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Genomics & Precision Health Database|Non-Genomics Precision Health Update Archive|Public Health Genomics and Precision Health Knowledge Base (PHGKB) Published on 09/16/2021

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

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Non-Genomics Precision Health Scan

Birth Defects and Child Health

Understanding Pediatric Surgery Cancellation: Geospatial Analysis.
Liu Lei et al. Journal of medical Internet research 2021 23(9) e26231

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.
Sazawal Sunil et al. BMC pregnancy and childbirth 2021 21(1) 609

Cancer

Dermatologic Follow-up and Assessment of Suspicious Lesions.
Iacullo Julie et al. Clinics in plastic surgery 2021 48(4) 617-629

Developing and validating a prediction model for lymphedema detection in breast cancer survivors.
Wei Xiaoxia et al. European journal of oncology nursing : the official journal of European Oncology Nursing Society 2021 54102023

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning.
Zhao Sheng-Bing et al. World journal of gastroenterology 2021 27(31) 5232-5246

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
Haggenmüller Sarah et al. European journal of cancer (Oxford, England : 1990) 2021 156202-216

Ensemble based machine learning approach for prediction of glioma and multi-grade classification.
Chandra Joshi Rakesh et al. Computers in biology and medicine 2021 137104829

A deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison.
Nam Joon Yeul et al. Gastrointestinal endoscopy 2021

Improved diagnosis of thyroid cancer aided with deep learning applied to sonographic text reports: a retrospective, multi-cohort, diagnostic study.
Zhang Qiang et al. Cancer biology & medicine 2021

An Adversarial Deep-Learning-Based Model for Cervical Cancer CTV Segmentation With Multicenter Blinded Randomized Controlled Validation.
Liu Zhikai et al. Frontiers in oncology 2021 11702270

Automated clinical target volume delineation using deep 3D neural networks in radiation therapy of Non-small Cell Lung Cancer.
Xie Yunhe et al. Physics and imaging in radiation oncology 2021 19131-137

Chronic Disease

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning.
Li Qingqing et al. Frontiers in physiology 2021 12714195

The BAriatic surgery SUbstitution and nutrition (BASUN) population: a data-driven exploration of predictors for obesity.
Höskuldsdóttir Gudrún et al. BMC endocrine disorders 2021 21(1) 183

Machine learning models for decision support in epilepsy management: A critical review.
Smolyansky Eliot D et al. Epilepsy & behavior : E&B 2021 123108273

Glomerular Disease Classification and Lesion Identification by Machine Learning.
Yang Cheng-Kun et al. Biomedical journal 2021

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.
Stephens Mark E et al. Neurosurgical review 2021

Ethical, Legal and Social Issues (ELSI)

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.
Benjamens Stan et al. NPJ digital medicine 2021 3(1) 118

General Practice

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice.
Kulkarni Viraj et al. JMIR medical informatics 2021 9(9) e28776

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology.
Fiani Brian et al. Reviews in the neurosciences 2021

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning.
Tsai Jen-Yung et al. Frontiers in bioengineering and biotechnology 2021 9708137

Development of a clinical prediction rule for patients with cervical spinal cord injury who have difficulty in obtaining independent living.
Hori Tomonari et al. The spine journal : official journal of the North American Spine Society 2021

Acceptability, usefulness, and satisfaction with a web-based video-tailored physical activity intervention: The TaylorActive randomized controlled trial: Acceptability, usefulness and satisfaction with the TaylorActive randomised controlled trial.
Schoeppe Stephanie et al. Journal of sport and health science 2021

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort.
Koutsouleris Nikolaos et al. Biological psychiatry 2021

Identifying clinical risk factors correlate with suicide attempts in patients with first episode major depressive disorder.
Li Xiao-Yan et al. Journal of affective disorders 2021 295264-270

Heart, Lung, Blood and Sleep Diseases

The applications of eHealth technologies in the management of asthma and allergic diseases.
Alvarez-Perea Alberto et al. Clinical and translational allergy 2021 11(7) e12061

Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome.
Schwager E et al. NPJ digital medicine 2021 4(1) 133

Artificial Intelligence-Based 3D Angiography for Visualization of Complex Cerebrovascular Pathologies.
Lang S et al. AJNR. American journal of neuroradiology 2021

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension.
Buffolo Fabrizio et al. Hypertension (Dallas, Tex. : 1979) 2021 HYPERTENSIONAHA12117444

Stratifying the Risk of Cardiovascular Disease in Obstructive Sleep Apnea Using Machine Learning.
Gourishetti Saikrishna C et al. The Laryngoscope 2021

Multi-task deep learning for cardiac rhythm detection in wearable devices.
Torres-Soto Jessica et al. NPJ digital medicine 2021 3(1) 116

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation.

Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.
Nielsen Maximilian et al. Stroke 2021 STROKEAHA120033807

Infectious Diseases

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.
Rashidi Hooman H et al. Scientific reports 2021 11(1) 17900

Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models.

Balanced Convolutional Neural Networks for Pneumoconiosis Detection.
Hao Chaofan et al. International journal of environmental research and public health 2021 18(17)

Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome.
Yuan Siyi et al. Frontiers in medicine 2021 8720926

Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C.
Feldman Theodore C et al. International journal of medical informatics 2021 154104562


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.
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