Digital epidemiology: what is it, and where is it going?

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Digital Epidemiology is a new field that has been growing rapidly in the past few years, fueled by the increasing availability of data and computing power, as well as by breakthroughs in data analytics methods. In this short piece, I provide an outlook of where I see the field heading, and offer a broad and a narrow definition of the term.

Related collections

Most cited references 9

Record: found
Abstract: found
Article: not found

Deep learning.

Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

0 comments Cited 8322 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

0 comments Cited 1996 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Detecting influenza epidemics using search engine query data.

Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel … (2009)

Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.

0 comments Cited 409 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Marcel Salathé:

ORCID: http://orcid.org/0000-0002-5079-7797

marcel.salathe@epfl.ch

Journal

Journal ID (nlm-ta): Life Sci Soc Policy

Journal ID (iso-abbrev): Life Sci Soc Policy

Title: Life Sciences, Society and Policy

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Electronic): 2195-7819

Publication date (Electronic): 4 January 2018

Publication date PMC-release: 4 January 2018

Publication date Collection: December 2018

Volume: 14

Electronic Location Identifier: 1

Affiliations

Digital Epidemiology Lab, School of Life Sciences and School of Computer and Communication Sciences, EPFL, Chemin des Mines 9, 1202 Geneva, Switzerland

Author information

Marcel Salathé http://orcid.org/0000-0002-5079-7797

Article

Publisher ID: 65

DOI: 10.1186/s40504-017-0065-7

PMC ID: 5754279

PubMed ID: 29302758

SO-VID: 0f844d91-e820-4e20-8fa7-dbe02315c93b

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 26 June 2017

Date accepted : 8 December 2017

Custom metadata

Data availability:

Comments

Comment on this article

scite_

Cited by 68

See all cited by

Most referenced authors 173

See all reference authors

- Version 1

Digital epidemiology: what is it, and where is it going?

Read this article at

Abstract

Related collections

Karger: Digital Health

Most cited references 9

Deep learning.

Dermatologist-level classification of skin cancer with deep neural networks

Detecting influenza epidemics using search engine query data.

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 10

Cited by 68

Most referenced authors 173