Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a machine learning method used in many research fields but has yet to be applied to estimating HOMA-IR.

Resting heart rate - The forgotten risk factor? Comparison of resting heart rate and hypertension as predictors of all-cause mortality in 692,217 adults in Asia and Europe.

Background: Resting Heart Rate (RHR) is commonly viewed as a reflection of underlying co-morbidities and not an independent risk factor. Here we compared whether high RHR (80-99 beats/min) and hypertension (blood pressure, BP ≥140/90 mmHg) independently predict all-cause mortality in 692,217 adults from Asia and Europe.

Methods: Taiwan MJ cohort constituted of 636,064 adults (1994-2017) and the HUNT cohort of 56,153 Norwegian adults (1995-1997).

Novel machine learning algorithm in risk prediction model for pan-cancer risk: application in a large prospective cohort.

Objective: To develop and validate machine-learning models that predict the risk of pan-cancer incidence using demographic, questionnaire and routine health check-up data in a large Asian population.

Methods And Analysis: This study is a prospective cohort study including 433 549 participants from the prospective MJ cohort including a male cohort (n=208 599) and a female cohort (n=224 950).

Results: During an 8-year median follow-up, 5143 cancers occurred in males and 4764 in females.

Association between Activity Quotient and cause-specific mortality - A prospective cohort study of 0.5 million participants in Asia.

Background: Regular physical activity (PA) is important for reducing the risk of chronic diseases and improving overall health. Activity Quotient (AQ) is a novel metric that translates heart rate during PA into a weekly score, providing an objective measure of an individual's PA. We prospectively examined the association of AQ with cancer and cardiovascular (CVD) mortality outcomes, the two major causes of death, in a Taiwanese population.

Using machine learning to predict patients with polycystic ovary disease in Chinese women.

Objective: With an estimated global frequency ranging from5 % to 21 %, polycystic ovary syndrome (PCOS) is one of the most prevalent hormonal disorders. There are many factors found to be related to PCOS. However, most of these researches used traditional methods such as multiple logistic regression (LR).

Next-visit prediction and prevention of hypertension using large-scale routine health checkup data.

This paper proposes the use of machine learning models to predict one's risk of having hypertension in the future using their routine health checkup data of their current and past visits to a health checkup center. The large-scale and high-dimensional dataset used in this study comes from MJ Health Research Foundation in Taiwan. The training data for models is separated into 5 folds and used to train 5 models in a 5-fold cross validation manner.

The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up.

The prevalence of osteoporosis has drastically increased recently. It is not only the most frequent but is also a major global public health problem due to its high morbidity. There are many risk factors associated with osteoporosis were identified.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver.

Background: The prevalence of non-alcoholic fatty liver (NAFLD) has increased recently. Subjects with NAFLD are known to have higher chance for renal function impairment. Many past studies used traditional multiple linear regression (MLR) to identify risk factors for decreased estimated glomerular filtration rate (eGFR).

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up.

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPG), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years.

Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women.

Introduction: Vitamin D plays a vital role in maintaining homeostasis and enhancing the absorption of calcium, an essential component for strengthening bones and preventing osteoporosis. There are many factors known to relate to plasma vitamin D concentration (PVDC). However, most of these studies were performed with traditional statistical methods.

Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese.

Background: The incidence of chronic kidney disease (CKD) has dramatically increased in recent years, with significant impacts on patient mortality rates. Previous studies have identified multiple risk factors for CKD, but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.

Aim: To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate (L-eGFR < 60 mL/min per 1.

Uncovering a dose-response relationship between positive fecal immunochemical test (FIT) and all-cause, cardiovascular and cancer-related mortality.

Background: Fecal immunochemical test (FIT) is for colorectal cancer (CRC) screening. Its association with non-CRC mortality has been overlooked. Given the quantitative FIT values, its dose-response relationships with different causes of deaths and years of life shortened were assessed.

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

Background: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches.

Ethnic differences in the age-related distribution of serum prostate-specific antigen values: A study in a Taiwanese male population.

