Search

Your search keyword '"Liang, Fu-Wen"' showing total 291 results
Start Over Author "Liang, Fu-Wen"
291 results on '"Liang, Fu-Wen"'

16. Transferring Knowledge from Text to Predict Disease Onset

Author

Liu, Yun, Chuang, Kun-Ta, Liang, Fu-Wen, Su, Huey-Jen, Stultz, Collin M., and Guttag, John V.

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract

In many domains such as medicine, training data is in short supply. In such cases, external knowledge is often helpful in building predictive models. We propose a novel method to incorporate publicly available domain expertise to build accurate models. Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem. We use these relevance estimates to rescale the features, causing more important features to experience weaker regularization. We apply our method to predict the onset of five chronic diseases in the next five years in two genders and two age groups. Our rescaling approach improves the accuracy of the model, particularly when there are few positive examples. Furthermore, our method selects 60% fewer features, easing interpretation by physicians. Our method is applicable to other domains where feature and outcome descriptions are available., Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA

Published
2016

17. Decomposing and simplifying the Fracture Risk Assessment Tool—a module from the Taiwan-specific calculator.

Author

Li, Chia-Chun, Liu, I-Ting, Cheng, Tien-Tsai, Liang, Fu-Wen, Sun, Zih-Jie, Chang, Yin-Fan, Chang, Chin-Sung, Yang, Yi-Ching, Lu, Tsung-Hsueh, Kuo, Li-Chieh, and Wu, Chih-Hsing

Subjects
BONE density, DUAL-energy X-ray absorptiometry, OLDER men, DISEASE risk factors, OLDER people, FEMUR neck, RISK assessment, YOUNG women
Abstract

The Fracture Risk Assessment Tool (FRAX®) is a widely utilized country-specific calculator for identifying individuals with high fracture risk; its score is calculated from 12 variables, but its formulation is not publicly disclosed. We aimed to decompose and simplify the FRAX® by utilizing a nationwide community survey database as a reference module for creating a local assessment tool for osteoporotic fracture community screening in any country. Participants (n  = 16384; predominantly women (75%); mean age = 64.8 years) were enrolled from the Taiwan OsteoPorosis Survey, a nationwide cross-sectional community survey collected from 2008 to 2011. We identified 11 clinical risk factors from the health questionnaires. BMD was assessed via dual-energy X-ray absorptiometry in a mobile DXA vehicle, and 10-year fracture risk scores, including major osteoporotic fracture (MOF) and hip fracture (HF) risk scores, were calculated using the FRAX®. The mean femoral neck BMD was 0.7 ± 0.1 g/cm2, the T-score was −1.9 ± 1.2, the MOF was 8.9 ± 7.1%, and the HF was 3.2 ± 4.7%. Following FRAX® decomposition with multiple linear regression, the adjusted R 2 values were 0.9206 for MOF and 0.9376 for HF when BMD was included and 0.9538 for MOF and 0.9554 for HF when BMD was excluded. The FRAX® demonstrated better prediction for women and younger individuals than for men and elderly individuals after sex and age stratification analysis. Excluding femoral neck BMD, age, sex, and previous fractures emerged as 3 primary clinical risk factors for simplified FRAX® according to the decision tree analysis in this study population. The adjusted R 2 values for the simplified country-specific FRAX® incorporating 3 premier clinical risk factors were 0.8210 for MOF and 0.8528 for HF. After decomposition, the newly simplified module provides a straightforward formulation for estimating 10-year fracture risk, even without femoral neck BMD, making it suitable for community or clinical osteoporotic fracture risk screening. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

28. Influence of Demographic, Socioeconomic, and Antenatal Care Factors on Iron Supplementation Adherence Among Filipino Pregnant Women Using Hierarchical Regression.

Author

Felipe-Dimog, Eva Belingon, Yu, Chia-Hung, and Liang, Fu-Wen

Subjects
THERAPEUTIC use of iron, STATISTICS, CROSS-sectional method, IRON in the body, SOCIOECONOMIC factors, DIETARY supplements, SURVEYS, DRUGS, DISEASE prevalence, DESCRIPTIVE statistics, SOCIODEMOGRAPHIC factors, PRENATAL care, PATIENT compliance, LOGISTIC regression analysis, DATA analysis software, ADULTS, MIDDLE age, ADOLESCENCE
Published
2023
Full Text
View/download PDF

30. Supplemental Table 1 from Genetic Modulation of Neurocognitive Function in Glioma Patients

Author

Liu, Yanhong, primary, Zhou, Renke, primary, Sulman, Erik P., primary, Scheurer, Michael E., primary, Boehling, Nicholas, primary, Armstrong, Georgina N., primary, Tsavachidis, Spiridon, primary, Liang, Fu-Wen, primary, Etzel, Carol J., primary, Conrad, Charles A., primary, Gilbert, Mark R., primary, Armstrong, Terri S., primary, Bondy, Melissa L., primary, and Wefel, Jeffrey S., primary

Published
2023
Full Text
View/download PDF

47. Association of maternal chronic disease with risk of congenital heart disease in offspring

Author

Chou, Hsin-Hsu, Chiou, Meng-Jiun, Liang, Fu-Wen, Chen, Lea-Hua, Lu, Tsung-Hsueh, and Li, Chung-Yi

Subjects
Congenital heart disease -- Risk factors, Maternal-fetal exchange -- Health aspects, Pregnancy, Complications of -- Influence, Health
Abstract

Background: Information about known risk factors for congenital heart disease is scarce. In this population-based study, we aimed to investigate the relation between maternal chronic disease and congenital heart disease in offspring. Methods: The study cohort consisted of 1 387 650 live births from 2004 to 2010. We identified chronic disease in mothers and mild and severe forms of congenital heart disease in their offspring from Taiwan's National Health Insurance medical claims. We used multivariable logistic regression analysis to assess the associations of all cases and specific types of congenital heart disease with various maternal chronic diseases. Results: For mothers with the following chronic diseases, the overall prevalence of congenital heart disease in their children was significantly higher than for mothers without these diseases: diabetes mellitus type 1 (adjusted odds ratio [OR] 2.32, 95% confidence interval [CI] 1.66-3.25), diabetes mellitus type 2 (adjusted OR 2.85, 95% CI 2.603.12), hypertension (adjusted OR 1.87, 95% CI 1.69-2.07), congenital heart defects (adjusted OR 3.05, 95% CI 2.45-3.80), anemia (adjusted OR 1.31, 95% CI 1.25-1.38), connective tissue disorders (adjusted OR 1.39, 95% CI 1.19-1.62), epilepsy (adjusted OR 1.37, 95% CI 1.0-81.74) and mood disorders (adjusted OR 1.25, 95% CI 1.11-1.41). The same pattern held for mild forms of congenital heart disease. A higher prevalence of severe congenital heart disease was seen only among offspring of mothers with congenital heart defects or type 2 diabetes. Interpretation: The children of women with several kinds of chronic disease appear to be at risk for congenital heart disease. Preconception counselling and optimum treatment of pregnant women with chronic disease would seem prudent., Congenital heart disease is the most common congenital disorder in newborns. Prevalence at birth ranges from 5 to 15 per 1000 live births. (1-4) Despite advances in pediatric cardiac diagnosis [...]

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results