Emily Chia-Yu Su

Taipei Medical University | TMU · Graduate Institute of Biomedical Informatics

Background Existing proposed pathogenesis for preeclampsia (PE) was only applied for early onset subtype and did not consider pre-pregnancy and competing risks. We aimed to decipher PE subtypes by identifying related transcriptome that represents endometrial maturation and histologic chorioamnionitis. Methods We utilized eight arrays of mRNA expre...

Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the “Daily Situation Report” provided by...

Background and Objective: Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstra...

The 15-item Geriatric Depression Scale (GDS-15) is widely used to screen for depressive symptoms among older populations. This study aimed to develop and validate a questionnaire-free, machine-learning model as an alternative triage test for the GDS-15 among community-dwelling older adults. The best models were the random forest (RF) and deep-insig...

Preventive policies and mobility restrictions are believed to work for inhibiting the growth rate of COVID-19 cases; however, their effects have rarely been assessed and quantified in Southeast Asia. We aimed to examine the effects of the government responses and community mobility on the COVID-19 pandemic in Southeast Asian countries. The study ex...

Background: A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that rep...

Background A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that repre...

Introduction Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF. Material...

Acute hepatopancreatic necrosis disease (AHPND) in shrimp is caused by Vibrio strains that harbor a pVA1-like plasmid containing the pirA and pirB genes. It is also known that the production of the PirA and PirB proteins, which are the key factors that drive the observed symptoms of AHPND, can be influenced by environmental conditions and that this...

Interleukin (IL)-10 is a homodimer cytokine that plays a crucial role in suppressing inflammatory responses and regulating the growth or differentiation of various immune cells. However, the molecular mechanism of IL-10 regulation is only partially understood because its regulation is the environment or cell type-specific. In this study, we develop...

Introduction Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients. Methods and materials Data of 1...

BACKGROUND Given the ongoing coronavirus disease 2019 (COVID-19) pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to incre...

Background: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. Howev...

This protocol aims to develop, validate, and deploy a prediction model using high dimensional data by both human and machine learning. The applicability is intended for clinical prediction in healthcare providers, including but not limited to those using medical histories from electronic health records. This protocol applies diverse approaches to i...

This protocol aims to develop, validate, and deploy a prediction model using high dimensional data by both human and machine learning. The applicability is intended for clinical prediction in healthcare providers, including but not limited to those using medical histories from electronic health records. This protocol applies diverse approaches to i...

This protocol aimed to describe data transformation procedure of medical histories from electronic health records (EHRs) to historical rates by Kaplan-Meier (KM) estimation. The applicability is to extract features from real-world, time-varying data of EHRs, for developing but not limited to a machine learning prediction model. By this extraction t...

We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can impro...

We proposed a learning algorithm for human to conduct literature and data mining for causal factor discovery. The applicability is to select features for a machine learning prediction model, including but not limited to that using real-world, time-varying data from electronic health records. This protocol is relatively quick to find potentially act...

We aimed to provide a framework that organizes internal properties of a convolutional neural network (CNN) model using non-image data to be interpretable by human. The interface was represented as ontology map and network respectively by dimensional reduction and hierarchical clustering techniques. The applicability is to implement a prediction mod...

We aimed to provide a framework that organizes internal properties of a convolutional neural network (CNN) model using non-image data to be interpretable by human. The interface was represented as ontology map and network respectively by dimensional reduction and hierarchical clustering techniques. The applicability is to implement a prediction mod...

We proposed a learning algorithm for human to conduct literature and data mining for causal factor discovery. The applicability is to select features for a machine learning prediction model, including but not limited to that using real-world, time-varying data from electronic health records. This protocol is relatively quick to find potentially act...

This protocol aimed to describe data transformation procedure of medical histories from electronic health records (EHRs) to historical rates by Kaplan-Meier (KM) estimation. The applicability is to extract features from real-world, time-varying data of EHRs, for developing but not limited to a machine learning prediction model. By this extraction t...

We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can impro...

Background and Objective Emergency physicians (EPs) frequently deal with abdominal pain, including that is caused by either gallstones or acute cholecystitis. Easy access and low cost justify point-of-care ultrasound (POCUS) use as a first-line test to detect these diseases; yet, the detection performance of POCUS by EPs is unreliable, causing misd...

Background Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular organisms, and are involved in the first line of defense function. Recent studies showed that AMPs perpetuate great potential that is not limited to antimicrobial activity. They are also crucial regulato...

