scholarly journals Variance Risk Identification and Treatment of Clinical Pathway by Integrated Bayesian Network and Association Rules Mining

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1191 ◽  
Author(s):  
Gang Du ◽  
Yinan Shi ◽  
Aijun Liu ◽  
Taoning Liu
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Cesarean Section ◽  
Bayesian Network ◽  
Clinical Features ◽  
Association Rules ◽  
Postpartum Hemorrhage ◽  
Clinical Pathways ◽  
Variance Analysis ◽  
Key Factors

With the continuous development of data mining techniques in the medical field, variance analysis in clinical pathways based on data mining approaches have attracted increasing attention from scholars and decision makers. However, studies on variance analysis and treatment of specific kinds of disease are still relatively scarce. In order to reduce the hazard of postpartum hemorrhage after cesarean section, we conducted a detailed analysis on the relevant risk factors and treatment mechanisms, adopting the integrated Bayesian network and association rule mining approaches. By proposing a Bayesian network model based on regression analysis, we calculated the probability of risk factors determining the key factors that result in postpartum hemorrhage after cesarean section. In addition, we mined a few association rules regarding the treatment of postpartum hemorrhage on the basis of different clinical features. We divided the risk factors into primary and secondary risk factors by realizing the classification of different causes of postpartum hemorrhage after cesarean section and sorted the posterior probability to obtain the key factors in the primary and secondary risk factors: uterine atony and prolonged labor. The rules of clinical features associated with the management of postpartum hemorrhage during cesarean section were obtained. Finally, related strategies were proposed for improving medical service quality and enhancing the rescue efficiency of clinical pathways in China.

scholarly journals Prevalence and risk factors of severe postpartum hemorrhage: a retrospective cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chen-ning Liu ◽  
Fu-bing Yu ◽  
Yun-zhe Xu ◽  
Jin-sheng Li ◽  
Zhi-hong Guan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cesarean Section ◽  
Postpartum Hemorrhage ◽  
Maternal Age ◽  
Placenta Previa ◽  
Previous Cesarean Section ◽  
Estimated Blood Loss ◽  
History Of ◽  
Prolonged Labor ◽  
Previous Cesarean

Abstract Background Although maternal deaths are rare in developed regions, the morbidity associated with severe postpartum hemorrhage (SPPH) remains a major problem. To determine the prevalence and risk factors of SPPH, we analyzed data of women who gave birth in Guangzhou Medical Centre for Critical Pregnant Women, which received a large quantity of critically ill obstetric patients who were transferred from other hospitals in Southern China. Methods In this study, we conducted a retrospective case-control study to determine the prevalence and risk factors for SPPH among a cohort of women who gave birth after 28 weeks of gestation between January 2015 and August 2019. SPPH was defined as an estimated blood loss ≥1000 mL and total blood transfusion≥4 units. Logistic regression analysis was used to identify independent risk factors for SPPH. Results SPPH was observed in 532 mothers (1.56%) among the total population of 34,178 mothers. Placenta-related problems (55.83%) were the major identified causes of SPPH, while uterine atony without associated retention of placental tissues accounted for 38.91%. The risk factors for SPPH were maternal age < 18 years (adjusted OR [aOR] = 11.52, 95% CI: 1.51–87.62), previous cesarean section (aOR = 2.57, 95% CI: 1.90–3.47), history of postpartum hemorrhage (aOR = 4.94, 95% CI: 2.63–9.29), conception through in vitro fertilization (aOR = 1.78, 95% CI: 1.31–2.43), pre-delivery anemia (aOR = 2.37, 95% CI: 1.88–3.00), stillbirth (aOR = 2.61, 95% CI: 1.02–6.69), prolonged labor (aOR = 5.24, 95% CI: 3.10–8.86), placenta previa (aOR = 9.75, 95% CI: 7.45–12.75), placenta abruption (aOR = 3.85, 95% CI: 1.91–7.76), placenta accrete spectrum (aOR = 8.00, 95% CI: 6.20–10.33), and macrosomia (aOR = 2.30, 95% CI: 1.38–3.83). Conclusion Maternal age < 18 years, previous cesarean section, history of PPH, conception through IVF, pre-delivery anemia, stillbirth, prolonged labor, placenta previa, placental abruption, PAS, and macrosomia were risk factors for SPPH. Extra vigilance during the antenatal and peripartum periods is needed to identify women who have risk factors and enable early intervention to prevent SPPH.

How Useful Can Be Data Mining For A Continuos Speech Therapist’s Education?

