Can search engine data save lives from pancreatic cancer?

Gerd Gigerenzer discusses how search engines use big data analytics to “diagnose” your state of health | BMJ Opinion

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Image source: NIH Image Gallery – Flickr // CC BY-NC 2.0

Image shows pancreatic desmoplasia. Pancreatic cancer is associated with a vast desmoplastic reaction in which the connective tissue around the tumor thickens and scars. 

Imagine this warning popping up on your search engine page: “Attention! There are signs that you might have pancreatic cancer. Please visit your doctor immediately.” Just as search engines use big data analytics to detect your book and music preferences, they may also “diagnose” your state of health.

Microsoft researchers have claimed that web search queries could predict pancreatic adenocarcinoma. A retrospective study of 6.4 million users of Microsoft’s search engine Bing identified first-person queries suggestive of a recent diagnosis, such as “I was told I have pancreatic cancer, what to expect.” Then the researchers went back months before these queries were made and looked for earlier ones indicating symptoms or risk factors, such as blood clots and unexplained weight loss. They concluded that their statistical classifiers “can identify 5% to 15% of cases, while preserving extremely low false-positive rates (0.00001 to 0.0001)”, and that “this screening capability could increase 5-year survival.” The New York Times reported: “The study suggests that early screening can increase the five-year survival rate of pancreatic patients to 5 to 7 percent, from just 3 percent.”

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How #bigdata is being mobilised in the fight against leukaemia @ConversationUK @GUcancersci

In a project funded by Bloodwise and the Scottish Cancer Foundation, we have created LEUKomics. This online data portal brings together a wealth of CML gene expression data from specialised laboratories across the globe | Lorna Jackson & Lisa Hopcroft for The Conversation

Leucemia mieloide cronica (LMC)

Image source: Paulo Henrique Orlandi Mourao – Wikimedia // CC BY-SA 3.0

Our intention is to eliminate the bottleneck surrounding big data analysis in CML. Each dataset is subjected to manual quality checks, and all the necessary computational processing to extract information on gene expression. This enables immediate access to and interpretation of data that previously would not have been easily accessible to academics or clinicians without training in specialised computational approaches.

Consolidating these data into a single resource also allows large-scale, computationally-intensive research efforts by bioinformaticians (specialists in the analysis of big data in biology). From a computational perspective, the fact that CML is caused by a single mutation makes it an attractive disease model for cancer stem cells. However, existing datasets tend to have small sample numbers, which can limit their potential.

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Reactions on Twitter to updated alcohol guidelines in the UK

Stautz K. et al. (2017) BMJ Open. 7:e015493

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Objectives: In January 2016, the 4 UK Chief Medical Officers released a public consultation regarding updated guidelines for low-risk alcohol consumption. This study aimed to assess responses to the updated guidelines using comments made on Twitter.

 

Conclusions: This descriptive analysis revealed a number of themes present in unsupportive comments towards the updated UK alcohol guidelines among a largely proalcohol community. An understanding of these may help to tailor effective communication of alcohol and health-related policies, and could inform a more dynamic approach to health communication via social media.

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Analysis of the time and workers needed to conduct systematic reviews of medical interventions

Borah R. et al. (2017) BMJ Open. 7:e012545

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Objectives: To summarise logistical aspects of recently completed systematic reviews that were registered in the International Prospective Register of Systematic Reviews (PROSPERO) registry to quantify the time and resources required to complete such projects.

 

Results: The mean estimated time to complete the project and publish the review was 67.3 weeks (IQR=42). The number of studies found in the literature searches ranged from 27 to 92 020; the mean yield rate of included studies was 2.94% (IQR=2.5); and the mean number of authors per review was 5, SD=3. Funded reviews took significantly longer to complete and publish (mean=42 vs 26 weeks) and involved more authors and team members (mean=6.8 vs 4.8 people) than those that did not report funding (both p<0.001).

Conclusions: Systematic reviews presently take much time and require large amounts of human resources. In the light of the ever-increasing volume of published studies, application of existing computing and informatics technology should be applied to decrease this time and resource burden. We discuss recently published guidelines that provide a framework to make finding and accessing relevant literature less burdensome.

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Transforming community care with digital technologies

Chris Gregory, head of clinical systems for LGSS Local Health and Care Shared Service explains how mobile solutions are transforming the work of community-based health teams | NHE

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As the IT provider to Northamptonshire Healthcare NHS FT, LGSS has been involved in delivering mobile working solutions to a number of community-based health teams, including health visitors and district nurses, and for providing similar solutions in local government.

The trend towards delivering care closer to home to meet both patient aspirations, and the need to deliver savings through the reduction of estate, means that increasing levels of flexible working are being demanded across the NHS. If done successfully, mobile working can help to deliver the type of service that patients tell us they would like from their health service.

As with many IT services we’ve had a few attempts at delivering practical mobile working solutions, each based on and constrained by the technology available at the time. Prior to our latest deployment, we asked staff what they needed from a mobile device. Overwhelmingly, those who responded wanted:

  • A small form factor: There is plenty of other equipment a district nurse needs to carry so devices need to be small, as light as possible and certainly no more awkward to carry than the files of paper notes previously used
  • Sufficient battery life to get through an entire working day
  • A fast start-up: Ensuring that as little of the precious contact time with the patient was spent waiting for the technology
  • Versatility: Multiple means of inputting data, suggesting the need for both touchscreen and keyboard input

Read the full article here

Can big data help cancer patients avoid ER visits?

For this project, doctors and data miners are specifically focusing on lung cancer patients | ScienceDaily

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By flagging things like recent lab tests, radiology visits, or patient-reported symptoms, Penn’s team is hoping to come up with a formula that will predict when a patient is likely to end up visiting the emergency room. Right now, the formula can predict an estimated one out of every three ER visits, giving doctors the chance to take action before a patient gets to that point.

Read the full overview here

PODCAST: Big Data – what effect is it going to have on EBM

In this discussion we went to the The Farr Institute which is a of 21 academic institutions and health partners in the UK – whose mission is to deliver high-quality, cutting-edge research using ‘big data” | BMJ Talk Medicine

We know what the problems are – but what would positive change, when it comes to the creation and use of medical evidence look like? To find out we’re doing a series of discussions at various places around the world – where we’re talking to people who have a particular insight into one area of the evidence ecosystem. Ultimately we’re collating this into what we’re calling the evidence manifesto.

Read the full over view here