This study investigates age-specific prostate-specific antigen (PSA) distributions in Taiwanese men and recommends reference ranges for this population after comparison with other studies. From January 1999 to December 2016, a total of 213,986 Taiwanese men aged above 19 years old without history of prostate cancer, urinary tract infection, or prostate infection were recruited from the Taiwan MJ cohort, an ongoing prospective cohort of health examinations conducted by the MJ Health Screening Center in Taiwan. Participants were divided into seven age groups.

The optimal pulse pressures for healthy adults with different ages and sexes correlate with cardiovascular health metrics.

Background: Pulse pressure (PP) may play a role in the development of cardiovascular disease, and the optimal PP for different ages and sexes is unknown. In a prospective cohort, we studied subjects with favorable cardiovascular health (CVH), proposed the mean PP as the optimal PP values, and demonstrated its relationship with healthy lifestyles.

Methods And Results: Between 1996 and 2016, a total of 162,636 participants (aged 20 years or above; mean age 34.

"Sugar-Sweetened Beverages" Is an Independent Risk From Pancreatic Cancer: Based on Half a Million Asian Cohort Followed for 25 Years.

Although the link between sugar-sweetened beverages (SSB) and pancreatic cancer has been suggested for its insulin-stimulating connection, most epidemiological studies showed inconclusive relationship. Whether the result was limited by sample size is explored. This prospective study followed 491,929 adults, consisting of 235,427 men and 256,502 women (mean age: 39.

Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison.

Background: Fatty liver disease (FLD) arises from the accumulation of fat in the liver and may cause liver inflammation, which, if not well controlled, may develop into liver fibrosis, cirrhosis, or even hepatocellular carcinoma.

Objective: We describe the construction of machine-learning models for current-visit prediction (CVP), which can help physicians obtain more information for accurate diagnosis, and next-visit prediction (NVP), which can help physicians provide potential high-risk patients with advice to effectively prevent FLD.

Methods: The large-scale and high-dimensional dataset used in this study comes from Taipei MJ Health Research Foundation in Taiwan.

Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks.

Background: Cardiotocography is a common method of electronic fetal monitoring (EFM) for fetal well-being. Data-driven analyses have shown potential for automated EFM assessment. For this preliminary study, we used a novel artificial intelligence method based on fully convolutional networks (FCNs), with deep learning for EFM evaluation and correct recognition, and its possible role in evaluation of nonreassuring fetal status.

A mobile multi-agent information system for ubiquitous fetal monitoring.

Electronic fetal monitoring (EFM) systems integrate many previously separate clinical activities related to fetal monitoring. Promoting the use of ubiquitous fetal monitoring services with real time status assessments requires a robust information platform equipped with an automatic diagnosis engine. This paper presents the design and development of a mobile multi-agent platform-based open information systems (IMAIS) with an automated diagnosis engine to support intensive and distributed ubiquitous fetal monitoring.

Valproic acid enhances Oct4 promoter activity in myogenic cells.

Induced pluripotent stem (iPS) cells are reprogrammed from somatic cells through ectopic expression of stem cell-specific transcription factors, including Oct4, Nanog, Sox2, Lin28, Klf4, and c-Myc. Although iPS cells are similar to embryonic stem (ES) cells in their pluripotency, their inherited defects, such as insertion mutagenesis, employment of oncogenes, and low efficiency, associated with the reprogramming procedure have hindered their clinical application. A study has shown that valproic acid (VPA) treatment can significantly enhance the reprogramming efficiency and avoid the usage of oncogenes.

Changes to the meiotic spindle and zona pellucida of mature mouse oocytes following different cryopreservation methods.

This study is to investigate the change of morphology of the meiotic spindle and the extent of zona hardening relating to the morphological survival and developmental competence of thawed oocytes. Four- to 8-week-old female mice (C57BL/6) primed with an intraperitoneal injection of pregnant mare's serum gonadotropin and human chorionic gonadotropin. Cryopreserved oocytes using two protocols: vitrificaton using ethylene glycol (EG) and slow freezing using propanediol (PROH).

Painless ovarian torsion mimicking a uterine myoma.

Objective: Uterine myoma is the most common benign solid pelvic tumor seen in women and is easily demonstrated by pelvic ultrasonography. However, a chronic ovarian torsion with entire necrosis may be an exceptional mimicker. We herein present an unusual case of painless ovarian torsion similar to a subserous uterine myoma.