Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing.
Sergio Rubio-Martín et al. Health Inf Sci Syst 2024 12(1) 20
Artificial Intelligence in Pediatrics: Learning to Walk Together.
Kaan Can Demirbas et al. Turk Arch Pediatr 2024 59(2) 121-130
Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.
Huijie Wang et al. Front Oncol 2024 141325514
Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology.
Kaan Ozbozduman et al. Sci Rep 2024 14(1) 5849
Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning.
Antonio Gallardo-Pizarro et al. Expert Rev Anti Infect Ther 2024 1-9
Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.
Lei Hu et al. Radiol Artif Intell 2024 e230362
Personalized cancer care can't rely on molecular testing alone.
James Larkin et al. Nature 2024 627(8002) 38
Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.
Zi-Mei Zhang et al. Sci Rep 2024 14(1) 5274
Systemic lupus in the era of machine learning medicine.
Kevin Zhan et al. Lupus Sci Med 2024 11(1)
Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.
Jun-Bo Tu et al. Sci Rep 2024 14(1) 5245
Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests.
Yanci Zhang et al. Sci Rep 2024 14(1) 5307
Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.
Laurens Topff et al. Eur Radiol 2024
Machine learning and the prediction of suicide in psychiatric populations: a systematic review.
Alessandro Pigoni et al. Transl Psychiatry 2024 14(1) 140
A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare.
Jana Fehr et al. Front Digit Health 2024 61267290
Clinical Practice Guidelines on using artificial intelligence and gadgets for mental health and well-being.
Vipul Singh et al. Indian J Psychiatry 2024 66(Suppl 2) S414-S419
Predicting the Climate Impact of Healthcare Facilities Using Gradient Boosting Machines.
Hao Yin et al. Clean Environ Syst 2024 12
Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction.
Xiang Zhu et al. BMC Med Res Methodol 2024 24(1) 59
Analyzing prehospital delays in recurrent acute ischemic stroke: Insights from interpretable machine learning.
Youli Jiang et al. Patient Educ Couns 2024 123108228
A robust framework for enhancing cardiovascular disease risk prediction using an optimized category boosting model.
Zhaobin Qiu et al. Math Biosci Eng 2024 21(2) 2943-2969
Using Social Listening for Digital Public Health Surveillance of Human Papillomavirus Vaccine Misinformation Online: Exploratory Study.
Dannell Boatman et al. JMIR Infodemiology 2024 4e54000
Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets.
Yiming Li et al. J Biomed Inform 2024 104621
Development of machine-learning models using pharmacy inquiry database for predicting dose-related inquiries in a tertiary teaching hospital.
Jungwon Cho et al. Int J Med Inform 2024 185105398
Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach.
J Xing et al. J Endocrinol Invest 2024
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About Scan
This update features emerging roles of big data science, machine learning, and predictive analytics across the life span. The scan focuses on various conditions including, birth defects, newborn screening, reproductive health, childhood diseases, cancer, chronic diseases, medication, family health history, guidelines and recommendations. The sweep also includes news, reviews, commentaries, tools and database.
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:Mar 18, 2024
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