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Hot Topics of the Day are picked by experts to capture the latest information and publications on public health genomics and precision health for various diseases and health topics. Sources include published scientific literature, reviews, blogs and popular press articles.

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259 hot topic(s) found with the query "Big data"

Get up to Speed on the Latest Developments in the Field! Register for the ORISE Current Issues in Genomics and Precision Public Health Online Training Event, September 7–8, 2023.
W White et al, CDC Blog Post, August 9, 2023 (Posted: Aug 09, 2023 11AM)

Advances in genomics, data science, machine learning, and artificial intelligence are transforming practice. Next generation public health and medical workforces need to understand these developments and how they can be used to benefit population health. Recognizing this challenge, Oak Ridge Institute for Science and Education (ORISE) is partnering with the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention (CDC) to offer a free 2-day in-person training event covering the latest developments in these fields: Current Issues in Genomics and Precision Public Health – Using Genomics and Big Data to Improve Population Health and Reduce Health Inequities.

Genomics and Precision Public Health Issues Enrichment Event
Oak Ridge Institute for Science Education Enrichment Event, Atlanta, Georgia, September 7-8, 2023 Brand (Posted: Jul 17, 2023 8AM)

In the past decade, genomics, and precision health approaches such as big data science and machine learning have emerged as important tools for public health. Those entering the public health and medical workforces must keep pace with these evolving fields to maximize the benefit to public health. Recognizing this need, ORISE is partnering with the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention to offer a two-day in-person enrichment event covering the latest developments in these fields.

Harnessing big data for health equity through a comprehensive public database and data collection framework.
Cameron Sabet et al. NPJ Digit Med 2023 5 (1) 91 (Posted: May 23, 2023 11AM)

We examine the reasons behind the delayed adoption of big data for healthcare equity, recent efforts embracing big data tools, and methods to maximize potential without overburdening physicians. We additionally propose a public database for anonymized patient data, introducing diverse metrics and equitable data collection strategies, providing valuable insights for policymakers and health systems to better serve communities.

Medicine and health of 21st Century: Not just a high biotech-driven solution.
Assidi Mourad et al. NPJ genomic medicine 2022 11 (1) 67 (Posted: Nov 16, 2022 8AM)

Although the potential of biotechnology is motivating, we should not lose sight of approaches that may not seem as glamorous but can have large impacts on the healthcare of many and across disparate population groups. A balanced approach of “omics and big data” solution in contemporary health systems along with a large scale, simpler, and suitable strategies should be defined with expectations properly managed.

Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.
Gao Chengcheng et al. NPJ digital medicine 2022 10 (1) 161 (Posted: Oct 29, 2022 11AM)

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data.

Big data and AI in pandemic preparedness
The Lancet Summit: October 27-28, 2022 (Posted: Oct 27, 2022 0PM)

Managing COVID-19 and infectious disease is a global priority over the next few decades. Clinical and research communities are committed to reviewing the global response to the COVID-19 pandemic and a key part has been the unprecedented use and rapid scale of technology. This conference will allow diverse stakeholders to discuss opportunities for new pandemic warning systems based on modelling approaches using AI; advances in real-world surveillance and tracking of disease spread; AI for drug screening and rapid diagnostics; and advances in remote treatment and telehealth.

Big data in basic and translational cancer research.
Jiang Peng et al. Nature reviews. Cancer 2022 9 (Posted: Sep 07, 2022 8AM)

Fast data growth has given rise to an evolving concept of ‘big data’ in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements.

Predicting Firearm Suicide-Small Steps Forward With Big Data.
Betz Marian E et al. JAMA network open 2022 7 (7) e2223758 (Posted: Jul 24, 2022 11AM)

The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU
JD Moorman, NPJ Digital Medicine, March 31, 2022 (Posted: Apr 02, 2022 8AM)

In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes.

Artificial Intelligence in Medicine and Public Health: Prospects and Challenges Beyond the Pandemic
D Rasooly et al, CDC Blog Post, March 1, 2022 Brand (Posted: Mar 02, 2022 8AM)

A recent Nature Medicine article discusses promising uses of artificial intelligence in medicine, particularly in medical imaging and big data integration, and considers technical and ethical challenges for their applications in improving human health. Here is a quick summary of the review and the implications for population health.

