Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Purpose: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver fibrosis in patients with severe obesity through noninvasive tests.

Materials And Methods: This prospective study included 194 patients with severe obesity who underwent wedge liver biopsy and metabolic bariatric surgery at Taipei Medical University Hospital between September 2016 and December 2020.

NR1H4 disease: rapidly progressing neonatal intrahepatic cholestasis and early death.

Background: Clinical studies on progressive familial intrahepatic cholestasis (PFIC) type 5 caused by mutations in NR1H4 are limited.

Methods: New patients with biallelic NR1H4 variants from our center and all patients from literature were retrospectively analyzed.

Results: Three new patients were identified to be carrying five new variants.

Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

Objective: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations.

Materials And Methods: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists.

Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data.

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another's facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction.

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.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts.

Adjustable Cuffless Smartphone Attachment (ACSA+) for Estimation of Blood Pressure Trends: A Pilot Study.

Among the elderly, hypertension remains one of the prevalent health conditions, which requires monitoring and intervention strategies. Nevertheless, regular reporting of blood pressure (BP) from these individuals still poses multiple challenges. However, most people own cell phone and are engaged in phone conversations daily.

Call for the responsible artificial intelligence in the healthcare.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare.

Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication.

Background And Objective: The overall benefits of using clinical decision support systems (CDSSs) can be restrained if physicians inadvertently ignore clinically useful alerts due to "alert fatigue" caused by an excessive number of clinically irrelevant warnings. Moreover, inappropriate drug errors, look-alike/sound-alike (LASA) drug errors, and problem list documentation are common, costly, and potentially harmful. This study sought to evaluate the overall performance of a machine learning-based CDSS (MedGuard) for triggering clinically relevant alerts, acceptance rate, and to intercept inappropriate drug errors as well as LASA drug errors.

Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions.

The current Objective Structured Clinical Examination (OSCE) is complex, costly, and difficult to provide high-quality assessments. This pilot study employed a focus group and debugging stage to test the Crowdsource Authoring Assessment Tool (CAAT) for the creation and sharing of assessment tools used in editing and customizing, to match specific users' needs, and to provide higher-quality checklists. Competency assessment international experts (n = 50) were asked to 1) participate in and experience the CAAT system when editing their own checklist, 2) edit a urinary catheterization checklist using CAAT, and 3) complete a Technology Acceptance Model (TAM) questionnaire consisting of 14 items to evaluate its four domains.

Relationship of Glucagon-like Peptide 1 and Peptide YY with Catch-up Growth in Children Born Small for Gestational Age.

Objective: Children born small for gestational age (SGA) are at a greater risk of developing insulin resistance, type 2 diabetes, and cardiovascular disease in adulthood. Gastrointestinal peptides, some secreted by intestinal L cells, regulate glucose and lipid metabolism and act on the hypothalamus to regulate energy homeostasis. The aim of this study was to explore whether gastrointestinal peptides are involved in metabolic disorders in SGA, which remains unclear.

Using alert dwell time to filter universal clinical alerts: A machine learning approach.

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.

Incidence and Mortality of Malignant Brain Tumors after 20 Years of Mobile Use.

(1) Objective: This population-based study was performed to examine the trends of incidence and deaths due to malignant neoplasm of the brain (MNB) in association with mobile phone usage for a period of 20 years (January 2000-December 2019) in Taiwan. (2) Methods: Pearson correlation, regression analysis, and joinpoint regression analysis were used to examine the trends of incidence of MNB and deaths due to MNB in association with mobile phone usage. (3) Results: The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB.

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.

New technology for preparing energetic materials by nanofiltration membrane (NF): rapid and efficient preparation of high-purity ammonium dinitramide (ADN).

The development of environment-friendly and non-toxic green energetic materials and their safe, environmentally friendly, and economical production is very important to the national economy and national security. As an innovative, efficient, and environmentally friendly energetic material, the preferred preparation method of ammonium dinitramide (ADN) is the nitro-sulfur mixed acid method, which has the advantages of high yield, simple method, and easy access to raw materials. However, the large number of inorganic salt ions introduced by this method limits the large-scale production of ADN.

Empowering Patients and Transforming Healthcare in the Post-COVID-19 Era: The Role of Digital and Wearable Technologies.

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...

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.

Synthesis and characterization of potential polycyclic energetic materials using bicyclic triazole and azetidine structures as building blocks.

Exploring the design strategy of new energetic materials is crucial to promote the development of energetic materials. In this study, a method for designing polycyclic energetic materials is proposed by combining the azetidine structure with azobis-1,2,4-triazole or bi-1,2,4-triazole. A series of typical triazolyl polycyclic compounds were designed and synthesized by simple nucleophilic reaction, which included 5,5'-dichloro-3,3'-bis(3,3'-difluoroazetidine)-4,4'-azobis-1,2,4-triazole (1), 5,5'-dichloro-3,3'-bis(3,3'-difluoroazetidine)-4,4'-bi-1,2,4-triazole (2), 5,5'-dichloro-3-(,-dimethyl)-3'-(3,3'-difluoroazetidine)-4,4'-bi-1,2,4-triazole (3) 5,5'-dichloro-3,3'-bis(3,3'-dinitroazetidine)-4,4'-bi-1,2,4-triazole (4), 5,5'-dichloro-3-(,-dimethyl)-3'-(3,3'-dinitroazetidine)-4,4'-bi-1,2,4-triazole (5), and 5,5'-diazido-3,3'-bis(3,3'-difluoroazetidine)-4,4'-azo-1,2,4-triazole (6).

Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis.

Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience.

Proton Pump Inhibitors Use and the Risk of Pancreatic Cancer: Evidence from Eleven Epidemiological Studies, Comprising 1.5 Million Individuals.

Previous epidemiological studies have shown that proton pump inhibitor (PPI) may modify the risk of pancreatic cancer. We conducted an updated systematic review and meta-analysis of observational studies assessing the effect of PPI on pancreatic cancer. PubMed, Embase, Scopus, and Web of Science were searched for studies published between 1 January 2000, and 1 May 2022.