Concerns over England's new system for collecting general practitioner data
I really don’t think this is a good idea.
The current plan is for GP data for the past 10 years to be extracted and shared with NHS Digital from Sept 1. The data will include information on diagnoses, symptoms, test results, and sexual, physical, and mental health. NHS Digital will also gather information on sex, ethnicity, and sexual orientation. Names and addresses will not be collected, nor will written notes—eg, those detailing conversations between patients and medical staff.
AlphaFold predicts the human proteome
This is truly amazing.
The human genome holds the instructions for more than 20,000 proteins. But only about one-third of those have had their 3D structures determined experimentally. And in many cases, those structures are only partially known.
Now, a transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google’s sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite (see ‘Folding options’).
The approximately 365,000 structure predictions deposited this week
should swell to 130 million — nearly half of all known proteins — by the year’s end, says Sameer Velankar, a structural bioinformatician at
EMBL-EBI. The database will be updated as new proteins are identified
and predictions improved.
NHS opens it’s vaults
From their statement:
NHS Digital is improving transparency about how patient data is accessed by launching its new Data Uses Register.
NHS Digital already publishes details of all the data it releases but this new interactive tool makes it easier to see which organisations access data, the purposes for which they are permitted to use it and the expected benefits.
Organisations using the data may be public sector bodies, charities or commercial organisations. They must all have a legal basis and legitimate need to use the data, which will only be used for health and care planning and research purposes.
Algorithmic Bias Cheatbook
PDF-file can be downloaded here.
This playbook will teach you how to define, measure, and mitigate racial bias in live algorithms. By working through concrete examples—cautionary tales—you’ll learn what bias looks like. You’ll also see reasons for optimism—success stories—that demonstrate how bias can be mitigated, transforming flawed algorithms into tools that fight injustice.
Tech-companies can’t solve health problems alone
From The Verge:
Tech companies were interested in health before the pandemic, and COVID-19 accelerated those initiatives. There may be things that tech companies are better equipped to handle than traditional public health agencies and other public institutions, and the past year showed some of those strengths. But it also showed their weaknesses and underscored the risks to putting health responsibilities in the hands of private companies — which have goals outside of the public good.
I suggest you read the entire article.
