Healthcare and BioTech

Accelerate insights from ML with data pipelines and autoscaling across BioTech, AgTech, Pharma, Genomics, healthcare, and general Life Sciences use cases.

TYPICAL DATA SOURCES

PATIENT RECORDS

Medical patient records include documents like progress notes, discharge summaries, test results, and medication lists.

X-RAY AND IMAGES

Medical images like x-rays, CT scans, MRI, ultrasound, and PET scans produce detailed images of the body for diagnosis and treatment.

genomic DATA

Genomic data like DNA sequence data, gene expression data, genetic variation data, epigenetic data and functional genomics data,

Top use cases with pachyderm

Modernizing care infrastructure

Processing millions of patient records, applying OCR and natural language processing to derive meaning from unstructured data and clinician audio recordings form the basis of modernizing patient records. With this modernization, data science teams can create ML models that improve patient diagnosis, patient treatment plans, and better outcomes.

Improved diagnostics

Bone density scans are often used to diagnose or assess the risk of osteoporosis. The problem with current manual processes is they are error-prone and inconsistent. New systems based on ML image processing are replacing these systems resulting in better osteoporosis detection, fewer false positives, and a better healthcare experience.

Genomic sequencing

A liquid biopsy can identify infectious diseases or biomarkers of pathogens freeing clinicians from the diagnostic maze so they can return patients back to health quickly and safely. ML models help clinicians avoid invasive, low-yield, and sequential diagnostic tests that can delay treatment for the most vulnerable hospitalized patients.

Optimized treatment

Before enhanced image processing and ML models, the existing processes to determine tumor size and volume relied on manual measurements by the oncologist, which is error-prone and inconsistent. New systems use ML models that can create volumetric calculations based on ultrasound images and determine cancer growth projections.

Pachyderm has enabled us to rapidly build and maintain a robust and automated data science pipeline that is scalable and completely reproducible.

Some Healthcare and Biotech Customers

Healthcare

Actionable Medical Insights from Terabytes of Clinical Data

Healthcare

Actionable Medical Insights from Terabytes of Clinical Data

BioTech

Finding the Next-Generation of Plant Microbiomes

Machine Learning for Heathcare

Investment in healthcare and life sciences continues to rise, driven by an aging population and the promise of new drugs and breakthrough technologies. The rapid development, creation and rollout of the new COVID vaccines and the promise of RSV vaccines continues to energize this market. Machine learning has played a role in developing these breakthroughs and will do so for future advances.

However, there are some unique challenges to developing ML models for healthcare and life sciences:

Data is unstructured

Most healthcare data isn’t stored in a database or files but in physical charts, EMRs, X-Rays, MRIs, audio files, and even DNA sequences.

Data sets are disparate

Data is in different formats (text, images, video and audio) and spread across different systems from providers to payors.

Data sets are large

Most use cases have petabytes of data and millions of records that need to be continually processed to derive accurate results.

Reproducibility is key

Organizations need to reproduce any outcome by identifying what data was used and what models were to used to produce what results.

Data changes frequently

The ML model is relatively static in comparison to the volumes of data being changed and updated.

Time for experimentation

The time required for running and re-running projects can be prohibitive on large complete data sets.

Pachyderm's Unique Approach

Pachyderm empowers data engineering teams to automate complex data pipelines. Our unique architecture is cost-effective at scale and enables sophisticated data transformations across any type of data. We provide auto-scaling and parallelized processing of multi-stage, language-agnostic pipelines with data versioning and data lineage tracking..

SUPPORT ALL DATA TYPES

Work with structured data such as EMRs, test results, claims data, and unstructured data such as PET, CAT, MRI scans, X-rays, audio recordings, and ultrasounds.

AGGREGATE ANY DATA

Connect to any data sets across on-premise and cloud environments and perform complex transformations on the data using any language and any ML Library.

Petabyte Scale

Scale to any size dataset and millions of records using autoscaling and parallel processing, enabling frequent model iterations and faster data processing.

Immutable Lineage

All data sets, data pipelines, and metadata are automatically version-controlled, providing an immutable record allowing any outcome to be reproduced and audited.

Automatic Processing

Data changes are automatically detected, and dependent pipelines run on only the changed data. This ensures that all models use the most current information providing accurate results.

Fast iterations

Supports parallel and concurrent processing, reducing the time required to run data pipelines and iterate on ML models, helping to converge on a champion model faster.

See Pachyderm In Action

Watch a short demo which outlines the product in action

Transform your data pipeline

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