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Sahha Data Analysis

· 3 min read

Definitions and understanding the data outputs from the Sahha API

Sahha

Terms & Definitions​

  • State: The condition assessed - ie: depression

  • Sub-state: The severity of the condition assessed ie: ‘mild’.

  • Probability: the probability of each state

  • Data source: The data types that have been used to predict the state ie: sleep time, age

  • Specificity: The ability of a model to correctly identify people without the condition - the probability of a negative assessment (AKA true negative rate)

  • Sensitivity: The ability of a model to correctly identify people with the condition - the probability of a positive assessment (AKA true positive rate)

  • Score: The predicted instrument score ie: PHQ9 Score of 16

  • Sub-state probabilities: the probabilities for each sub state ie: 0.9 [high probability of no depression], 0.3 [low probability of mild depression]...

  • Score confidence interval: the standard deviation ie: 13, 19 2 standard deviations from a score of 16

  • Score error: the score error margin ie: +-3 from score of 16

Current Status:​

Binary Classification:​

Currently this model offers:

  • sensitivity (or true positive rate, the ability to detect the depressed) of 0.75 or 75%

  • specificity (or true negative rate, the ability to detect the non-depressed) of 0.59 or 59%

In order to create a confident analysis 4-7 days of data is required for each profile’s first analysis.

Multi-class Classification: Currently unavailable

Types of Analysis Sahha provides​

Currently Sahha provides two levels of analysis, a ‘simple’ version and a more data rich version. More detailed and immediately available analysis types will be available soon.

Binary Classification​

A “simple” analysis providing a binary output of either depressed or not depressed as well as a probability score indicating the likelihood the analysis is correct.

Binary Classification output:​

{
    probability: 0.75
    state: "depressed"
    data_souce: ["screentime", "sleeptime", "age"]
}

Binary classification use cases:​

  • Provide a simple indication of mental state of a user

  • Use to enrich data for better feature delivery

Multi-class Classification (Coming soon)​

A more feature rich classification output providing more detail and sub-state analysis.

Multi class classification output:​

{
    probability: 0.8,
    state: "depressed",
    substate: "moderate",
    substate_probabilities: [0.9, 0.1, 0, 0],
    score: 16,
    score_confidence_interval: (13, 19),
    score_error: 3
    data_souce: ["screentime", "sleeptime", "age"]
}

Multi-class classification use cases:​

  • Provide a detailed indication of mental state of a user

  • Use to enrich data for better feature delivery

  • Use to deliver specific interventions with more confidence

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