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Machine Learning · ML-11

Predict recorded chronic-care progression

Explore temporal models of specified chronic-care events using diagnosis timing, encounter sequences and life circumstances.

  • Synthetic records
  • Longitudinal
  • Nairobi + Lagos
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Scope & purpose

Connected scope

Connect recorded conditions and encounters with demographic, household and employment context to explore health timelines.

  • Health
  • Demographics
  • Employment
  • Households

Define the prediction date and horizon. Keep earlier inputs separate from later outcomes.

Possible uses

Prediction prototyping · Feature exploration · Temporal validation · Cohort comparison

Who it is for

Health researchers · analytics teams · universities · policy groups

Preview type
Illustrative format preview
Shows example structure and fields. It is not a sample extracted from the paid release.
Commercial availability
Confirmed in your written quote
The listed cities describe catalogue coverage. We confirm the exact release and available extract before agreement.
Starting-price basis
USD per selected city
Cohort size and period are specified in the quote; the listed rate does not define a fixed-size package. Discount and tax are shown separately.
Your agreed delivery
Scope and supporting artifacts listed in the quote
Confirm entities or rows, observation period, fields, formats, documentation, evidence, known limits and usage rights.

Paid dataset delivery

What you receive

SD delivery standard
Published cities
Nairobi + Lagos
Record scope
Resident-level
Structure
Longitudinal

Striped Donkey delivery package

  • data/Agreed extract and formats
  • documentation/Schema, fields and relationships
  • evidence/Agreed release checks and limits
  • examples/Agreed loading and join examples
  • LICENSEApplicable rights
  • MANIFESTRelease and file inventory
  • CHECKSUMSSHA-256 fingerprints
  • DELIVERY_NOTEScope and handoff record

Your written quote confirms selected cities, period, formats, commercial rights, detailed artifacts and secure delivery. The complete paid package is prepared privately after agreement.

Free evaluation preview

Inspect before you request

15 illustrative rows · 11 fields · CSV

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Illustrative rows show the format. Your quote confirms delivered records, fields and time coverage.

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Fields and record structure

Listed features

Resident-level · Longitudinal

  • diagnosis timing
  • encounter sequence
  • progression events
  • dated encounters
  • care history
  • health-state events
  • age / life stage

Study question

Evaluation design

Can diagnosis and encounter histories predict the timing and type of the next specified chronic-care event within a defined care pathway?

Name the eligible condition and event labels from the release; preserve censoring and distinguish a newly recorded event from biological disease progression.

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Evidence you can inspect

Source & limitations

Review release-specific methods and published checks. Preview rows show format and scope; they do not certify a dataset.

Supporting references
NairobiEvidenceSamples
LagosEvidenceSamples
MethodsGenerationReproduce

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Licence & delivery

Free preview

Use free samples under the public-use policy or their dataset-specific licence. Attribution is required for publication and research outputs.

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Purchased dataset

Your agreement defines the release, licensed organization, permitted uses and duration. Commercial model training, external applications and redistribution require explicit agreement.

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