Patient Similarity
background

Patient similarity analytics works as follows:

  • 1. Analyzes aggregated demographic, social, clinical and financial factors along with unstructured data such as medical notes.
  • 2. Enables healthcare professionals to examine thousands of patient characteristics at once to generate personalized treatment plans.
  • 3. Identifies other patients with similar clinical characteristics to see what treatments were most effective or what complications they may have encountered.

business challenges

We had been given the objective to create algorithm that aligns bias in cohorts and implement it into observational studies.


value delivered

Implementing strong new statistical methods has allowed researchers to improve the quality of various studies, avoiding unexpected errors of cohort inclusion criteria and therefore false results. Using it our clients could focus on medical aspects of their research only.


approach

We have created sequential way of dealing with objectives:

  • 1. Propensity score estimation
  • 2. Сhoise of matching algorithm
  • 3. Checking overlap and common support
  • 4. Assessing matching quality and treatment effects and their standard errors estimation
  • 5. Sensitivity analysis of estimated treatment effects with respect to unobserved heterogeneity.


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our expertise in

Health IT Technologies

Data Analytics

Big Data Science
ETL
MapReduce
Hadoop

Data Warehouses

Relational Data Bases
SQL Server
Redshift

Hardware and Infrastructure

Cloud Computing
DevOps
AWS EC2
AWS S3

are you ready to see your software project getting real?

contact us


about us

Hi, we are Sciforce - a company where the integration of various branches of science builds up a powerful force to create robust software solutions. Working at the intersection of Computer Science with other technical, natural and humanitarian sciences let us go beyond traditional IT services and become both technical and scientific forces to our customers.