Publications by authors named "Phung-Anh Alex Nguyen"
Article Synopsis
- Hepatocellular carcinoma (HCC) is a major cancer that leads to high mortality rates globally, and early detection is key for better treatment outcomes.
- This study aims to create a deep learning model that uses electronic health record data to predict which patients are likely to be diagnosed with HCC within a year.
- The model was trained on data from nearly 48,000 individuals and demonstrated a high predictive accuracy, with an area under the receiver operating curve (AUROC) of 0.94 for predicting HCC risk one year in advance.
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 Health Insurance Research Database sample cohort of 1999 to 2013 in Taiwan.
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 reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs.
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- The study aimed to analyze pediatric drug use and prescription rates across South Korea, Hong Kong, Taiwan, Japan, and Australia, focusing on age and gender differences, while identifying the most commonly used drugs in each country.
- Data was collected from various healthcare databases covering children under 18 years from 2009 to 2013, and findings showed that inpatient prescriptions were highest for those under two years old, while outpatient prescriptions peaked in the 2-11 years age group.
- Results indicated that antibiotics were the most frequently dispensed medications, particularly for ambulatory patients, and highlighted a notable prevalence of mucolytic usage in East Asia, suggesting a need for further research into this trend.
Background And Aims: Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.
View Article and Find Full Text PDFPrediction of fatty liver disease using machine learning algorithms.
Comput Methods Programs Biomed
March 2019
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 that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD.
View Article and Find Full Text PDFImproving the quality healthcare through the efficient computer-aided prediction models.
Comput Methods Programs Biomed
August 2018
Objective: Firm conclusion about whether short and long-term gout medications use has an impact on cancer risk remain inconclusive. The aim of this study was to investigate the association between gout drugs use and risk of cancer.
Methods: We conducted a retrospective longitudinal population-based case-control study in Taiwan.
Objective: Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season.
Material And Methods: This study utilizes electronic health record data from 6 sites representing 10.
Cancer is a multifactorial disease, and imbalances of the immune response and sex-associated features are considered risk factors for certain types of cancer. The present study aimed to assess whether ankylosing spondylitis (AS), an immune disorder that predominantly affects young adult men, is associated with an increased risk of cancer. Using the Taiwan National Health Insurance Research Database, a cohort of patients diagnosed with AS between 2000 and 2008 who had no history of cancer prior to enrollment was established (n=5,452).
View Article and Find Full Text PDFViral warts (Human Papilloma Virus) as a potential risk for breast cancer among younger females.
Comput Methods Programs Biomed
June 2017
Introduction: There have been several reports on the role of human papillomavirus (HPV) in the etiology of breast cancer. To our knowledge, this is first study to use disease-disease association data-mining approach to analyzing viral warts and breast cancer to be conducted in Taiwanese population.
Materials And Methods: We analyzed the Taiwan's National Health Insurance database (NHIDM data comprising of 23 million patient data) to examine the association between viral warts and female breast carcinoma.
Article Synopsis
- * A study involving 1,198 participants in Taiwan showed that sending daily SMS reminders significantly reduced both medication delays (from 85% to 18%) and forgetfulness (from 46% to 5%) among those receiving reminders compared to a control group.
- * The majority of participants found the SMS reminders helpful for managing their health, with 92% willing to recommend the system to others, suggesting that timely reminders can lead to improved adherence and potential cost savings in healthcare.
Cancer-disease associations: A visualization and animation through medical big data.
Comput Methods Programs Biomed
April 2016
Article Synopsis
- The study aims to develop a tool called Cancer Associations Map Animation (CAMA) to visualize the connections between different cancers and other diseases over time, improving treatment customization for patients.
- The research utilized a vast dataset from Taiwan’s National Health Insurance Database covering 782 million outpatient visits, focusing on nine major cancers to quantify and display these associations using a motion chart.
- CAMA provides an animated, interactive view of cancer connections with other diseases based on age and gender, helping clinicians detect comorbidities earlier and potentially guiding better cancer treatment strategies.
Background: Electronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve.
Methods: We develop a standardized data analysis process to support cohort study with a focus on a particular disease.
Objective: The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization.
Materials And Methods: Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011.