About Non-Genomics Precision Health Scan
This update features emerging roles of big data science, machine learning, and predictive analytics across the life span. The scan focus 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. View Data Selection Criteria
Non-Genomics Precision Health Scan
The Effectiveness of Artificial Intelligence in Assisting Mothers with Assessing Infant Stool Consistency in a Breastfeeding Cohort Study in China.
Jieshu Wu et al. Nutrients 2024 16(6)
A prediction model based on random survival forest analysis of the overall survival of elderly female papillary thyroid carcinoma patients: a SEER-based study.
Yuqiang Lun et al. Endocrine 2024
Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review.
Simran Uppal et al. Int J Med Inform 2024 186105421
Early detection of nasopharyngeal carcinoma through machine-learning-driven prediction model in a population-based healthcare record database.
Jeng-Wen Chen et al. Cancer Med 2024 13(7) e7144
Men's sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics - A focus group study.
Emilie Hybertsen Lysø et al. Soc Sci Med 2024 347116771
Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.
Cristian-Constantin Volovat et al. Diagnostics (Basel) 2024 14(6)
Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts.
Isabel D Friesner et al. JAMA Oncol 2024
Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models.
Seeta Devi et al. Asian Pac J Cancer Prev 2024 25(3) 1077-1085
Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.
Moritz Gross et al. Eur Radiol 2024
Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population.
Shiming Yang et al. Sci Rep 2024 14(1) 7618
Harnessing Big Data in Amyotrophic Lateral Sclerosis: Machine Learning Applications for Clinical Practice and Pharmaceutical Trials.
Ee Ling Tan et al. J Integr Neurosci 2024 23(3) 58
Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.
Feng He et al. J Med Internet Res 2024 26e41065
Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions.
Taiane de Azevedo Cardoso et al. JMIR Ment Health 2024 11e58631
A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.
Sanaz Vahdati et al. J Imaging Inform Med 2024
Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research.
Kai Sun et al. J Crit Care 2024 82154792
Participant flow diagrams for health equity in AI.
Jacob G Ellen et al. J Biomed Inform 2024 152104631
Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.
Santiago Papini et al. JAMA Psychiatry 2024
Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review.
Lisa C Lindley et al. Comput Inform Nurs 2024
Machine learning in mental health and its relationship with epidemiological practice.
Marcos DelPozo-Banos et al. Front Psychiatry 2024 151347100
Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence.
Sulaiman Somani et al. NPJ Digit Med 2024 7(1) 83
The Hypotension Prediction Index is equally effective in predicting intraoperative hypotension during non-cardiac surgery compared to a mean arterial pressure threshold: a prospective observational study.
Marijn P Mulder et al. Anesthesiology 2024
Echocardiographic Detection of Regional Wall Motion Abnormalities using Artificial Intelligence Compared to Human Readers.
Jeremy A Slivnick et al. J Am Soc Echocardiogr 2024
The Role of Artificial Intelligence in Cardiac Imaging.
Carlotta Onnis et al. Radiol Clin North Am 2024 62(3) 473-488
PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.
Giorgio Colangelo et al. Stroke 2024
Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS.
Rayhan Erlangga Rahadian et al. Resusc Plus 2024 18100606
Application of machine learning in predicting frailty syndrome in patients with heart failure.
Remigiusz Szczepanowski et al. Adv Clin Exp Med 2024 33(3) 309-315
Machine learning to identify a composite indicator to predict cardiac death in ischemic heart disease.
Alessandro Pingitore et al. Int J Cardiol 2024 131981
Machine learning prediction of adolescent HIV testing services in Ethiopia.
Melsew Setegn Alie et al. Front Public Health 2024 121341279
Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction.
Lars Palmowski et al. PLoS One 2024 19(3) e0300739
COVID-19 outbreaks surveillance through text mining applied to electronic health records.
Hermano Alexandre Lima Rocha et al. BMC Infect Dis 2024 24(1) 359
Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.
Moamen M Soliman et al. Biomed Signal Process Control 2024 92
Equitable Artificial Intelligence in Obstetrics, Maternal-Fetal Medicine, and Neonatology.
Ryan M McAdams et al. Obstet Gynecol 2024
Predictive value of ultrasonic artificial intelligence in placental characteristics of early pregnancy for gestational diabetes mellitus.
Huien Zhou et al. Front Endocrinol (Lausanne) 2024 151344666
Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.
Yifan Liu et al. J Am Med Inform Assoc 2024
Disclaimer: Articles listed in Non-Genomics Precision Health Scan are selected by the CDC Office of Genomics and Precision Public Health to provide current awareness of the scientific 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 Clips, 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
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