Low antiviral uptake of nirmatrelvir/ritonavir and molnupiravir in adult patients with COVID-19 in Taiwan in 2022.

Background: Antivirals are effective in reducing hospitalisation and death in mild-to-moderate coronavirus 2019 (COVID-19) patients. We estimated the antiviral uptake of nirmatrelvir/ritonavir and molnupiravir in adult patients with a syndrome coronavirus 2 (SARS-CoV-2) infection during the Emergency Use Authorization (EUA) period in Taiwan.

Methods: A retrospective cohort study was conducted in Taiwan between January 2022 and December 2022.

Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.

Background: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.

Objective: This study aimed to develop a personalized predictive model, using artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with type 2 diabetes mellitus (T2DM) who are prescribed antidiabetic medications.

Methods: This retrospective multicenter study used data from the Taipei Medical University Clinical Research Database, which comprises electronic medical records from 3 hospitals in Taiwan.

Comprehensive Evaluation of the Cardiovascular Protective Effects of Sodium-Glucose Cotransporter 2 Inhibitors in Patients with Advanced Chronic Kidney Disease: A Real-World Evidence.

Introduction: Diabetes, kidney disease, and cardiovascular disease have complex interactions and coexistences that significantly worsen a patient's overall health. Previous research results have shown that SGLT2i hypoglycemic drugs can not only effectively control blood sugar in diabetic patients but also protect the kidneys and heart. This study further focuses on diabetic patients with kidney disease to explore the effectiveness of using SGLT2i hypoglycemic drugs in avoiding heart-related complications or death.

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study.

Background And Purpose: Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.

Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

Objective: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources.

Methods: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information.

Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

Introduction: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance.

Areas Covered: The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments.

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

Background: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5.

Methods: Retrospective electronic health record data from the Taipei Medical University clinical research database were used.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.

Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes.

Aims: The prevalence of Type 2 Diabetes Mellitus (T2DM) is projected to be 7 % in 2030. Despite its need for long-term diabetes care, the adherence rate of injectable medications such as insulin is around 60 %, lower than the acceptable threshold of 80 %. This study aims to create classification models to predict insulin adherence among adult T2DM naïve insulin users.

A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning.

Introduction: Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high-risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real-world database containing clinical features and employing numerous artificial intelligent approach algorithms.

Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases.

Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.

Durian Shell-Mediated Simple Green Synthesis of Nanocopper against Plant Pathogenic Fungi.

The synthesis of fungicides in eco-friendly and cost-effective ways is significantly essential for agriculture. Plant pathogenic fungi cause many ecological and economic issues worldwide, which must be treated with effective fungicides. Here, this study proposes the biosynthesis of fungicides, which combines copper and CuO nanoparticles (Cu/CuO) synthesized using durian shell (DS) extract as a reducing agent in aqueous media.

Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study.

Background: Psoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA.

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy.

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: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019.

Renin-Angiotensin-Aldosterone System Inhibitors and Development of Gynecologic Cancers: A 23 Million Individual Population-Based Study.

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 inhibitors use with gynecologic cancer risks. A large population-based case-control study was conducted from claim databases of Taiwan's Health and Welfare Data Science Center (2000-2016) and linked with Taiwan Cancer Registry (1979-2016).

Association between Proton Pump Inhibitor Use and the Risk of Female Cancers: A Nested Case-Control Study of 23 Million Individuals.

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 Data Science Center’s databases (2000−2016) and linked to pathologically confirmed cancer data from the Taiwan Cancer Registry (1979−2016).

Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival.

A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018.

Green Orange Peel-Mediated Bioinspired Synthesis of Nanoselenium and Its Antibacterial Activity against Methicillin-Resistant .

In this study, green orange peel (GOP) was feasibly evidenced in preparing selenium nanoparticles (SeNPs). Acting as reducing agents, polyphenolic compounds were extracted from GOP at the optimal extraction conditions (at 70 °C for 1.5 h, mass ratio of dried orange peel/distilled water of 5/100).