AI
ML in Health: Allen alliance fights Corona
![](https://informator.se/wp-content/uploads/2020/04/milanin_blogi_0904.jpg)
Since my last post on Health and (Federated) Machine Learning, the tech sector’s involvement has multiplied, almost day by day. MS Research & AI 2 (the Allen Institute for AI) partnered with leading research groups to prepare and distribute for free the COVID-19 Open Research Dataset (CORD-19). Mining useful insights from this vast-and-growing data is a goldmine for analytics, NLP, and Deep Learning.
The CORD-19 AI Challenge
Canadian BlueDot and a couple of similar AI-based warning systems showed, weeks ahead of the WHO, that public authorities worldwide were using outdated analytics tools in forecasting and warning. Hence the Tasks section of the CORD-19 dataset: “a call to action to the world’s artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers … …data mining approaches to find answers to questions within, and connect insights across, this content… …What do we know about: ”
- Virus Transmission, incubation, stability, seasonality, persistence, protective gear
- Medical Care, challenges, solutions, best practices, management, shortages, telemed, home care
- Risk Factors, mitigation measures, pre-existing diseases, smoking, etc.
- Virus Origins & Evolution, variations, different strains, animal/livestock hosts
- Interventions to prevent community spread, such as school closures or travel bans etc.
- Vaccines and treatments, drugs in development, clinical studies
- Diagnostics & Surveillance, screening, early detection, sampling methods, tradeoffs between speed, accuracy and accessibility (of tests).
- Ethical Considerations, social science research, needs of caregivers, identifying misinformation during outbreaks etc.
- Information Sharing, data-collection standards, communication methods, coordination of local and Federal, private, public, non-commercial and academic communities.
And the response of tech firms was?
Overwhelming.In a few days, AI people volunteered by thousands. From one company alone (Ericsson, to pick just one example), 350+ employees took part: data scientists, data engineers, data visualizers, PM’s, task managers, leaders…
For operational AI, there’s a need for much more data, up to date, with less “noise” and outlier data (“far-off” values). This is improving as you go, in pace with the spread of COVID-19 and international collaboration. Also, Google, FB etc. are trying to reduce noise “out there.” During pandemics, text mining from social media in general is a useful training-data source for ML systems (but, consequently, also a source of noise, outliers, or outright fakes).
Several open-source apps are emerging. Among the first, CovNet for diagnostics based on pattern-recognition in lung images (to complement COVID testkits and, that way, to lift the accuracy of diagnosis above 98%).
IMO, the underlying constraints on ownership/tenancy/privacy/transfers of medical data, along with urgency and tight deadlines, will make Federated ML and distributed techniques a lot more popular in health apps, if not absolutely necessary (see also the last paragraphs of the previous post).
Federated, UML sequence diagram (Informator course AI, Architecture, and Machine Learning)
![](https://informator.se/wp-content/uploads/2020/04/Milan_0904-840x548.png)
Instead of sending the sensitive data to the computation, architects shall remember the “D” in SOLID and do their best to send the computation (here, as a vector of parameters) the other way, to the privately owned data.
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