The ACH Heart Center is a comprehensive, full-service resource for infants and children with congenital and acquired heart disease. UAMS physicians and nurses and ACH nurses and staff are here to care for these children both directly and by providing support to pediatricians and primary care physicians within Arkansas and the surrounding states.
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Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach.
Risk Manag Healthc Policy
January 2025
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, 235603, Taiwan.
Purpose: As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF.
Patients And Methods: A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records.
Impact of Government Healthcare Policy Changes on Consumption and Human Movements During COVID-19: An Interrupted Time Series Analysis in Korea.
J Korean Med Sci
January 2025
Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea.
Background: The coronavirus disease 2019 (COVID-19) pandemic has altered daily behavioral patterns based on government healthcare policies, including consumption and movement patterns. We aimed to examine the extent to which changes in the government's healthcare policy have affected people's lives, primarily focusing on changes in consumption and population movements.
Methods: We collected consumption data using weekly credit card transaction data from the Hana Card Corporation and population mobility data using mobile phone data from SK Telecom in Seoul, South Korea.
An Overview of Patient-reported Outcomes for Men with Prostate Cancer: Results from the PIONEER Consortium.
Eur Urol Open Sci
January 2025
Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, The Netherlands.
Background And Objective: Patient-reported outcome measures (PROMs) are increasingly being used to capture the patients' perspective of their functional status and quality of life (QoL). Big data can help us better understand patient-reported outcomes (PROs). Using prospectively collected data from the Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) consortium, we aimed to describe the functional status and QoL in men with prostate cancer (PCa) treated with active surveillance (AS), radical prostatectomy (RP), and radiotherapy (RT), and to demonstrate the applicability of PROM data on a large scale and at a European level.
View Article and Find Full Text PDFIntegrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation.
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFExplainable unsupervised anomaly detection for healthcare insurance data.
BMC Med Inform Decis Mak
January 2025
Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Background: Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detecting waste and fraud. However, labeled data is costly and difficult to acquire as it requires expert investigators and known care providers with atypical behavior.
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