Hsuan-Chia Yang

Purpose: Medication errors such as potential inappropriate prescriptions would induce serious adverse drug events to patients. Information technology has the ability to prevent medication errors; however, the pharmacology of traditional Chinese medicine (TCM) is not as clear as in western medicine. The aim of this study was to apply the appropriat...

For locally advanced head and neck squamous cell carcinoma (HNSCC), therapeutic decisions depend on comorbidity or age. We estimated the treatment outcomes of patients with different Charlson comorbidity index (CCI) scores and ages to determine whether aggressive treatment improves survival.Data from the Taiwan National Health Insurance and cancer...

The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient’s medical records. In response, deep learning (DL) methods have been developed to a...

Background: Alerts in computerized physician order entry (CPOE) systems can improve patient safety. However, alerts in rule-based systems cannot be customized based on individual patient or user characteristics. This limitation can lead to the presentation of irrelevant alerts and subsequent alert fatigue. Objective: We used machine learning app...

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods:...

The chronic receipt of renin-angiotensin-aldosterone system (RAAS) inhibitors including angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been assumed to be associated with a significant decrease in overall gynecologic cancer risks. This study aimed to investigate the associations of long-term RAAS inhib...

Objective: The popularity of the "bring your own device (BYOD)" concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period. Methods: We used the technology acceptance model to develop and validat...

Background: Firm conclusions about whether long-term proton pump inhibitor (PPI) drug use impacts female cancer risk remain controversial. Objective: We aimed to investigate the associations between PPI use and female cancer risks. Methods: A nationwide population-based, nested case-control study was conducted within Taiwan’s Health and Welfare Dat...

Background Several epidemiological studies have shown that psoriasis increases the risk of developing atrial fibrillation but evidence of this is still scarce. Aims Our objective was to systematically review, synthesise and critique the epidemiological studies that provided information about the relationship between psoriasis and atrial fibrillati...

Proton pump inhibitors (PPIs) are used for maintaining or improving gastric problems. Evidence from observational studies indicates that PPI therapy is associated with an increased risk of gastric cancer. However, the evidence for PPIs increasing the risk of gastric cancer is still being debated. Therefore, we aimed to investigate whether long-term...

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers prof...

Currently, the International Classification of Diseases (ICD) codes are being used to improve clinical, financial, and administrative performance. Inaccurate ICD coding can lower the quality of care, and delay or prevent reimbursement. However, selecting the appropriate ICD code from a patient’s clinical history is time-consuming and requires exper...

Background and Objective : Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination camp...

A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis...

Despite previous studies on statins, aspirin, metformin, and angiotensin-converting-enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), little has been studied about all their possible combinations for chemoprevention against cancers. This study aimed to comprehensively analyze the composite chemopreventive effects of all the combina...

Background Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. Objective The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health...

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Background and objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-in...

Background: Artificial intelligence approaches can integrate complex features and can be used to predict a patient's risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. Objective: The aim of this study was to use electronic medical records to prescreen patients who are at risk of de...

Immune checkpoint inhibitors (ICIs) have been approved to treat patients with various cancer types, including lung cancer, in many countries. This study aims to investigate the effectiveness and safety of ICIs under different treatment conditions of non-small cell lung cancer patients. A population-based retrospective cohort study was conducted usi...

Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians spend more...

Levothyroxine is a widely prescribed medication for the treatment of an underactive thyroid. The relationship between levothyroxine use and cancer risk is largely underdetermined. To investigate the magnitude of the possible association between levothyroxine use and cancer risk, this retrospective case‐control study was conducted using Taiwan’s Hea...

Background: The Coronavirus Disease 2019 (COVID-2019) outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While using swabs from patients is the main way for detecting coronavirus, analyzing chest images could offer an alternative to hospitals where healthcare personnel and testing kits are scarce. Deep learning, in p...

Background and Objective : Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modi...

UNSTRUCTURED We propose the idea of using an open dataset of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patient emotions so they can reach out the patient through empathic care. However, face recognition datasets are often difficult to acquire...

Background After two months of implementing a partial lockdown, the Indonesian government had announced the “New Normal” policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding public perception through effective risk communica...

Background Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial. Objective We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs. MethodsA nationwide population-based, nested, case-control study was conducted within the National Health I...

Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analy...

BACKGROUND As the obesity rate continues to rise persistently, there is an urgent need to develop an effective weight-loss management strategy. Nowadays, the development of Artificial Intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offe...

We aimed to develop deep learning models for the prediction of the risk of advanced nonmelanoma skin cancer (NMSC) in Taiwanese adults. We collected the data of 9494 patients from Taiwan National Health Insurance data claim from 1999 to 2013. All patients’ diseases and medications were included in the development of the convolution neural network (...

