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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 04/22/2021

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

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation.
Zhou Huaqiong et al. Australian health review : a publication of the Australian Hospital Association 2021

Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh.
Khan Jahidur Rahman et al. Informatics for health & social care 2021 1-18

Cancer

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.
Tabatabaei Mohsen et al. Oncology 2021 1-11

Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks.
Jiang Beibei et al. European radiology 2021

Impact of image compression on deep learning-based mammogram classification.
Jo Yong-Yeon et al. Scientific reports 2021 11(1) 7924

Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions.
Deng Yan et al. Frontiers in oncology 2021 11620981

Development of a Novel Prognostic Model for Predicting Lymph Node Metastasis in Early Colorectal Cancer: Analysis Based on the Surveillance, Epidemiology, and End Results Database.
Ahn Ji Hyun et al. Frontiers in oncology 2021 11614398

Interpretable survival prediction for colorectal cancer using deep learning
E Wulczyn et al, NPJ Digital Medicine, April 19, 2021

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. We developed a deep learning system for predicting survival for stage II and III colorectal cancer using 3652 cases. When evaluated on two validation datasets containing 1239 cases and 738 cases, respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 and 0.69 and added significant predictive value to a set of nine clinicopathologic features.

Predicting breast cancer 5-year survival using machine learning: A systematic review.
Li Jiaxin et al. PloS one 2021 16(4) e0250370

Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.
O'Shea Robert J et al. European radiology 2021

Application of logistic regression and convolutional neural network in prediction and diagnosis of high-risk populations of lung cancer.
Yuan Huijie et al. European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) 2021

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).
Gurbani Sidharth et al. Abdominal radiology (New York) 2021

General Practice

Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis.
Bittar André et al. JMIR medical informatics 2021 9(4) e22397

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.
Borges do Nascimento Israel Júnior et al. Journal of medical Internet research 2021 23(4) e27275

Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov.
Liu Guina et al. Frontiers in medicine 2021 8634197

Medical image analysis based on deep learning approach.
Puttagunta Muralikrishna et al. Multimedia tools and applications 2021 1-34

Can synthetic data be a proxy for real clinical trial data? A validation study.
Azizi Zahra et al. BMJ open 2021 11(4) e043497

Automated caries detection with smartphone color photography using machine learning.
Duong Duc Long et al. Health informatics journal 2021 27(2) 14604582211007530

Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation.
Kim Kipyo et al. Journal of medical Internet research 2021 23(4) e24120

Machine learning prediction of blood alcohol concentration: a digital signature of smart-breathalyzer behavior
K Aschbacher et al, NPJ Digital Medicine, April 20, 2021

We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals’ breath alcohol concentration (BrAC) levels trained on data from a smart breathalyzer. We analyzed roughly one million datapoints from 33,452 users of a commercial smart-breathalyzer device, collected between 2013 and 2017. For validation, we analyzed the associations between state-level observed smart-breathalyzer BrAC levels and impaired-driving motor vehicle death rates.

Heart, Lung, Blood and Sleep Diseases

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients.
Morey Jacob R et al. Cerebrovascular diseases (Basel, Switzerland) 2021 1-6

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field.
Sverdlov Oleksandr et al. Contemporary clinical trials 2021 106397

A deep learning approach for the automatic recognition of prosthetic mitral valve in echocardiographic images.
Vafaeezadeh Majid et al. Computers in biology and medicine 2021 133104388

Infectious Diseases

Using Artificial Intelligence in Fungal Lung Disease: CPA CT Imaging as an Example.
Angelini Elsa et al. Mycopathologia 2021

Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
S Bhatia et al, NPJ Digital Medicine, April 16, 2021

Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.
Badawi Alaa et al. Journal of public health research 2021 10(1)

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China.
Yin Yi et al. BioMed research international 2021 20216696041

Diagnostic performance of artificial intelligence model for pneumonia from chest radiography.
Kwon TaeWoo et al. PloS one 2021 16(4) e0249399


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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