Clinical trial analytics

Use Snorkel Flow to extract invaluable data from clinical trials and patient records, assisting with trial design, optimization, site selection, and patient recruiting.

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Gain deeper insights

With Snorkel Flow, you can develop classifiers and extractors for inclusion/exclusion criteria or protocol design using public and private data sources to train state-of-the-art models—without the bottleneck of manual labeling.

Privacy-safe labeling

Generate training data in a privacy-safe, compliant fashion using powerful labeling functions from all types of patient records, faxes, scans, reports, and billing codes.
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Patient
records
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Billing codes
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Faxes
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Reports
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Scans

High-accuracy models

Customize state-of-the-art models by training them using data from your patient pools and specifically for your trial objectives.
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Faster development

Go beyond hand-labeling and reduce development time and cost using programmatic labeling techniques.
What are the possible risks and side effects of taking part 
in the study? [CONSEQUENCES]
Who will pay for the tests and treatments I receive? [FINANCE]
Does the service keep my information confidential? [PRIVACY]
How long will the trial last? [DURATION]

Adaptable applications

Adapt applications to new or changing data points (such as test results, latest medication, current prognosis, and more) or trial objectives with few clicks.
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Data-centric AI

Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
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Accelerated

Save time and costs by replacing manual labeling with rapid, programmatic labeling.
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Adaptable

Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
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Collaborative

Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
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Accurate

Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
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Governable

Version and audit data like code, leading to more responsive and ethical deployments.
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Private

Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.


Case study

Fortune 500 biotech

A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read more



Problem

Building AI applications to extract entities requires high domain expertise and large amounts of labeled training data, which is expensive and time consuming.

Solution

With Snorkel Flow they built a custom model with 99.1% accuracy by adjusting label schema and re-labeling programmatically.

Results

With Snorkel Flow, this biotech giant programmatically labeled ~300K documents in minutes versus using manual labeling, all while saving $10M in costs.

$10M

saved on labeling for extraction

99.1%

accuracy on complex ML pipeline

1 day

vs. 1 year to adjust label schema


Case Study

Fortune 500 Biotech

A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read more



Problem

Building AI applications to extract entities requires high domain expertise, and large amounts of labeled training data, which is expensive and time consuming.

$10M

saved on labeling for extraction

Solution

Used Snorkel Flow to build a custom model with 99.1% accuracy by adjusting label schema and re-labeling done in hours.

99.1%

accuracy on complex ML pipeline

Results

With Snorkel Flow, this biotech giant programmatically labeled ~300k documents in minutes versus using manual labeling, all while saving $10M in costs.

1 day

vs. 1 year to adjust label schema


Case Study

Fortune 500 Biotech

A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read More



Problem

Building AI applications to extract entities requires high domain expertise, and large amounts of labeled training data, which is expensive and time consuming.

Solution

Used Snorkel Flow to build a custom model with 99.1% accuracy by adjusting label schema and re-labeling done in hours.

Results

With Snorkel Flow, this biotech giant programmatically labeled ~300k documents in minutes versus using manual labeling, all while saving $10M in costs.

$10M

saved on labeling for extraction

99.1%

accuracy on complex ML pipeline

1 day

vs. 1 year to adjust label schema

A radically new approach to AI

Conventional AI approaches rely on generic third-party models, or brittle rule-based systems, or armies of human labelers. With Snorkel Flow, programmatically labeling unlocks a new workflow that accelerates AI app development.

With Snorkel Flow

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Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
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Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
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Label thousands of data points programmatically in hours while keeping your data in-house and private.

With conventional approaches

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Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
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Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
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Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.
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Let's connect

Speed time to value, reduce costs, and unlock more AI possibility with the Snorkel Flow platform.
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