AI/ML-Empowered Data Analytics
for Digital Health

  • Health descriptive analytics
  • Health diagnostics
  • Predictive health analytics
  • Prescriptive & preventive care

1. Descriptive Analytics in Working with Massive Health Data

For starters, let’s pay attention to descriptive analytics. This type is the simplest but no less important than the others.

  • Data mining — this method includes working with sources like medical records (text, audio), pictures (X-rays, TMS), and biosignals (ECG, EEG) for data structuring and evaluation. In those cases, the main LM-tools are NLP, CV, and DSP. As a result, the benefits gained include the ability to improve electronic medical records.
  • Data visualization and reporting — these techniques use classical statistics libraries and visualization libraries to improve health reporting for doctors and providers.
  • Data cloud integration, discovery, and processing — these means derive insights and build additional value. These days, cloud services like Amazon provide infrastructure for working with health data. This approach allows digital health services to be delivered.

2. Diagnostic Health Analytics for Better Medical Treatments

The second type of health analytics is diagnostics. This is one of the most popular types and one of the most important since it is used to diagnose various pathologies and diseases.

  • Symptom-based diagnosing — this method uses classification models (single-, multi-label, or even multi-output regression) based on Logistics Regression, NN, or decision trees, to serve as the best choice. The obvious application is for clinical decision support systems.
  • Biomedical image analysis — this technique uses Computer Vision (CV) and is usually based on deep (deep learning) convolutional neural networks (CNN). This approach allows for direct pathology detection and computer-assisted diagnosis.
  • Big data and bioinformatics — this approach uses almost all ranges of ML/AI algorithms for early detection and population health management.

3. Predictive Health Analytics to Foresee Risks

The following vital type of data analytics in healthcare is predictive analytics.

  • Trend and pattern recognition — this approach offers essential tools for predictive analytics. In particular, decision trees, regressions, and neural networks are successfully applied for outbreak and chronic disease management.
  • Multivariate statistics — these methods are actually used in classic ML tools and are able to find complex dependencies that are not often obvious. These hidden dependencies can be helpful for outcomes, readmission, and risk foreseeing.
  • Data modeling — this technique, in line with mathematical and statistical models, provides powerful instruments for medical and clinical research.

4. Prescriptive Analytics for Health Improvement

Last but not least, we will explain prescriptive, also called predictive, analytics.

Health Analytics Types, Use cases, ML/AI Technologies & Tools: Infographic

Neurons Lab Solutions for Creating Health Analytics Products

Neurons Lab has been operating in the AI/ML domain for 10+ years and has completed a few dominant projects that improved the healthcare sector.

  • Expertise in the intersection of AI, advanced science, and business that provides a unique team composed of a PhD-level applied scientists, recognized DS/ML/AI Engineers, and MLOps
  • Managed capacity engagement model with fast team allocation as well as efficient and transparent agile delivery process
  • Solution accelerators with a fast iterative R&D deployment approach for POC/MVP delivery and cloud/edge deployment



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Neurons Lab

Neurons Lab

We are a group of scientists, engineers, and developers who are passionate to revolutionize the future of businesses with AI and machine learning technologies.