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  • ๐Ÿ’กLitepaper
  • โ˜ ๏ธIntroduction and Challenges
  • ๐Ÿ’กSolution
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  • โค๏ธIncentivization
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  • ๐Ÿ”“Data Security
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  • ๐Ÿ”ŒPlugins
    • ๐Ÿ•ธ๏ธApi Integration Plugins
    • ๐Ÿ–ฅ๏ธData Analysis Plugins
    • ๐Ÿ“ฒData Collection Plugins
    • ๐Ÿ“‘Observational Studies Plugin
    • ๐Ÿ’‰OpenCures Trial Management Plugin
    • ๐Ÿค–Optomitron Real-Time Decision Support Plugin
    • ๐Ÿท๏ธOutcome Labels Plugin
    • ๐Ÿฅ•Root Cause Analysis Plugin
    • ๐Ÿ”ŽPredictor Search Engine Plugin
  • ๐Ÿ“–Reference Databases
    • ๐ŸฉธBiomarker Databases
    • ๐ŸคฎDiseases
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  • Data Types
  • Automated Data Acquisition

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  1. Plugins

Data Collection Plugins

Data collection can be done using wearable sensors, third-party applications, and client applications.

PreviousData Analysis PluginsNextObservational Studies Plugin

Last updated 3 years ago

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Data collection can be done using wearable sensors, third-party applications, and client applications.

Data Types

Ultimately, the system should be able to handle the following data types:

  • Omics data (e.g., genomics, proteomics, metabolomics, etc.)

  • Image and physiological data (e.g., CT, PET/SPECT, sMRI, fMRI, rMRI, DTI, EEG, MEG, ultrasound, cellular level imaging, multi-electrode recording, etc.)

  • Clinical data (e.g., lab tests, pathology, imaging, diagnosis, electronic health records, etc.)

  • Multiscale data (genomic, epigenomic, subcellular, cellular, network, organ, systems, organism, population levels)

  • Multiplatform data (desktop, cloud-based storage, etc.)

  • Data from multiple research areas and diseases (e.g., common inflammatory pathways in cancer, obesity, immune diseases, and neurodegenerative diseases)

  • Data with special considerations (e.g., sparse data, heterogeneous data, very large or very small datasets)

  • Human-computer interfaces and visualization

Automated Data Acquisition

At the present time, it requires a great deal of effort and diligence on the part of the self-tracker to gather all of the data required to identify the triggers of mental illness and quantify the effectiveness of different treatments. Tracking oneโ€™s mood, diet, sleep, activity, and medication intake can be extremely time-consuming. The present invention automatically pulls data from a number of data sources (adding more all the time).

The data sources would include:

  • Biometric Devices: that could measure vital signs and biomarkers

  • Purchase Records: Data regarding consumption of foods and supplements could be automatically collected by and inferred from receipts or other financial aggregation services like Mint.com.

  • Auditory Records: Voice recognition may be used to quantify emotion through conscious verbal expression, spectral analysis of the magnitudes of different frequencies of speech would probably be a better means of quantifying unconscious human affect and thus providing more accurate data for the machine learning process. CommonSense is a cloud-based platform for sensor data.

  • Visual Affect Data via Web-Cameras: By tracking hundreds of points on the subjectsโ€™ faces, InSight can accurately capture emotional states.

  • Prescription Records: Microsoft HealthVault can automatically collect lab results, prescription history, and visit records from a growing list of labs, pharmacies, hospitals, and clinics.

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Diagram showing data collection ranging from human and public data sources ultimately ending up at the aggregation layer(30)
data import
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