The Crowdsourcing Cures app collects and aggregates data on symptoms, diet, sleep, exercise, weather, medication, and anything else from dozens of life-tracking apps and devices. Then it analyzes this data to reveal hidden factors exacerbating or improving symptoms of chronic illness.
- Automatically identify food sensitivities
- Determine personalized optimal daily values for dietary intake.
- Quantify the effectiveness of drugs, supplements, and treatments.
- Calculate optimal doses of nutrients, ingredients, and other wellness factors.
- Determine optimal environmental parameters such as humidity, temperature, and light exposure.
- Determine the optimal amount of physical activity and the best forms of physical activity, intensity, and duration.
- Determine the optimal amount of sleep and the optimal time to go to sleep and the optimal conditions for sleep.
- Determine the amount of time it takes to build a tolerance to a drug and the length of the withdrawal period from the drug.
- Easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second
- Add notes with your ratings
- Create reminders to track treatments, symptoms, emotions, diet, physical activity, and anything else that could influence your outcome of interest
- Import your data from over 30 other apps and devices like
- Analyze your data to identify which hidden factors are most likely to be influencing your mood or symptoms and their optimal daily values
- View mood trends helping to identify triggers for symptoms and identify the potential effects of treatments
- Export and email your data to your healthcare provider
- Create and publish studies using your data or aggregated user data
- Search for predictors and see the most significant factors influencing your conditions and their optimal daily values
- Make informed changes and optimize your life!
The Crowdsourcing Cures Connector Framework imports and normalizes data on all quantifiable aspects of human existence (sleep, mood, medication, diet, exercise, etc.) from dozens of applications and devices including:
- Rescuetime – Time Tracking
- WhatPulse – Keystroke and Mouse Behaviour
- Fitbit – Sleep Duration, Sleep Quality, Steps, Physical Activity
- Withings – Blood Pressure, Weight, Environmental CO2 Levels, Ambient Temperature
- Weather – Local Humidity, Cloud Cover, Temperature
- Facebook – Social Interaction, Likes
- Github – Code Commits
- MyFitnessPal – Food and Nutrient Intake
- MoodPanda – Basic Reported Mood
- MoodScope – Detailed Reported Mood Using Panas
- Sleep as Android – Snoring, Deep Sleep, Reported Sleep Rating
- BodyMedia – Sleep Duration, Sleep Quality, Steps, Physical Activity
- RunKeeper – Physical Activity
- MyNetDiary – Food and Nutrient Intake, Vital Signs
Rate their symptom severity within a fraction of a second using a unique popup interface.
The Crowdsourcing Cures Analytics Engine mines data using powerful, proprietary algorithms which perform temporal precedence accounting, longitudinal data aggregation, erroneous data filtering, unit conversions, ingredient tagging, and variable grouping to quantify correlations between symptoms, treatments, and other factors.
It then pairs every combination of variables and identifies likely causal relationships using correlation mining algorithms in conjunction with a pharmacokinetic model. The algorithms first identify the onset delay and duration of action for each hypothetical cause. It then identifies the optimal daily values for each factor.
With the advent of mobile technologies we are all producing tremendous amounts of “digital exhaust”. Any time we change our diet, environment, medication, exercise routine, or anything else in our daily routine we are creating a “natural experiment”. Throughout history, man has been swimming in an ocean of naturally generated data but it has never been possible to capture and use it. Whereas the invention of the internal combustion engine finally allowed us to exploit fossil fuels to raise the universal standard of living, new mobile technologies are allowing us to finally exploit naturally generated data to improve human existence. There are applications and devices that allow us to effortlessly track sleep, diet, exercise, medications, symptoms, mood, environmental factors and just about anything else.
We now have the opportunity to use machine learning to solve problems that have been plaguing mankind throughout history. Our children will look back on the current era as a Dark Age where millions of people suffered needlessly because health decisions were a product of intuition and data from the mind of a single physician.
The rules of living “the Good Life”, for thousands of years left to the speculation of philosophers, can now be derived as personal mathematical equations for the personal health and happiness of every individual. With this platform, we plan to give birth to a new quantitative scientific discipline of quantitative life optimization.