Harnessing big data to characterize immune-related adverse events
Y Jing et al, Nat Rev Clin Oncology, January 2022 (Posted: Jan 26, 2022 7AM)

We summarize the advantages and shortcomings of different sources of ‘big data’ for the study of irAEs and highlight progress made using such data to identify biomarkers of irAE risk, evaluate associations between irAEs and therapeutic efficacy, and characterize the effects of demographic and anthropometric factors on irAE risk. Harnessing big data will accelerate research on irAEs and provide key insights that will improve the clinical management of patients receiving ICIs.

Using big data and mobile health to manage diarrheal disease in children in low-income and middle-income countries: societal barriers and ethical implications
KH Keddy et al, Lancet Inf Dis, December 13, 2021 (Posted: Dec 15, 2021 8AM)

Diarrhea is an important cause of morbidity and mortality in children from low-income and middle-income countries (LMICs), despite advances in the management of this condition. Understanding of the causes of diarrhea in children in LMICs has advanced owing to large multinational studies and big data analytics computing the disease burden, identifying the important variables that have contributed to reducing this burden. The advent of the mobile phone has further enabled the management of childhood diarrhea by providing both clinical support to health-care workers/

A needle for Alzheimer’s in a haystack of claims data
E Gunney et al, Nature Aging, December 2021 (Posted: Dec 12, 2021 9AM)

In the era of big data, looking for insights in large datasets has become the norm — and health data are no exception. Combining systems-biology-driven, endophenotype-based analysis of drug targets with large-scale medical claims data points to sildenafil as a potential treatment opportunity for Alzheimer’s disease.

Insights Into Immune-Mediated Disease and Cancer Risk-Delivering on the Promise of UK Biobank Big Data.
Stewart Douglas R et al. JAMA oncology 2021 12 (Posted: Dec 03, 2021 11AM)

Large, longitudinal data sets derived from the electronic health records are powerful tools for discovery. A recent study extracted from the big data provided by the UK Biobank clinically and scientifically useful insights and estimates of cancer risk associated with immune-mediated disease.

From Public Health Genomics to Precision Public Health: On to the Next Generation!
Muin J. Khoury Video Presentation, at the Transdisciplinary Conference for Future Leaders in Precision Public Health. Posted on November 11, 2021 (Posted: Nov 12, 2021 11AM)

In this presentation Dr Khoury discusses the evolution of the field of public health genomics over the past 25 years into precision medicine and precision public health, which involves applications of genomics , big data and predictive analytics to population health. He also describes ongoing applications of precision public health to the COVID-19 response.

Who has long-COVID? A big data approach
ER Plaff et al, MEDRXIV, October 22, 2021 (Posted: Oct 24, 2021 6PM)

Greece used AI to curb COVID: what other nations can learn- Governments are hungry to deploy big data in health emergencies. Scientists must help to lay the legal, ethical and logistical groundwork.
Nature editorial, September 22, 2021 (Posted: Sep 22, 2021 4PM)

During the pandemic, there has been no shortage of ideas on how to deploy big data and AI to improve public health or assess the pandemic’s economic impact. However, relatively few of these ideas have made it into practice. This is partly because companies and governments that hold relevant data — such as mobile-phone records or details of financial transactions — need agreed systems to be in place before they can share the data with researchers. It’s also not clear how consent can be obtained to use such personal data, or how to ensure that these data are stored safely and securely.

Diagnostic Errors, Health Disparities, and Artificial Intelligence A Combination for Health or Harm?
SA Ibrahim et al, JAMA Health Forum (Posted: Sep 19, 2021 10AM)

The health care industry is emerging as a leader in the adoption of AI through sophisticated machine learning–assisted diagnostic tools. For clinical trials, AI has the potential to support every stage of the process, including finding a trial in which to enroll, addressing patient-centric enrollment issues, and assessing trial medication adherence with remote and digital monitoring. It is equally important to tap into the opportunities provided by big data and AI to detect and address diagnostic disparities in populations at risk for such disparities.

Preventing Glaucoma Vision Loss with ‘Big Data’
F Collins, NIH Director blog, September 16, 2021 (Posted: Sep 17, 2021 6AM)

A recent study analyzed data from more than 1,200 people with glaucoma who participate in NIH’s All of Us Research Program. With consent from the participants, Baxter used their EHRs to train a computer to find telltale patterns within the data and then predict with 80 to 99 percent accuracy who would later require eye surgery.