Background: The prevalence of nonalcoholic fatty liver disease is increasing over time worldwide, with similar trends to those of diabetes and obesity. A liver biopsy, the gold standard of diagnosis, is not favored due to its invasiveness. Meanwhile, noninvasive evaluation methods of fatty liver are still either very expensive or demonstrate poor...

Objective Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. Hence, this study identified COVID-19 clustering and defined the predictability performance of Google RSV models in cl...

Importance Prognostic predictions of prelabor rupture of membranes lack proper sample sizes and external validation. Objective To develop, validate, and deploy statistical and/or machine learning prediction models using medical histories for prelabor rupture of membranes and the time of delivery. Design A retrospective cohort design within 2-year...

The purpose of this paper was to compare the relative efficiency of COVID-19 transmission mitigation among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). The mitigation efficiency for each country was evaluated at each stage by usin...

[This corrects the article DOI: 10.1371/journal.pone.0247597.].

Maternal nutrition intake during pregnancy may affect the mother-to-child transmission of bacteria, resulting in gut microflora changes in the offspring, with long-term health consequences in later life. Longitudinal human studies are lacking, as only a small amount of studies showing the effect of nutrition intake during pregnancy on the gut micro...

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Th...

Public health agencies have suggested nonpharmaceutical interventions to curb the spread of the COVID-19 infections. The study intended to explore the information-seeking behavior and information needs on preventive measures for COVID-19 in the Philippine context. The search interests and related queries for COVID-19 terms and each of the preventiv...

Background: Preclinical studies have demonstrated that hyperoxia disrupts the intestinal barrier, changes the intestinal bacterial composition, and injures the lungs of newborn animals. Objectives: The aim of the study was to investigate the effects of hyperoxia on the lung and intestinal microbiota and the communication between intestinal and l...

This study aimed to investigate the possible incidence of visual light perceptions (VLPs) during radiation therapy (RT). We analyzed whether VLPs could be affected by differences in the radiation energy, prescription doses, age, sex, or RT locations, and whether all VLPs were caused by radiation. From November 2016 to August 2018, a total of 101 pa...

Background: Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients....

NSCLC (non-small cell lung cancer) is a leading cause of cancer-related deaths worldwide. Clinical trials showed that Hiltonol, a stable dsRNA representing an advanced form of polyI:C (polyinosinic-polycytidilic acid), is an adjuvant cancer-immunomodulator. However, its mechanisms of action and effect on lung cancer have not been explored pre-clini...

Given the increasing burden of chronic diseases in Indonesia, characteristics of chronic multimorbidities have not been comprehensively explored. Therefore, this research evaluated chronic multimorbidity patterns among Indonesians using Indonesian National Health Insurance (INHI) sample data. We included 46 chronic diseases and analyzed their distr...

We greatly appreciate Idrovo’s comments on our research and wish to specifically respond to his comments. Idrovo indicates that rapid increases in the number of confirmed cases in the past few weeks were observed in Latin America, where some countries had implemented stringent lockdowns for more than three months since the second half of March 2020...

Background South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis...

The purpose of this paper is to compare the relative mitigation efficiency of COVID-19 transmission among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). This paper evaluated the mitigation efficiency for each country at each stage b...

The initiation and progression of breast cancer (BRCA) is associated with inflammation and immune-overactivation, which is critically modulated by the E3 ubiquitin ligase. However, the underlying mechanisms and key factors involved in BRCA formation and disease advancement remains under-explored. By retrospective studies of BRCA patient tissues; an...

The major purpose of this paper was to examine the transmission of COVID-19 and the associated factors that affect the transmission. A qualitative analysis was conducted by comparing the COVID-19 transmission of six countries: China, Korea, Japan, Italy, the USA, and Brazil. This paper attempted to examine the mitigation effectiveness for the trans...

Background: Newborns with respiratory disorders often require supplemental oxygen. Preclinical studies have demonstrated that hyperoxia disrupts the intestinal barrier, impairs intestinal function, and injures the lungs of newborn animals. The effects of neonatal hyperoxia on intestinal and lung microbiota and the role of the intestinal microbiota...

Administrative claim data is believed as one of the promising data set to augment the mandatory surveillance system which suffered from under-reporting and delay in reporting. Therefore, this study aims to examine whether the Indonesian National Health Insurance (INHI) sample data could complement dengue case-based surveillance system in a more pra...

