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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 02/25/2021

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

Artificial intelligence to enhance the evaluation of refractory epilepsies.
Bonilha Leonardo et al. Epilepsy & behavior : E&B 2021 Feb 107776

Cancer

AI-aided detection of malignant lesions in mammography screening - evaluation of a program in clinical practice.
Johansson Greta et al. BJR open 2021 3(1) 20200063

Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning.
Chen Mingyu et al. NPJ precision oncology 2020 Jun 4(1) 14

Chronic Disease

Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes.
Xia Shu-Jie et al. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2021 Feb 137111367

A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification.
Zhang Jie et al. Magnetic resonance imaging 2021 Feb

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.
Varrecchia Tiwana et al. PloS one 2021 16(2) e0244396

Comparison of an Artificial Intelligence-Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis: A Randomized Clinical Trial.
Jayakumar Prakash et al. JAMA network open 2021 Feb 4(2) e2037107

This randomized clinical trial at a single US academic orthopedic practice included 129 new adult patients presenting for OA-related knee pain from March 2019 to January 2020. Data were analyzed from April to May 2020.Patients were randomized into a group that received a decision aid including patient education, preference assessment, and personalized outcome estimations (intervention group) or a group receiving educational material only (control group) alongside usual care.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.
Stafford I S et al. NPJ digital medicine 2020 Mar 3(1) 30

Ethical, Legal and Social Issues (ELSI)

Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities.
Paulus Jessica K et al. NPJ digital medicine 2020 Jul 3(1) 99

General Practice

Development and Validation of Risk Scores for All-Cause Mortality for a Smartphone-Based "General Health Score" App: Prospective Cohort Study Using the UK Biobank.
Clift Ashley K et al. JMIR mHealth and uHealth 2021 Feb 9(2) e25655

The objective of the study was to develop and validate a novel, easily interpretable, points-based health score ("C-Score") derived from metrics measurable using smartphone components and iterations thereof that utilize statistical modeling and machine learning (ML) approaches.A literature review was conducted to identify relevant predictor variables for inclusion in the first iteration of a points-based model. This was followed by a prospective cohort study in a UK Biobank population.

Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph.
Kuo Po-Chih et al. NPJ digital medicine 2021 Feb 4(1) 25

Multimodal temporal-clinical note network for mortality prediction.
Yang Haiyang et al. Journal of biomedical semantics 2021 Feb 12(1) 3

Validation of a Machine Learning Brain Electrical Activity-Based Index to Aid in Diagnosing Concussion Among Athletes.
Bazarian Jeffrey J et al. JAMA network open 2021 Feb 4(2) e2037349

A survey of deep learning models in medical therapeutic areas.
Nogales Alberto et al. Artificial intelligence in medicine 2021 Feb 112102020

Driving success in personalized medicine through AI-enabled computational modeling.
Chakravarty Kaushik et al. Drug discovery today 2021 Feb

Measuring Adoption of Patient Priorities-Aligned Care Using Natural Language Processing of Electronic Health Records: Development and Validation of the Model.
Razjouyan Javad et al. JMIR medical informatics 2021 Feb 9(2) e18756

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare.
Cirillo Davide et al. NPJ digital medicine 2020 Jun 3(1) 81

A machine learning approach predicts future risk to suicidal ideation from social media data.
Roy Arunima et al. NPJ digital medicine 2020 May 3(1) 78

Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. We used 512,526 tweets from N?=?283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls.

Digitizing clinical trials.
Inan O T et al. NPJ digital medicine 2020 Jul 3(1) 101

Defining precision health: a scoping review protocol.
Ryan Jillian C et al. BMJ open 2021 Feb 11(2) e044663

Heart, Lung, Blood and Sleep Diseases

Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study.
Walkey Allan J et al. JMIR cardio 2021 Feb 5(1) e18840

Missed Incidental Pulmonary Embolism: Harnessing Artificial Intelligence to Assess Prevalence and Improve Quality Improvement Opportunities.
Wildman-Tobriner Benjamin et al. Journal of the American College of Radiology : JACR 2021 Feb

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.
Kim Eun Young et al. PloS one 2021 16(2) e0246472

Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models.
Andy Anietie U et al. JMIR cardio 2021 Feb 5(1) e24473

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.
Narang Akhil et al. JAMA cardiology 2021 Feb

Inter-rater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.
Cesari Matteo et al. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 2021 Feb

Artificial intelligence for the diagnosis of heart failure.
Choi Dong-Ju et al. NPJ digital medicine 2020 Apr 3(1) 54

Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research.
Nelson Benjamin W et al. NPJ digital medicine 2020 Jun 3(1) 90

Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study.
Zhang Yaqi et al. JMIR medical informatics 2021 Feb 9(2) e23606

The future of sleep health: a data-driven revolution in sleep science and medicine.
Perez-Pozuelo Ignacio et al. NPJ digital medicine 2020 Mar 3(1) 42

Infectious Diseases

PI Prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections.
Rider Nicholas L et al. PloS one 2021 16(2) e0237285

Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters.
Gao Yue et al. Journal of intensive care 2021 Feb 9(1) 19

Use Internet search data to accurately track state level influenza epidemics.
Yang Shihao et al. Scientific reports 2021 Feb 11(1) 4023

ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track influenza activity and other infectious diseases.


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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