The Complementarity of Public Health and Medicine — Achieving “the Highest Attainable Standard of Health”
DJ Hunter, NEJM, August 5, 2021 (Posted: Aug 04, 2021 5PM)

Preventive medicine can be practiced with individual patients in a consulting room or through organized activities such as vaccine outreach, community blood-pressure screening, or health education. Future “big data” analyses may both provide insights into population health as well as permitting more personalized medical care.

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases
N Hurvitz et al, EJHG, July 19, 2021 (Posted: Jul 19, 2021 8AM)

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians.

Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom
N Al-Zobaidy et al, MERDXIV, July 10,2021 (Posted: Jul 11, 2021 0PM)

Genetics of substance use disorders in the era of big data
J Gelernter et al, Nat Rev Genetics, July 1, 2021 (Posted: Jul 02, 2021 7AM)

The past few years have seen remarkable progress in our understanding of the genetics, and therefore the biology, of substance use and abuse. Various studies — including of well-defined phenotypes in deeply phenotyped samples, as well as broadly defined phenotypes in meta-analysis and biobank samples — have revealed multiple risk loci for these common traits

Using ‘big data’ to disentangle aging and COVID-19
RR Montgomery et al, Nature Aging, June 14, 2021 (Posted: Jun 15, 2021 8AM)

A new study leverages different types of big data, either generated in house from cohorts of healthy aging and COVID-19, or downloaded from the ever-increasing public data archives, to disentangle the distinct cellular and proteomic mechanisms of COVID-19 and aging.

Big data, artificial intelligence, and the opioid crisis
Lancet Digital Health editorial, June 1, 2021 (Posted: May 26, 2021 6AM)

Big data and artificial intelligence can help to target preventive measures (eg, addressing comorbidities and socioeconomic inequalities) to reduce future risks of opioid misuse. Community-level modelling can forecast the number of fatal overdoses that could be avoided through wider availability of key treatments. But model explainability and integration of individual-level factors— clinical and social—are crucial to remove barriers and ensure prevention and treatment efforts are accessible to all.

20 years of precision medicine in oncology
The Lancet editorial, May 15, 2021 (Posted: May 19, 2021 8AM)

In the 20 years since the publication of the first draft of the human genome project, the use of genotyping and genomics have become part of standard treatment for some cancers. The desire to go beyond blanket, and often difficult, treatments for patients to a more refined, efficient, and patient-centered approach is an ideal that is hard to oppose. But as precision medicine in oncology expands to include big data, proteomics, transcriptomics, molecular imaging, and more, there are serious challenges ahead to translate that ideal into meaningful and equitable health care for patients.

Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective.
Velmovitsky Pedro Elkind et al. Frontiers in public health 2021 9561873 (Posted: Apr 27, 2021 9AM)

With the coming of Big Data, the fields of precision medicine and public health are converging into precision public health, the study of biological and genetic factors supported by large amounts of population data. In this paper, we explore through a comprehensive review the data types and use cases found in precision medicine and public health.

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 (Posted: Apr 16, 2021 10AM)

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.

Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
N Shang et al, NPJ Digital Medicine, April 13, 2021 (Posted: Apr 13, 2021 6AM)

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods.

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity.
Majnaric Ljiljana Trtica et al. Journal of clinical medicine 2021 10(4) (Posted: Mar 09, 2021 9AM)

In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity.

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies
DV Gunasekeran et al, NPJ Digital Medicine, February 26, 2021 (Posted: Feb 27, 2021 7AM)

Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n?=?111/247) and big data analytics (36.0%, n?=?89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations.

Complicated legacies: The human genome at 20
CM Jones et al, SCience, February 4, 2021 (Posted: Feb 05, 2021 7AM)

Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong
H Zhou et al, MEDRXIV, February 2, 2021 (Posted: Feb 03, 2021 9AM)

We develop a data-driven agent-based model for 7.55 million Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has split into 4,905 500m×500m grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns.