The initiation and progression of breast cancer (BRCA) is associated with inflammation and immune-overactivation. The E3 ubiquitin ligase is known to subtly balance immune-overactivation and pro-tumorigenesis. Here, by global transcriptional profiling of BRCA patient tissues, we identified a signature expression profile of F-box factors, of which F...

BACKGROUND South Korea is among the best-performing countries to tackle the coronavirus pandemic utilizing massive drive-through tests and facemasks, as well as extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impact of disease spread during crisis...

This study aimed to explore the patterns of community health risk perception of coronavirus disease 2019 (COVID-19) in South Korea using Internet search data. Google and NAVER relative search volume data were collected using COVID-19-related terms in Korean language. Online queries were compared with the number of new COVID-19 cases and tests. Time...

Background: Emergency treatments determined by emergency physicians may affect mortality and patient satisfaction. This paper attempts to examine the impact of patient characteristics, health status, the accredited level of hospitals, and triaged levels on the following emergency treatments: immediate life-saving interventions (LSIs), computed tomo...

Background We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia. Methods The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) fro...

Background: Preeclampsia and intrauterine growth restriction are placental dysfunction-related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed fr...

Objective An emerging outbreak of COVID-19 has been detected in at least 26 countries worldwide. Given this pandemic situation, robust risk communication is urgently needed particularly in affected countries. Therefore, this study explored the potential use of Google Trends (GT) to monitor public restlessness toward COVID-19 epidemic infection in T...

Objective: To develop artificial neural network (ANN)-based functional outcome prediction models for patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis based on immediate pretreatment parameters. Methods: The derived cohort consisted of 196 patients with AIS treated with intravenous thrombolysis between 2009 and 2017 at...

Abstract Background Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance...

BACKGROUND Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. OBJECTIVE This study aims to review and compare the predictive performances between logistic regression (LR) and other machine lea...

BACKGROUND Preeclampsia and intrauterine growth restriction are placental dysfunction–related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed from...

Nowadays, social media is often being used by users to create public messages related to their health. With the increasing number of social media usage, a trend has been observed of users creating posts related to adverse drug reactions (ADR). Mining social media data for these information can be used for pharmacological post-marketing surveillance...

In recent decades, many researchers have focused on the issue of medical failures in the healthcare industry. A variety of techniques have been employed to assess the risk of medical failure and to generate strategies to reduce the frequency of medical failures. Considering the limitations of the traditional method—failure mode and effects analysis...

Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-r...

Sedentary behaviors and dietary intake are independently associated with obesity risk. In the literature, only a few studies have investigated gender differences for such associations. The present study aims to assess the association of sedentary behaviors and unhealthy foods intake with obesity in men and women in a comparative manner. The analysi...

Prior studies primarily suggest that the generation of medical wastes maintains a positive relationship with national incomes. In contrast, higher incomes may lead to improved health status, consequently resulting in lower generation of medical wastes. Under this circumstance, an inverted U-shaped curve may be used to describe the generation of med...

Big data is concerned with all kinds of sources, that the majority of it comes from unstructured sources. Social media constitutes the biggest source of unstructured sources for big data. It has also been demonstrated that implications of public health research could be discovered from mining of social media data. Several studies have developed app...

In this study, we describe our methods to automatically classify Twitter posts describing events of adverse drug reaction and medication intake. We developed classifiers using linear support vector machines (SVM) and Naïve Bayes Multinomial (NBM) models. We extracted features to develop our models and conducted experiments to examine their effectiv...

Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3 days following a hospital admission. It has also been proven that mental disorders may be...

Ensemble methods are learning algorithms that classify new data points by synthesizing the predictions of a set of classifiers. Many methods for constructing ensembles have been proposed such as weighted voting, manipulations of training samples, features, or labels. The paper proposes a novel ensemble algorithm which constructs ensembles by manipu...

Background The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can res...

BioC is a simple XML format for text, annotations and relations, and was developed to achieve interoperability for biomedical text processing. Following the success of BioC in BioCreative IV, the BioCreative V BioC track addressed a collaborative task to build an assistant system for BioGRID curation. In this paper, we describe the framework of the...

Objectives: To determine whether periodontitis is a modifiable risk factor for dementia. Design: Prospective cohort study. Setting: National Health Insurance Research Database in Taiwan. Participants: Individuals aged 65 and older with periodontitis (n = 3,028) and an age- and sex-matched control group (n = 3,028). Measurements: Individual...