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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 02/11/2021

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

Cancer

Designing deep learning studies in cancer diagnostics
A Kleppe et al, Nature Rev Cancer, January 2021

The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. We discuss reasons for the moderate progress and describe remedies designed to facilitate transition to the clinic.

Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database
C Lee et al, Lancet Digital Health ,February 3, 2021

Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.
Zhang Yuanchuan et al. Journal of laparoendoscopic & advanced surgical techniques. Part A 2021 Feb

A Systematic Review of Artificial Intelligence in Prostate Cancer.
Van Booven Derek J et al. Research and reports in urology 2021 1331-39

Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images.
Kwak Min Seob et al. Frontiers in oncology 2020 10619803

Data-Driven Methods for Advancing Precision Oncology.
Nedungadi Prema et al. Current pharmacology reports 2018 Apr 4(2) 145-156

Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device.
Shaukat Aasma et al. Endoscopy international open 2021 Feb 9(2) E263-E270

A Novel Prognostic Scoring System of Intrahepatic Cholangiocarcinoma With Machine Learning Basing on Real-World Data.
Li Zhizhen et al. Frontiers in oncology 2020 10576901

Role of artificial intelligence in hepatobiliary and pancreatic surgery.
Bari Hassaan et al. World journal of gastrointestinal surgery 2021 Jan 13(1) 7-18

Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database.
Lee Changhee et al. The Lancet. Digital health 2021 Feb

Chronic Disease

Alzheimer’s Prediction May Be Found in Writing Tests. IBM researchers trained artificial intelligence to pick up hints of changes in language ahead of the onset of neurological diseases.
G Kolata, February 1, 2021

Data-driven identification of ageing-related diseases from electronic health records.
Kuan Valerie et al. Scientific reports 2021 Feb 11(1) 2938

Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease.
Powers Rob et al. Science translational medicine 2021 Feb 13(579)

The authors developed a smartwatch-based ambulatory monitoring system to track dyskinesia and resting tremor in patients with Parkinson’s disease. Smartwatch-detected tremor and dyskinesia matched clinician-reported evaluations seen during in-clinic visits. The smartwatch-based system could identify changes in symptoms resulting from better adherence to medication or deep brain stimulation treatment.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment.
Kang Sung Hoon et al. Journal of Alzheimer's disease : JAD 2021 Jan

Recent advances in medical image processing for the evaluation of chronic kidney disease.
Alnazer Israa et al. Medical image analysis 2021 Jan 69101960

Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study.
Schultebraucks Katharina et al. Neurobiology of stress 2021 May 14100297

A narrative review of machine learning as promising revolution in clinical practice of scoliosis.
Chen Kai et al. Annals of translational medicine 2021 Jan 9(1) 67

A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.
Li Jun et al. Journal of biomedical informatics 2021 Feb 103693

We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health.Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics.

General Practice

DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence.
et al. Nature medicine 2021 Feb

Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure.
Leiner Tim et al. Insights into imaging 2021 Feb 12(1) 11

Clinician checklist for assessing suitability of machine learning applications in healthcare.
Scott Ian et al. BMJ health & care informatics 2021 Feb 28(1)

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed.

Artificial intelligence in outcomes research: a systematic scoping review.
Graili Pooyeh et al. Expert review of pharmacoeconomics & outcomes research 2021 Feb

Heart, Lung, Blood and Sleep Diseases

Applications of artificial intelligence for hypertension management.
Tsoi Kelvin et al. Journal of clinical hypertension (Greenwich, Conn.) 2021 Feb

Infectious Diseases

Prediction of Late-Onset Sepsis in Preterm Infants Using Monitoring Signals and Machine Learning.
Cabrera-Quiros Laura et al. Critical care explorations 2021 Jan 3(1) e0302

Artificial Intelligence-Based Screening for Mycobacteria in Whole-Slide Images of Tissue Samples.
Pantanowitz Liron et al. American journal of clinical pathology 2021 Feb

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.
Li Fengxia et al. Personal and ubiquitous computing 2021 Jan 1-13

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.
Jimenez-Solem Espen et al. Scientific reports 2021 Feb 11(1) 3246

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England.
Romero M Pilar et al. Preventive veterinary medicine 2021 Jan 188105264

Preference for artificial intelligence medicine before and during COVID-19 pandemic: Discrete choice experiment with propensity score matching.
Liu Taoran et al. Journal of medical Internet research 2021 Feb


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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