Big Data and Digital Solutions: Laying the Foundation for Cardiovascular Population Management CME .
Nasir Khurram et al. Methodist DeBakey cardiovascular journal 16(4) 272-282 (Posted: Feb 02, 2021 11AM)

Big data analytics and digital application platforms-such as patient care dashboards, clinical decision support systems, mobile patient engagement applications, and key performance indicators-offer unique opportunities for value-based healthcare delivery and efficient cardiovascular population management. Successful implementation of big data solutions must include a multidisciplinary approach.

Big data and simple models used to track the spread of COVID-19 in cities
C Ma et al, Nature News, November 10, 2020 (Posted: Nov 11, 2020 3PM)

Understanding the dynamics of SARS-CoV-2 infections could help to limit viral spread. Analyzing mobile-phone data to track human contacts at different city venues offers a way to model infection risks and explain infection disparities.

The intersection of genomics and big data with public health: Opportunities for precision public health.
Khoury Muin J et al. PLoS medicine 2020 Oct 17(10) e1003373 (Posted: Oct 30, 2020 10AM)

Precision public health (PPH) has emerged as a response to the increasing availability of genomics, biobanks, and other sources of big data in healthcare and public health. ?The field has evolved starting with genomics to include multiple practical applications such as pathogen genomics that address population health. ?PPH can expand understanding of health disparities, advance strategic public health science, and demonstrate the need for innovation and workforce development.

Social license for the use of big data in the COVID-19 era
JA Shaw et al, NPJ Digital Medicine, October 2, 2020 (Posted: Oct 03, 2020 5PM)

In this Comment, we outline the importance of earning social license for public approval of big data initiatives, and specify principles of data law and data governance practices that can promote social license. We provide illustrative examples from the United States, Canada, and the United Kingdom.

Putting (Big) Data in Action: Saving Lives with Countrywide Population Movement Monitoring Using Mobile Devices during the COVID-19 Crisis
MK Szocska et al, MEDRXIV, September 23, 2020 (Posted: Sep 24, 2020 10AM)

Looking at neurodevelopment through a big data lens.
Briscoe James et al. Science (New York, N.Y.) 2020 Sep (6510) (Posted: Sep 19, 2020 8PM)

The billions of neurons that make up the adult brain are organized into domains and circuits during development. High-resolution measurements such as those enabled by single-cell molecular profiling have revealed unexpected cellular diversity. Genomic tools are lending insight into mechanisms behind neurodevelopmental disorders.

Evidence of gender bias in the diagnosis and management of COVID-19 patients: A Big Data analysis of Electronic Health Records
J Ancochea et al, MEDRXIV, July 26, 2020 (Posted: Jul 26, 2020 11AM)

Big Data and Collaboration Seek to Fight COVID-19
E Yazinski, The Scientist, July 21, 2020 (Posted: Jul 26, 2020 7AM)

Researchers try unprecedented data sharing and cooperation to understand COVID-19—and develop a model for diseases beyond the coronavirus pandemic.

White Neighborhoods Have More Access To COVID-19 Testing Sites- But the disease is hitting Black and Hispanic communities hardest.
SR Kim et al, FiveThirtyEight, July 22, 2020 (Posted: Jul 22, 2020 8AM)

Big data analysis is one of the first to look at testing site locations in all 50 states using data provided by the health care navigation company Castlight Health. An assessment of city and state health department websites also revealed fewer testing sites in areas primarily inhabited by racial minorities.

How Facebook, Twitter and other data troves are revolutionizing social science-
H Ledford, Nature News, June 17, 2020 (Posted: Jun 19, 2020 6AM)

A new breed of researcher is turning to computation to understand society — and then change it. Traditional social science and computational social science are actually becoming closer over time. In 20 years, there will be no divide.

Machine learning on Big Data from Twitter to understand public reactions to COVID-19
J Xue et al, ARXIV, May 18, 2020 (Posted: May 19, 2020 8AM)

Big Data Begin in Psychiatry
MM Weissman, JAMA Psychiatry, May 13, 2020 (Posted: May 13, 2020 11AM)

The review traces the subsequent evolution of big data in psychiatry to 5 overlapping phases, other population surveys in the US and globally, cohort studies, administrative claims, large genetic data sets, and electronic health records.

Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.
Wang Qian-Chen et al. Journal of cardiovascular translational research 2020 May (Posted: May 12, 2020 3PM)

A Snapshot of Doctoral Training in Epidemiology: Positioning us for the Future.
Hlaing Way Way M et al. American journal of epidemiology 2020 May (Posted: May 10, 2020 7AM)

Over 80% of the programs currently emphasize 2 of 9 non-core competencies, i.e., competency to (1) develop and write grant proposals, and (2) assess evidence for causality on the basis of different causal inference concepts. “Big Data” is the most frequently cited topic currently lacking in doctoral curricula.

Improving epidemic surveillance and response: big data is dead, long live big data
C Buckee, Lancet Digital Health, March 2020 (Posted: May 04, 2020 7AM)

New data streams provide important, real-time information about travel patterns that spread disease and spatial shifts in populations at risk, which until recently have been very difficult to quantify on timescales relevant to a fast-moving epidemic. This information will be key to planning surveillance and containment strategies.

Using Digital Technologies in Precision Public Health: COVID-19 and Beyond
MJ Khoury et al, CDC Blog, April 2020 Brand (Posted: Apr 26, 2020 8AM)

With the global spread of COVID-19, there is a proliferation of scientific information and publications that use novel approaches such as genomics and precision health tools (e.g., big data, wearables, and digital devices) in surveillance and epidemiologic investigations. Could these new technologies provide added value to traditional approaches?

The impact of genomics on precision public health
Call for Papers, Genome Medicine, April 22, 2020 (Posted: Apr 23, 2020 8AM)

Human and pathogen genomics are at the leading edge of the application of new technologies to public health practice. Research efforts in this area are contributing to a new era of 'precision public health', an emerging multidisciplinary field that uses genomics, other big data and artificial intelligence to improve population health.

The Public Health Impact of COVID-19: Why Host Genomics?
MJ Khoury el al, CDC Blog, April 21, 2020 Brand (Posted: Apr 22, 2020 0PM)

Ideally, scientific studies of COVID-19 risk factors for transmission and severity should include both viral and human genomes and the interaction of these two genomes, along with other traditional environmental, social and economic factors, and emerging types of big data. This is part of a precision public health response.

Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city.
Kim Ryung S et al. BMC medical research methodology 2020 Apr 20(1) 77 (Posted: Apr 15, 2020 10AM)

AI for the Eye — Automated Assistance for Clinicians Screening for Papilledema
I Kohane, NEJM, April 14, 2020 (Posted: Apr 14, 2020 1PM)

The fuel that has powered the recent success of machine learning has been the availability of two aspects of “big” data. The first is large data sets. Usually the largest and most representative data sets perform the best. The second aspect driving success in machine learning has been the availability of labels that describe the data.

Rapid implementation of mobile technology for real-time epidemiology of COVID-19
DA Drew et al, MEDRXIV, April 6, 2020 (Posted: Apr 07, 2020 10AM)

The COronavirus Pandemic Epidemiology (COPE) consortium brings together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application garnering more than 2.25 million users to date. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots.

AI Can Help Us Fight Infectious Diseases In A More Effective Way
M Colangelo et al, Forbes, March 27, 2020 (Posted: Mar 28, 2020 8AM)

There are several approaches that can be taken with technology. One approach is to apply data science techniques to personalized vaccination on a massive scale. Another is to optimize immunization management using AI. By using AI, big data, and small data techniques together, vaccines could be distributed on a massive scale in a more precise way.

Digital technology and COVID-19
DSW Ting et al, Nature Medicine, March 27, 2020 (Posted: Mar 27, 2020 1PM)

We explore the potential application of four inter-related digital technologies (the IoT, big-data analytics, AI and blockchain) to augmenting two traditional public-health strategies for tackling COVID-19: (1) monitoring, surveillance, detection and prevention of COVID-19; and (2) mitigation of the impact to healthcare indirectly related to COVID-19

Response to COVID-19 in Taiwan- Big Data Analytics, New Technology, and Proactive Testing
CJ Wang et al, JAMA, March 3, 2020 (Posted: Mar 03, 2020 11AM)

Taiwan learned from its 2003 SARS experience and established a public health response mechanism for enabling rapid actions for the next crisis. Well-trained and experienced teams of officials were quick to recognize the crisis and activated emergency management structures to address the emerging outbreak.

A Turing test for artificial intelligence in cancer
Nature Cancer, February 2020 (Posted: Mar 02, 2020 0PM)

The convergence of big data and artificial intelligence is poised to revolutionize cancer research and care, from basic conceptual developments to translational and clinical applications. To reap these benefits, it is important to separate the hope from the hype.