2014 ◽  
Vol 1 (1) ◽  
pp. 339-342
Author(s):  
Mirela Danubianu ◽  
Dragos Mircea Danubianu
Keyword(s):  
Data Mining ◽  
Information And Communication Technology ◽  
Association Rules ◽  
Communication Technology ◽  
Speech Therapy ◽  
Proper Treatment ◽  
Speech Impairments ◽  
Information And Communication ◽  
Specific Education

AbstractSpeech therapy can be viewed as a business in logopaedic area that aims to offer services for correcting language. A proper treatment of speech impairments ensures improved efficiency of therapy, so, in order to do that, a therapist must continuously learn how to adjust its therapy methods to patient's characteristics. Using Information and Communication Technology in this area allowed collecting a lot of data regarding various aspects of treatment. These data can be used for a data mining process in order to find useful and usable patterns and models which help therapists to improve its specific education. Clustering, classification or association rules can provide unexpected information which help to complete therapist's knowledge and to adapt the therapy to patient's needs.

A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

2017 ◽  
Vol 3 (1) ◽  
pp. 76 ◽  
Author(s):  
Duong Tran Duc ◽  
Pham Bao Son ◽  
Tan Hanh ◽  
Le Truong Thien
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Web Applications ◽  
Service Providers ◽  
Main Idea ◽  
Classification Methods ◽  
Cost Sensitive Learning ◽  
Network Method ◽  
Demographic Attributes ◽  
Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

scholarly journals Risk Factors for Postpartum Hemorrhage in a Thai–Myanmar Border Community Hospital: A Nested Case-Control Study

2021 ◽  
Vol 18 (9) ◽  
pp. 4633
Author(s):  
Waraporn Thepampan ◽  
Nuchsara Eungapithum ◽  
Krittai Tanasombatkul ◽  
Phichayut Phinyo
Keyword(s):  
Risk Factors ◽  
Pregnant Women ◽  
Postpartum Hemorrhage ◽  
Minority Groups ◽  
Case Control Study ◽  
Community Hospital ◽  
Case Control ◽  
Border Community ◽  
Nested Case Control ◽  
Control Study

Postpartum hemorrhage (PPH) is a common complication of pregnancy and a global public health concern. Even though PPH risk factors were extensively studied and reported in literature, almost all studies were conducted in non-Asian countries or tertiary care centers. Our study aimed to explore relevant risk factors for PPH among pregnant women who underwent transvaginal delivery at a Thai–Myanmar border community hospital in Northern Thailand. An exploratory nested case-control study was conducted to explore risk factors for PPH. Women who delivered transvaginal births at Maesai hospital from 2014 to 2018 were included. Two PPH definitions were used, which were ≥ 500 mL and 1000 mL of estimated blood loss within 24 h after delivery. Multivariable conditional logistic regression was used to identify significant risk factors for PPH and severe PPH. Of 4774 women with vaginal births, there were 265 (5.55%) PPH cases. Eight factors were identified as independent predictors for PPH and severe PPH: elderly pregnancy, minority groups, nulliparous, previous PPH history, BMI ≥ 35 kg/m2, requiring manual removal of placenta, labor augmentation, and fetal weight > 4000 gm. Apart from clinical factors, particular attention should be given to pregnant women who were minority groups as PPH risk significantly increased in this population.

scholarly journals When Uncontrolled Diabetes Mellitus and Severe COVID-19 Converge: The Perfect Storm for Mucormycosis

2021 ◽  
Vol 7 (4) ◽  
pp. 298
Author(s):  
Teny M. John ◽  
Ceena N. Jacob ◽  
Dimitrios P. Kontoyiannis
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Clinical Features ◽  
The Other ◽  
Diabetic Patients ◽  
Epidemiological Factors ◽  
Uncontrolled Diabetes ◽  
Diagnostic Certainty ◽  
Patients With Diabetes ◽  
Perfect Storm

Mucormycosis (MCR) has been increasingly described in patients with coronavirus disease 2019 (COVID-19) but the epidemiological factors, presentation, diagnostic certainty, and outcome of such patients are not well described. We review the published COVID-19-associated mucormycosis (CAMCR) cases (total 41) to identify risk factors, clinical features, and outcomes. CAMCR was typically seen in patients with diabetes mellitus (DM) (94%) especially the ones with poorly controlled DM (67%) and severe or critical COVID-19 (95%). Its presentation was typical of MCR seen in diabetic patients (mostly rhino-orbital and rhino-orbital-cerebral presentation). In sharp contrast to reported COVID-associated aspergillosis (CAPA) cases, nearly all CAMCR infections were proven (93%). Treating physicians should have a high suspicion for CAMCR in patients with uncontrolled diabetes mellitus and severe COVID-19 presenting with rhino-orbital or rhino-cerebral syndromes. CAMR is the convergence of two storms, one of DM and the other of COVID-19.

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