision is feasible, safe, cosmetic, and worth popularizing.

Download Full-text

Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm

Frontiers in Endocrinology ◽

10.3389/fendo.2021.745039 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ran Liu ◽

Shun Bai ◽

Xiaohua Jiang ◽

Lihua Luo ◽

Xianhong Tong ◽

...

Keyword(s):

Machine Learning ◽

Embryo Transfer ◽

Learning Algorithm ◽

Conditional Inference ◽

Support Vector ◽

Machine Learning Algorithm ◽

Significant Difference ◽

Predictive Rate ◽

Ivf Et ◽

Day Of Embryo Transfer

In vitro fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factors are correlated with the outcome of FET which is unpredictable. Machine learning is a field of study that predict various outcomes by defining data attributes and using relevant data and calculation algorithms. Machine learning algorithm has been widely used in clinical research. The present study focuses on making predictions of early pregnancy outcomes in FET through clinical characters, including age, body mass index (BMI), endometrial thickness (EMT) on the day of progesterone treatment, good-quality embryo rate (GQR), and type of infertility (primary or secondary), serum estradiol level (E2) on the day of embryo transfer, and serum progesterone level (P) on the day of embryo transfer. We applied four representative machine learning algorithms, including logistic regression (LR), conditional inference tree, random forest (RF) and support vector machine (SVM) to build prediction models and identify the predictive factors. We found no significant difference among the models in the sensitivity, specificity, positive predictive rate, negative predictive rate or accuracy in predicting the pregnancy outcome of FET. For example, the positive/negative predictive rate of the SVM (gamma = 1, cost = 100, 10-fold cross validation) is 0.56 and 0.55. This approach could provide a reference for couples considering FET. The prediction accuracy of the present study is limited, which suggests that there may be some other more effective predictors to be developed in future work.

Download Full-text

Sedative use of Dexmedetomidine vs. Propofol after Cardiac Surgery: A critical review and meta-analysis.

10.21203/rs.2.230/v1 ◽

2019 ◽

Author(s):

Hesham Abowali ◽

Matteo Paganini ◽

Ayman Elbadawi ◽

Enrico Camporesi

Keyword(s):

Intensive Care Unit ◽

Mechanical Ventilation ◽

Cardiac Surgery ◽

Intensive Care ◽

Length Of Stay ◽

Time Dependent ◽

Confounding Factors ◽

Ventilation Time ◽

Significant Difference ◽

Mechanical Ventilation Time

Abstract BACKGROUND: The efficacy and safety of dexmedetomidine in sedation for postoperative cardiac surgeries is controversial when compared to propofol. METHODS: A computerized search of Medline, Cochrane and Google Scholar databases was performed through August 2018. Studies evaluating the efficacy of dexmedetomidine versus propofol in the sedation of postoperative cardiac surgery patients were searched. The main study outcomes were divided into time dependent (mechanical ventilation time; time to extubation; length of stay in the intensive care unit and in the hospital) and non-time dependent (delirium, bradycardia, and hypotension). RESULTS: The final analysis included 15 trials with a total of 2488 patients. Time to extubation was significantly reduced in the dexmedetomidine group (Standardized Mean Difference (SMD) = -0.54, 95% Confidence Interval (CI): -0.89 to -0.18, p=0.003), as well as mechanical ventilation time (SMD= -0.71, 95% CI: -1.19 to -0.23, p=0.004). Moreover, the dexmedetomidine group showed a significant reduction in Intensive Care Unit length of stay (SMD= -0.38, 95% CI: -0.60 to -0.16, p=0.001) and hospital length of stay (SMD= -0.39, 95% CI: -0.60 to -0.19, p<0.001). However, these time dependent outcomes could have been affected by several confounding factors, thus limiting the value of these results. Incidence of delirium was reduced in the dexmedetomidine group (OR: 0.47, 95% CI: 0.29 to 0.76, p=0.002), while this group of patients had higher rates of bradycardia (OR: 2.52, 95% CI: 1.15 to 5.55, p=0.021). There was no significant difference in rates of hypotension between the two groups. CONCLUSION: Despite the apparent time advantages afforded by dexmedetomidine over propofol, the former does not show particular overall improvements in postoperative care of cardiac surgery patients. Since time dependent outcomes seems to be affected by several confounding factors, more efforts are needed to analyze factors that could affect sedation in post-cardiac surgery patients and choose unbiased outcomes. KEYWORDS: Dexmedetomidine; propofol; cardiac surgery; postoperative sedation.

Download Full-text

Systematic Evaluation of the Effect of Bedside Ward Round Checklist on Clinical Outcomes of Critical Patients

Journal of Healthcare Engineering ◽

10.1155/2021/8105516 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Xuemin Wen ◽

YuXiang Wen ◽

Ge Wang ◽

Hui Li ◽

Hong Zuo

Keyword(s):

Mechanical Ventilation ◽

Cohort Studies ◽

Clinical Outcomes ◽

Meta Analysis ◽

Ward Round ◽

Cochrane Library ◽

Ventilation Time ◽

Hospitalization Time ◽

Critical Patients ◽

Mechanical Ventilation Time

Objective. To systematically evaluate the effect of bedside ward round checklists on the clinical outcomes of critical patients and thus provide a scientific and rational basis for decision-making in its clinical application. Methods. PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, and Wanfang databases were searched to collect the literature studies about randomized controlled trials (RCTs) and cohort studies involving the effect of bedside ward round checklists on the clinical outcomes of critical patients, and the retrieval time limit was from the establishment of the database to August 2019. After two researchers independently screened the literature studies, extracted the literature data, and evaluated the risk of bias in included studies, meta-analysis was carried out by using Stata 12.0 software. Results. Two RCTs and nine cohort studies were included in this study. The results of meta-analysis showed that compared with the ordinary bedside ward round, the application of checklist in bedside ward round could shorten the ICU hospitalization time (standardized mean difference (SMD) = – 0.37, 95% CI (– 0.78, 0.04), P ≤ 0.001) and mechanical ventilation time (SMD = – 0.24, 95% CI (– 0.44, −0.04), P = 0.037) and reduce the incidence of ventilator-associated pneumonia (VAP) (SMD = 0.61, 95% CI (0.38, 0.99), P = 0.057) in critical patients. However, there were no significant differences in central venous catheter (CVC) retention time and incidence and mortality of deep venous thrombosis (DVT) between ordinary ward round and bedside ward round checklist. Conclusion. The existing evidence shows that compared with the ordinary ward round, the application of bedside ward round checklists can shorten ICU hospitalization time and mechanical ventilation time and reduce VAP incidence and ICU mortality in critical patients. However, due to the limitations of the quality of the included studies, the above conclusions need to be verified with more high-quality studies.

Download Full-text

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

Download Full-text

Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Download Full-text