Brief introduction of medical database and data mining technology in big data era.
Yang Jin et al. Journal of evidence-based medicine 2020 Feb (Posted: Feb 26, 2020 8AM)

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases.

Making Informed CHOICES: The Launch of a "Big Data" Pragmatic Trial to Improve Cholesterol Management and Prevent Heart Disease in Ontario.
Ferreira-Legere Laura E et al. Healthcare quarterly (Toronto, Ont.) 2020 Jan 22(4) 6-9 (Posted: Feb 26, 2020 8AM)

The study is a pragmatic, registry-based, cluster randomized controlled trial that aims to improve cholesterol management through appropriate statin use in adults and to ultimately reduce cardiovascular events in high-risk communities. It uses an innovative, multicomponent intervention approach that includes audit and feedback reports and educational materials.

The data economy- A deluge of data is giving rise to a new economy
The Economist, February 2020 (Posted: Feb 21, 2020 9AM)

Tracking the spread of novel coronavirus (2019-nCoV) based on big data
X Zhao et al, MedRXIV, February 2020 (Posted: Feb 12, 2020 8AM)

We used the traffic flow data from Baidu Map, and number of air passengers who left Wuhan from 1st January to 26th January, to quantify the potential infectious people. We developed multiple linear models with local population and air passengers as predicted variables to explain the variance of confirmed cases in every city across China.

The application of big data to cardiovascular disease: paths to precision medicine.
Leopold Jane A et al. The Journal of clinical investigation 2020 Jan (1) 29-38 (Posted: Feb 07, 2020 8AM)

Evidence from big data in obesity research: international case studies.
Wilkins Emma et al. International journal of obesity (2005) 2020 Jan (Posted: Feb 05, 2020 8AM)

The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity. However, the case studies also encountered limitations and biases.

Translating 'big data': better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry.
Deblais Loïc et al. Animal health research reviews 2020 Jan 1-21 (Posted: Jan 15, 2020 8AM)

Big hopes for big data
Nature Medicine editorial, January 13, 2020 (Posted: Jan 14, 2020 8AM)

Large-scale multi-modal information on patients’ health is ever increasing, providing an opportunity to use big data for taking individualized medicine to a global scale.

Sizing up big data
MA Banks, Nature Medicine, January 13, 2020 (Posted: Jan 14, 2020 8AM)

Just how big is big data? The numbers may surprise you. Big data—as opposed to small data—is too complex and varied to be contained in one spreadsheet or sometimes even in one computer. But while there might be slight differences in what precisely defines ‘big data’, the one thing everyone can agree on is that there is more of it these days .

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration.
Kim Hun Sung et al. Endocrinology and metabolism (Seoul, Korea) 2019 Dec 34(4) 349-354 (Posted: Jan 02, 2020 9AM)

Pharmacoepidemiology and Big Data Analytics: Challenges and Opportunities when Moving towards Precision Medicine.
Burden Andrea M et al. Chimia 2019 Dec 73(12) 1012-1017 (Posted: Jan 02, 2020 9AM)

Pharmacoepidemiology is the study of the safety and effectiveness of medications following market approval. The increased availability and size of healthcare utilization databases allows for the study of rare adverse events, sub-group analyses, and long-term follow-up. These datasets are large, including thousands of patient records spanning multiple years.

In 2020, Could Artificial Intelligence Help Cure Cancer?
K McPherson, Daily Beast, December 2019 (Posted: Dec 30, 2019 10AM)

In the past, vast quantities of information were lost to file cabinets and unconnected servers. The big data era is allowing CancerLinQ and other initiatives to access this large and growing amount of de-identified data to help physicians provide high-quality care.

Finding individuals with familial hypercholesterolemia using FIND FH (machine learning and big data)
FH Foundation, youtube video, December 2, 2019 (Posted: Dec 03, 2019 8AM)

90% of individuals with FH are undiagnosed today. It's why the FH Foundation developed a machine learning algorithm to help identify those most at risk within a healthcare system and bring them into care

Translational Health Disparities Research in a Data-Rich World.
Breen Nancy et al. Health equity 2019 3(1) 588-600 (Posted: Nov 20, 2019 8AM)

The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies. This data-rich world for health disparities research, however, will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias.

