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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 05/13/2021

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

Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques.
Huang Brian et al. International journal of laboratory hematology 2021

How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system.
Berger Alexandra et al. Orphanet journal of rare diseases 2021 16(1) 198

Cancer

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications.
Hashemzadeh Hadi et al. Scientific reports 2021 11(1) 9804

Artificial intelligence in oncology: Path to implementation.
Chua Isaac S et al. Cancer medicine 2021

AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user-design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data.

Deep learning for fully-automated prediction of overall survival in patients with oropharyngeal cancer using FDG PET imaging: an international retrospective study.
Cheng Nai-Ming et al. Clinical cancer research : an official journal of the American Association for Cancer Research 2021

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.
Raya-Povedano José Luis et al. Radiology 2021 203555

Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Patel M et al. Clinical radiology 2021

Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images.
Buda Mateusz et al. Radiology. Artificial intelligence 2020 2(1) e180050

Radiomics Model to Predict Early Progression of Nonmetastatic Nasopharyngeal Carcinoma after Intensity Modulation Radiation Therapy: A Multicenter Study.
Du Richard et al. Radiology. Artificial intelligence 2019 1(4) e180075

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.
Tan Mingyu et al. Frontiers in oncology 2021 11658138

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.
Hayward Mary-Kate et al. Biochimica et biophysica acta. Reviews on cancer 2021 1876(1) 188555

Chronic Disease

Automated assessment of glomerulosclerosis and tubular atrophy using deep learning.
Salvi Massimo et al. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2021 90101930

Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.
Cheung Ronald et al. Eye (London, England) 2021

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.
Rashed-Al-Mahfuz Md et al. IEEE journal of translational engineering in health and medicine 2021 94900511

Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders.
Varghese Julian et al. Sensors (Basel, Switzerland) 2021 21(9)

Machine Learning and Natural Language Processing in Mental Health: Systematic Review.
Le Glaz Aziliz et al. Journal of medical Internet research 2021 23(5) e15708

Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources.

Clinical validation of an artificial intelligence-enabled wound imaging mobile application in diabetic foot ulcers.
Chan Kai Siang et al. International wound journal 2021

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.
Podichetty Jagdeep T et al. Clinical and translational science 2021

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients.
Schmidt Danilo et al. Journal of visualized experiments : JoVE 2021 (170)

General Practice

Artificial intelligence in ultrasound.
Shen Yu-Ting et al. European journal of radiology 2021 139109717

Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.
Shawwa Khaled et al. Clinical kidney journal 2021 14(5) 1428-1435

Reporting guidelines for artificial intelligence in healthcare research.
Ibrahim Hussein et al. Clinical & experimental ophthalmology 2021

Use of Machine Learning Algorithms to Predict the Understandability of Health Education Materials: Development and Evaluation Study.
Ji Meng et al. JMIR medical informatics 2021 9(5) e28413

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.
Tomašev Nenad et al. Nature protocols 2021

Using supervised machine learning classifiers to estimate likelihood of participating in clinical trials of a de-identified version of ResearchMatch.
Vazquez Janette et al. Journal of clinical and translational science 2021 5(1) e42

Integrating data science into the translational science research spectrum: A substance use disorder case study.
Slade Emily et al. Journal of clinical and translational science 2021 5(1) e29

Lessons and tips for designing a machine learning study using EHR data.
Arbet Jaron et al. Journal of clinical and translational science 2021 5(1) e21

Machine learning-based mortality prediction model for heat-related illness.
Hirano Yohei et al. Scientific reports 2021 11(1) 9501

Ethical implications of AI in robotic surgical training: A Delphi consensus statement.
Collins Justin W et al. European urology focus 2021

Development of a Random Forest model for forecasting allergenic pollen in North America.
Lo Fiona et al. The Science of the total environment 2021 773145590

Predicting Monthly Community-Level Domestic Radon Concentrations in the Greater Boston Area with an Ensemble Learning Model.
Li Longxiang et al. Environmental science & technology 2021

Analyzing Description, User Understanding and Expectations of AI in Mobile Health Applications.
Su Zhaoyuan et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2021 20201170-1179

User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.
Reale Carrie et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2021 20201050-1058

Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning.
Mardini Mamoun T et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2021 2020803-812

PRimary carE digital Support ToOl in mental health (PRESTO): design, development and study protocols.
Anmella Gerard et al. Revista de psiquiatria y salud mental 2021

Heart, Lung, Blood and Sleep Diseases

Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke.
Kappelhof N et al. Computers in biology and medicine 2021 133104414

A radiomic approach to predict myocardial fibrosis on coronary CT angiography in hypertrophic cardiomyopathy.
Qin Le et al. International journal of cardiology 2021

Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients.
Mbotwa John L et al. PloS one 2021 16(5) e0243674

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.
Yao Xiaoxi et al. Nature medicine 2021

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.
Xue Yuzhou et al. Lipids in health and disease 2021 20(1) 48

Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review.
Wesselius Fons J et al. Computers in biology and medicine 2021 133104404

Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
X Yao et al, Nature Medicine, May 6, 2021

We conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. We find that the use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

Machine learning analysis: general features, requirements and cardiovascular applications.
Ricciardi Carlo et al. Minerva cardiology and angiology 2021

The current state of artificial intelligence in cardiac transplantation.
Goswami Rohan et al. Current opinion in organ transplantation 2021 26(3) 296-301

Neural Network-derived Perfusion Maps for the Assessment of Lesions in Patients with Acute Ischemic Stroke.
Meier Raphael et al. Radiology. Artificial intelligence 2019 1(5) e190019

Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes.
Gevaert Andreas B et al. ESC heart failure 2021

Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.
Zhang Haoyue et al. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2021 90101926

Infectious Diseases

Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery.
Han Xuan et al. Critical care medicine 2021

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.
Kamaleswaran Rishikesan et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2021 2020619-628

Effective interventions to increase routine childhood immunization coverage in low socioeconomic status communities in developed countries: A systematic review and critical appraisal of peer-reviewed literature.
Machado Amanda Alberga et al. Vaccine 2021

Reproductive Health

An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation.
Prieto Juan C et al. Proceedings of SPIE--the International Society for Optical Engineering 2021 11596


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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