Commoditize Health

Commoditize Health

Colon Cancer

Due to my goal of improving the performance of some biotech algorithms 1000x to start saving money, I have come to the conclusion that we live in the stone age for health. I should not have to do anything to be healthy.

We need a zero-friction, 24x7x365, full-body, real-time, atomic-level precision, health diagnostic tool that knows when something is fucking us up, like my hemorrhoid that might be cancer, and fixes it— all without me having to think about it.

And just like how Amazon + SpaceX bootstrapped knowledge in a new domain through business + engineering, not science, we need a flywheel that does this. A self-sustaining, network-growing data flywheel that both creates the mathematical equation while getting live feedback.

Reddit threads that inspired me:

https://www.reddit.com/r/cancer/comments/90vruy/serious_redditors_with_colon_cancer_what_were/

https://www.reddit.com/r/ostomy/comments/18kucqu/people_who_got_colon_cancer_in_their_20s_or_30s/

image
image
image

The Device

1. Signal Acquisition

What can we sense cheaply, non-invasively, and continuously— today?

  • OpenBCI: EEG/EMG/ECG open source biosensors
  • Empatica E4 SDK: Physiological data streams (heart rate, EDA, temp)
  • HealthyPi v5: Raspberry Pi-based open-source vital signs monitor
  • OpenHealth: Wearable platform for real-time biomedical data

2. Data Unification + Inference

A NN that unifies scattered biological signals and discovers patterns.

  • OpenMined / PySyft: Privacy-preserving federated learning
  • Nilearn + MONAI: Medical imaging + signal processing
  • NightShift / Circadia: Circadian + chronobiology signal modeling
  • VitalDB: ICU signal dataset for training real-time predictive models

3. Disease Prediction + Interpretation

Focused pipelines we can open-source, scale, and later integrate.

  • DeepHealth (MIT) or DeepMed: Cancer classification pipelines
  • lifelines: Survival models for predicting disease progression
  • GenoML / BioAutoML: Genomic machine learning made modular
  • FHE-ML (Susakshai-style): Secure inference without exposing raw data

4. Real-Time Infrastructure

After we have captured all information, we need to process signals and actually garner useful patterns to build systems off of.

  • Home Assistant + MQTT + Node-RED: Modular edge monitoring
  • Edge Impulse: Real-time ML inference at the edge (on-device health AI)
  • Prometheus + Grafana: Real-time observability stack for your own body

Immediate Starter Project (Modular, Real, Expandable):

Build an open-source pipeline that:

  • Takes in EKG, HRV, SpO2 from OpenBCI or HealthyPi
  • Runs real-time models on edge (via Edge Impulse or PyTorch Mobile)
  • Streams output to a Grafana dashboard with alerts (e.g., abnormal arrhythmia)
  • Adds federated learning support via OpenMined to protect user privacy
  • Long-term: plug in blood biomarkers, microbiome data, genome