3 Myths About Machine Learning in Health Care
DA Haas et al, Harvard Business Review, November 2019 (Posted: Nov 15, 2019 7AM)

Myth 1: Machine learning can do much of what doctors do. Myth 2: “Big data” + brilliant data scientists are always a recipe for success. Myth 3: Successful algorithms will be adopted and utilized.

Diabetes Care Editors' Expert Forum 2018: Managing Big Data for Diabetes Research and Care.
Riddle Matthew C et al. Diabetes care 2019 Jun 42(6) 1136-1146 (Posted: Nov 06, 2019 8AM)

Technological progress in the past half century has greatly increased our ability to collect, store, and transmit vast quantities of information, giving rise to the term "big data." This term refers to very large data sets that can be analyzed to identify patterns, trends, and associations.

The health information exchange has evolved from hunter and gatherer to cultivator
C Wiliams, Stat News, October 31, 2019 (Posted: Oct 31, 2019 0PM)

Data is the new oil,” British data scientist Clive Humby once said. “It’s valuable, but if unrefined it cannot really be used.” I thought about that line recently when I met with the chief medical officer of a large health system. “I don’t want more data,” she told me, “we are already drowning in it.”

Values, challenges and future directions of big data analytics in healthcare: A systematic review.
Galetsi P et al. Social science & medicine (1982) 2019 Sep 112533 (Posted: Oct 09, 2019 8AM)

This is systematic review of 804 scholarly publications related to big data analytics in health in order to identify the organizational and social values along with associated challenges. Key principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology were followed for conducting systematic reviews

Data Science and Machine Learning in Public Health: Promises and Challenges
C Patel et al, CDC Blog Post, September 20, 2019 Brand (Posted: Sep 23, 2019 8AM)

Big data include large amount of information becoming available to researchers. What makes them really “big” is the sheer number of individuals and/or the massive amount of information. Given that the primary use for these datasets is often not research, the question is, “are these data helpful for health-related discoveries and public health surveillance?”

Seeking More Precision in Public Health
K. Bibbins-Domingo CDC Seminar, October 22 at 1 pm. Brand (Posted: Sep 16, 2019 9AM)

While precision medicine has made advances in individualized patient treatments, progress at the population level requires a public health approach focused on tailored population health and prevention strategies and driven by “big data” approaches. Inclusion of diverse populations and a focus on disparities reduction are key components.

How precision medicine and screening with big data could increase overdiagnosis
H Vogt, BMJ, September 13, 2019 (Posted: Sep 14, 2019 8AM)

Precision medicine promises to improve disease prevention but entails a massive, new form of screening. The wide scope of big data screening risks increased detection of abnormalities that will never be clinically relevant. We need a clearer understanding of the natural course of multiple markers and the value of repeated measurement of markers.

Opinion: Big data scientists must be ethicists too
J Vitti, Broad Institute Blog, August 29, 2019 (Posted: Aug 30, 2019 7AM)

Artificial intelligence for medicine needs a Turing test. Obesity would be a good one
M Joyner, StatNews, August 28, 2019 (Posted: Aug 28, 2019 7AM)

If you read high-profile medical journals, the high-end popular press, and magazines like Science or Nature, it is clear that the medicalization of artificial intelligence, machine learning, and big data is in full swing. Speculation abounds about what these can do for medicine. It’s time to put them to the test.

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.
Makino Masaki et al. Scientific reports 2019 Aug 9(1) 11862 (Posted: Aug 21, 2019 9AM)

The imaginary of precision public health.
Kenney Martha et al. Medical humanities 2019 Aug (Posted: Aug 21, 2019 8AM)

Advocates of precision public health argue that adopting cutting-edge big data approaches will allow public health actors to precisely target populations who experience the highest burden of disease and mortality, creating more equitable health futures.

Putting the data before the algorithm in big data addressing personalized healthcare
EM Cahan et al, NPJ Digital Medicine, August 19, 2019 (Posted: Aug 20, 2019 9AM)

All Your Data Is Health Data- And Big Tech has it all.
C Warzel, NY Times, August 13, 2019 (Posted: Aug 19, 2019 8AM)

The “inconvenient truth” about AI in healthcare
T Panch et al, NPJ Digital Medicine, August 16, 2019 (Posted: Aug 17, 2019 9AM)

In the age of big data, each healthcare organization has built its own infrastructure to support its own needs, typically involving on-premises computing and storage. Data is balkanized along organizational boundaries, severely constraining the ability to provide services to patients across a care continuum within one organization or across organizations.

