Client Background

Client Name: Confidential (Healthcare Research Organization)

Industry Type: Medical Research & Digital Health

Products & Services: Clinical evidence platforms, therapeutic research engines, vibration-based therapy validation systems

Organization Size: 100+

About Client:

The client operates a proprietary research platform focused on vibration-based therapeutic interventions. Their system ingests global biomedical research to build structured, validated clinical evidence used by healthcare professionals, device manufacturers, and research teams.

Their platform requires extremely high-precision scientific inputs — incorrect or irrelevant studies directly degrade downstream AI models, clinical analytics, and regulatory-grade evidence engines.

The Problem

The client needed to build a high-quality dataset of human clinical vibration therapy studies from a massive universe of global biomedical literature.

However:

Most vibration-related papers are noise, not therapy:

• Engineering vibration
• Haptics and VR
• Perception studies
• Device calibration
• Animal or simulation research

Their internal data engine was rejecting 40–60% of papers due to poor filtering

They needed:

• Only human therapeutic vibration studies
• Only studies with functional or clinical outcomes
• Only studies that matched strict intervention criteria
• And only five clean identity fields for ingestion:
  • Title
  • Authors
  • Year
  • DOI
  • PubMed link

The client estimated ~1,000 valid studies but had no reliable way to isolate them without manual scientific screening.

Our Solution

We built a hybrid AI + human validation research pipeline that:

1. Crawled and extracted thousands of vibration-related biomedical papers
2. Applied strict medical inclusion rules based on the client’s Vibragenix pre-screen framework
3. Eliminated irrelevant domains such as:
   1. Haptics
   2. Perceptual psychophysics
   3. VR vibration
   4. Engineering and hardware calibration
4. Retained only studies where:
   1. Vibration was the therapeutic independent variable
   2. Subjects were human
   3. Outcomes were clinical or functional
   4. Intervention parameters existed

Each retained study was then manually verified by analysts against the inclusion checklist before being accepted.

This ensured the dataset met clinical-grade quality, not just keyword relevance.

Solution Architecture

1. Automated Research Discovery
   1. Search queries across PubMed, CrossRef, Google Scholar, and medical repositories
   2. Keyword clustering for vibration modalities (WBV, LMV, vibroacoustic, etc.)
2. AI-Based Pre-Filtering
   1. NLP models filtered abstracts for:
      1. Human subjects
      2. Therapeutic intent
      3. Clinical outcomes
      4. Intervention details
3. Vibragenix Rule Engine
   1. Applied the client’s six-point pre-screen:
      1. Modality validity
      2. Clinical outcomes
      3. Vibration as intervention
      4. Human-only
      5. Intervention detail
      6. Cluster mapping 
4. Manual Scientific Validation
   1. Trained analysts reviewed every candidate paper
   2. Borderline cases were rejected to preserve dataset purity
5. Final Identity-Only Export
   1. Only the five required fields were delivered, ensuring seamless ingestion into the client’s Evidence Engine 

Deliverables

A 100% validated dataset of vibration therapy studies containing:

• Study Title
• Authors
• Publication Year
• DOI
• PubMed URL

Delivered in structured spreadsheet format ready for ingestion into the client’s research platform

Unlimited corrections and clean-up cycles were supported until the dataset met full accuracy.

Tech Stack

• Framework used

Custom data extraction pipelines, validation workflows

• Language/techniques used

Python, NLP text mining, rule-based filtering, manual QA loops

• Models used

NLP classification models, Keyword & semantic similarity engines, Rule-driven validation layers

• Skills used

Medical data extraction
Clinical study screening

Data validation

AI-assisted research curation

Quality assurance

• Databases used

Structured CSV / Excel datasets

• Web Cloud Servers used

Cloud-hosted data processing and storage environments

What are the technical Challenges Faced during Project Execution

• Massive false-positive volume from vibration keywords
• Abstracts often misleading about whether vibration was therapeutic
• Many papers mixed vibration with unrelated device testing
• Inconsistent DOI and PubMed metadata across sources
• Need for zero-tolerance errors due to clinical downstream use

How the Technical Challenges were Solved

We used layered filtering instead of naive search:

• AI models removed obvious noise
• Vibragenix rule-sets eliminated domain mismatches
• Human validators enforced medical interpretation
• DOI and PubMed fields were cross-verified against authoritative sources
• Continuous feedback loops refined inclusion accuracy over time

This prevented junk science from entering the final dataset.

Business Impact

• Reduced client ingestion rejection rate by over 60%
• Created a clean, high-signal research corpus for their Evidence Engine
• Accelerated their ability to:
  • Train AI models
  • Validate therapeutic claims
  • Support regulatory and clinical workflows
• Eliminated weeks of internal manual screening work

The client received more valid studies than originally estimated without additional cost, increasing the value of their research pipeline

Project Snapshots

Project Video