BACKGROUND Artificial intelligence can integrate complex features and may be used to predict the risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. OBJECTIVE Using electronic medical records to pre-screening patient’s risk for developing lung cancer. METHODS Two million participants...

Coronavirus disease 2019 (COVID-19) characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created serious concerns about its potential adverse effects. There are limited data on clinical, radiological, and neonatal outcomes of pregnant women with COVID-19 pneumonia. This study aimed to assess clinical manifestations and...

Background Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies report...

Background Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains under...

BACKGROUND Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains under...

Statins have shown beneficial treatment as chemotherapy and target‐therapy for lung cancer. This study aims to investigate the effectiveness of statins in combination with epidermal growth factor receptor‐ tyrosine kinase inhibitors (EGFR‐TKIs) therapy on resistance and mortality of lung cancer patients. A population‐based cohort study was conducte...

We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included...

We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

BACKGROUND The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in pa...

BACKGROUND Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial. OBJECTIVE We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs. METHODS A nationwide population-based, nested, case-control study was conducted within the National Healt...

Purpose: Proton pump inhibitors (PPIs), one of the most widely used medications, are commonly used to suppress several acid-related upper gastrointestinal disorders. Acid-suppressing medication use could be associated with increased risk of community-acquired pneumonia (CAP), although the results of clinical studies have been conflicting. Data so...

BACKGROUND Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. OBJECTIVE The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health...

BACKGROUND Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies report...

Background and objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enab...

Background and Aims: Statins are the first-line medication to treating hypercholesterolemia. Several studies have investigated the impact of statins on the risk of hepatocellular carcinoma (HCC). However, the extent to which statins may prevent HCC remains uncertain. Therefore, we performed a meta-analysis of relevant studies to quantify the magnit...

Résumé Objectif La fracture de la hanche est l’une des principales causes de handicap, de dépenses, de morbidité et de mortalité. Plusieurs études ont décrit une association entre la prise de benzodiazépines (BZD) et un risque accru de fracture de la hanche chez les personnes âgées. L’objectif de cette étude était d’évaluer l’ampleur du risque de...

BACKGROUND The automatic segmentation of skin lesions has been reported using the data of dermoscopic images. It is, however, not applicable to real-time detection using a smartphone. OBJECTIVE This study aims to examine a deep learning model for detecting and localizing positions of the mole on the captured images to precisely extract the crop im...

Background: A potential evidence from previous epidemiological studies remains conflicting findings regarding the association between atrial fibrillation (AF) and dementia risk. We, therefore, carried out a meta-analysis of relevant studies to investigate the magnitude of the association between AF and dementia risk. Methods: We performed a systema...

Objective: Hip fracture is one of the leading causes of disability, cost, morbidity, and mortality. Several studies reported that benzodiazepines (BDZs) have been associated with an increased risk of hip fracture in older individuals. The aim of this study was to evaluate the magnitude of hip fracture risk with BDZs. Methods: A systematic litera...

Background and aims: The impact of statin on dementia risk reduction has been a subject of debate over the last decade, but the evidence remains inconclusive. Therefore, we performed a meta-analysis of relevant observational studies to quantify the magnitude of the association between statin therapy and the risk of dementia. Methods: We systemat...

Antidiabetic medications are commonly used around the world, but their safety is still unclear. The aim of this study was to investigate whether long-term use of insulin and oral antidiabetic medications is associated with cancer risk. We conducted a well-designed case–control study using 12 years of data from Taiwan's National Health Insurance Res...

We performed a cohort study to quantify the association between rheumatic arthritis (RA) and acute myocardial infarction (AMI) risk. ICD-9 was used to identify AMI and RA patients, and the Cox proportional hazards model with adjusted confounding factors was used to quantify the risk. The overall risk of AMI for RA patients was an aHR of 1.05 (95% C...

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (...

The demand for AI to improve patients outcome has been increased; we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under r...

Background The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about pat...

BACKGROUND The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about pat...

Résumé Objectif Aucune conclusion définitive n’a été tirée à ce jour concernant le risque de cancer lié aux traitements de la goutte administrés à court et long terme. Cette étude avait pour objectif d’évaluer l’association entre l’utilisation des traitements antigoutteux et le risque de cancer. Méthodes Nous avons mené une étude rétrospective lon...

Background: Psoriasis, a common chronic inflammatory disease, increases the risk of developing multiple sclerosis (MS), but evidence for this outcome is still unclear. However, we performed a meta-analysis of relevant studies to quantify the magnitude of the association between psoriasis and MS. It will help to assess the current state of knowledg...

Study objective: Sepsis is a common and major health crisis in hospitals globally. An innovative and feasible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments and minimize the diagnostic uncertainty. Machine learning models could help to identify potential cli...

Background and objective: Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD th...