Big Data, Big Tech, and Protecting Patient Privacy
IG Cohen et al, JAMA, August 9, 2019 (Posted: Aug 10, 2019 9AM)

The market for patient data has never been more active. Technology companies, from startups to giants, are eager to access electronic health record (EHR) data to build the next generation of health-focused products. Medical artificial intelligence (AI) is particularly data-hungry.

Digital literacy—a blind spot in medical education?
Z Hassan, BMJ, August 8 2019 (Posted: Aug 09, 2019 8AM)

We are only beginning to exploit the potential applications of automation, machine learning, and big data in medicine. Yet few doctors know any programming skills and many workplaces are still waiting to update to Windows 10, let alone electronic notes or prescribing.

NIH-funded project aims to build a ‘Google’ for biomedical data
R Hailu, Stat News, July 31, 2019 (Posted: Aug 01, 2019 8AM)

Report: Dry AMD requires broad, systems biology approach leveraging big data, multiple disciplines
NIH News Release, July 26, 2019 Brand (Posted: Jul 28, 2019 4PM)

Can Big Data Science Deliver Precision Public Health?
MJ Khoury et al, CDC Blog Post, July 23, 2019 Brand (Posted: Jul 24, 2019 10AM)

In the age of big data, more extensive information by place, person and time are becoming available to measure public health impact and implementation needs. In principle, big data could point to implementation gaps and disparities and accelerate the evaluation of implementation strategies to reach population groups in most need for interventions. However, major challenges need to be overcome.

Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?
Khoury MJ et al, Public Health Genomics, July 17, 2019 (Posted: Jul 18, 2019 8AM)

This CDC paper explores how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person, and time.

Zip Code vs. Genetic Code,
by Erin O?Donnell, Harvard Magazine, July-August 2019 (Posted: Jul 17, 2019 8AM)

It's common to think of disease and health �as this tension of ZIP code versus genetic code,� explains Chirag Patel, assistant professor of biomedical informatics at Harvard Medical School.But a study by Patel and his research team challenges this �either-or� thinking, using Big Data to tease apart the complex interplay of environment, genes, and other factors in disease. They analyzed an insurance database of almost 45 million people in the United States.

Role of Big Data in Cardiovascular Research
WS Weintraub, JAHA, July 11, 2019 (Posted: Jul 11, 2019 8AM)

Perhaps you have noticed that there seems to be an awful lot more data in recent years, but perhaps not a lot more knowledge. Welcome to the world of Big Data. Just what is Big Data, and how is it changing the world of cardiovascular medicine? A thoughtful minireview of the opportunities and challenges of big data.

Assessing Gene-Environment Interactions in the Study of Rare Diseases
CDC Webinar, August 21, 2019 Brand (Posted: Jun 28, 2019 8AM)

The third installment of the 2019 CDC summer public health genomics seminar series. Sign up today to all three seminars, also available for viewing remotely and covering a wide range of topics from infectious disease, big data and rare diseases.

Genomics, Big Data and Data Science in Public Health
CDC Webinar, August 9, 2019 Brand (Posted: Jun 28, 2019 8AM)

Big Data Scientist Training Enhancement Program (BD-STEP)
NCI, 2019 Brand (Posted: Jun 24, 2019 8AM)

A two-year fellowship opportunity that uses data science to advance cancer research and care.

Risk, Benefit, and Fairness in a Big Data World
C Cassell et al, JAMA Forum, June 13, 2019 (Posted: Jun 17, 2019 11AM)

UNRAVEL: big data analytics research data platform to improve care of patients with cardiomyopathies using routine electronic health records and standardised biobanking.
Sammani A et al. Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation 2019 May (Posted: May 29, 2019 8AM)

Big Data and the Intelligence Community - Lessons for Health Care.
Vigilante Kevin et al. The New England journal of medicine 2019 May (20) 1888-1890 (Posted: May 18, 2019 0PM)


Disclaimer: Articles listed in Hot Topics of the Day are selected by Public Health Genomics Branch 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.