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Healthcare

Patient outcomes, clinical analytics, and HIPAA-compliant ML pipelines

Predict patient outcomes, optimize clinical trial enrollment, and build diagnostic models — all within HIPAA-compliant pipelines with automated PHI detection and de-identification.

The Challenges

HIPAA Compliance

Protected health information (PHI) must be automatically detected and de-identified before any model training can begin.

Small Dataset Sizes

Rare diseases and specialized conditions result in limited training data, leading to underfitting and poor generalization.

Model Explainability

Clinicians require transparent, interpretable predictions — not black-box scores — to guide treatment decisions.

Data Fragmentation

Patient records are scattered across EHR systems, lab databases, and imaging platforms with inconsistent schemas.

How CorePlexML Helps

Privacy Suite

HIPAA Privacy Profiles

One-click HIPAA compliance scans detect and transform 18+ PHI categories including patient names, MRNs, and diagnosis codes.

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SynthGen

Synthetic Patient Records

SynthGen creates statistically accurate synthetic patient datasets to augment rare condition training data without privacy risk.

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ML Studio

Explainable Predictions

ML Studio provides SHAP contributions, partial dependence plots, and What-If analysis for every prediction.

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Dataset Builder

Conversational Data Prep

Dataset Builder merges, cleans, and normalizes fragmented clinical data through an AI-guided conversational interface.

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SDK Example

example.py
from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Apply HIPAA compliance to patient data
scan = client.privacy.scan(
    dataset_version_id="dsv_patient_records",
    compliance_profile="HIPAA"
)

# Generate synthetic patient records
synth_model = client.synthgen.create_model(
    project_id="proj_clinical",
    dataset_version_id="dsv_patient_records",
    engine="CTGAN",
    epochs=300
)

synthetic_data = client.synthgen.generate(
    model_id=synth_model["id"],
    num_rows=5000
)

Expected Impact

99.7%
PHI Detection Accuracy
0.89
Readmission Prediction AUC
74%
Data Prep Time Saved
< 1 min
Audit Report Generation

Ready to get started?

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