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I am a computer science student and my task is to develop a mobile app for Android and/ or IOS using Artificial Intelligence, designed to help people reduce/ combat depression.Regarding the programming language, I am thinking to go with JavaScript because this is the one I am more comfortable with. I mention that my knowledge on artificial intelligence is little and I haven ' t done anything in AI or of this kind before. I am planning to use AI to help people suffering from depression by creating a mobile app:

  • user-friendly app in terms of design and sequence of actions ,easily customizable looks and functionality
  • to be able to import all the relevant information:key words, text, images, videos from Facebook, Instagram, Twitter to prevent or report the existence of depression and correctly interpret the nature, intensity and frequency of depression symptoms
  • the app includes: a patient profile,an achievement board and a dream chart page, to-do list page, led lights which react to emotions, facial recognition technology, Thinking Patterns page( the app uses the principals of cognitive- behavioral therapy)
  • incorporates sensors that can measure changes in mood, sleep patterns and increased isolation, measuring the device lock time, ambient lighting and audio to predict sleep time
  • utilizes GPS sensor, recent calls to monitor the wellbeing of the patients
  • the app provides users with self-help guidelines/techniques,questionnaire designed to track severity of symptoms over time, educational resources for treatment
  • the users are encouraged to write down daily thoughts in order to analyze and identify negative thinking patterns
  • the app sends notifications to remind the patients to take their medication, is offering activity suggestions
  • the app incorporates appointment scheduler tool for the counseling sessions including private messages, online meetings

    1. How do I get started? What platform would you recommend me to develop a mental health mobile app (for Android and/or iOS) using AI in order to help people suffering from depression to combat it and why? A platform based on cloud or better not?

    2. Can a chat bot be integrated in a mental health mobile app?

    Please, suggest me some related literature, eBooks to set my research objectives because I'm having a hard time finding it. Please, help me!Thank you very much in advance.

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  • $\begingroup$ This is quite a broad question and it might be easier to answer if you said precisely how you were planning to use AI to help people. $\endgroup$ – DrMcCleod Dec 16 '18 at 10:32
  • $\begingroup$ It looks like you've accidentally created two accounts. Please see this help center article if you would like to get them merged together. $\endgroup$ – Ben N May 24 at 18:55
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Addressing your how-to-get-started point.... A currently popular approach to creating intelligent software is to use machine learning. The most popular language for implementing a machine learning system is Python. Take a look at https://scikit-learn.org/stable/ as an example of a machine learning Python framework.

It does help to know what you are doing first though, so I would recommend an online machine learning course such as https://www.coursera.org/learn/machine-learning

If you are intending to create an Android application, these are typically written in Java, using the free Android Studio development environment, https://developer.android.com/studio/ In this case, you might create your app in Android to gather user data and send it to a cloud service for analysis. You wouldn't have to create a cloud service from scratch though as there are online services such as Microsoft AzureML (https://studio.azureml.net) which make it relatively easy to create and deploy online machine learning models based on Python.

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  1. Android provides functionality for running inferences over TensorFlow models.
  2. TensorFlow Lite is an extention of TensorFlow which can run TF models on Android as well as iOS.
  3. For Android, we have various Java dependencies which could get TF Lite directly in code.
  4. TensorFlow, itself is in C++ and is used in Python, but TensorFlowJS is a machine learning framework directly in JavaScript which you are comfortable with.
  5. Since, TensorFlow and Android come from the Google family, most features are compatible with each other.
  6. Also, as a bonus, you can serve TF Lite models via Firebase MLKit, a distribution platform for ML models.
  7. For a chatbot, you can deploy a seq2seq model. They are excellent at natural language processing and used at production level chatbots.
  8. For identifying the daily thoughts and their sentiments, you can use Sentiment Analysis and learn it from here.
  9. For Android use Android Studio and for TensorFlow use PyCharm or Google Colab
  10. Other than ML, Android's Material Design guidelines providea customizable UI options.
  11. Android's SensorManager class provides interface for dealing with Sensors. You can learn further Android skills from here.

Hence use TensorFlow and Android together for the best ML experience.

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Mobile Health Application Development Considerations

Cross development is easier for Android and iOS than it used to be. Open source solutions such as Mobile Angular UI and commercial solutions such as Sencha Touch remove the need to develop two distinct user interfaces. The other excellent option is to use responsive unsophisticated HTML5 and CSS with JavaScript, AJAX, and SVG to create lean web pages and create a system for testing on the most common mobile browsers. In either case, testing on multiple phone models and makes will be necessary.

JavaScript is orders of magnitude slower in calculation intensive processing, so any computationally intensive processes should be made available as an HTTPS RestFUL service, and the transaction must require an authentication token. Use of AI components should make use of this architectural wisdom to produce comfortable response times for mobile users. For artificial networks, there are two approaches.

  • Execute the network on the server, with the RestFUL request containing the authentication token and input tensor and the RestFUL response containing the output tensor and a user-comprehensible exception list.

  • Execute the network on the client, which will require implementing a way to transfer learned parameters to the custom Java and objective-C containers for execution on the Android and iOS clients respectively.

Network training must be on the server and leverage C, C++, or nvcc compiled code, which can also be accessed through Java or Python wrapper libraries.

Mental Health Considerations

Mitigating depression, which in non-clinical terms, is a lack of hope. Clinically, it is usually a genetic vulnerability expressed in the wake of an affecting sequence of some combination of developmental and recent events. It is almost always a complex of interacting factors.

[Wish] to be able to import all the relevant information: key words, text, images, videos from Facebook, Instagram, Twitter to prevent or report the existence of depression and correctly interpret the nature, intensity and frequency of depression symptoms. ... a patient profile ...

This gathering is subject to HIPPA law in the United States, and there is similar law in other jurisdictions, so there will need to be a waiver process integrated in to the sign-up and use of the application. You will need to consult experienced legal talent for both legalese and the requirements regarding integration.

Analysis of the Proposal

There are several inputs mentioned.

  • Social networking content ++
  • Patient profile ++
  • Camera (for facial recognition and ambient lighting)
  • Sensors that can measure changes in mood (not clear what properties would be measured)
  • Device lock time (not clear what this means or why it helps)
  • Audio
  • GPS sensor ++
  • Recent calls ++

There are several models mentioned.

  • Facial recognition (probably employing CNN, along with semi-automatic user assisted panning and ranging to get a good image for recognition)
  • Model of cognitions, such as doubt, doom, and gloom and their converse confidence, hope, and excitement
  • Sleep patterns
  • Increased isolation ++
  • Map of depressing versus encouraging locations (since the GPS is an input) ++
  • Map of depressing versus encouraging contacts ++
  • Map of depressing versus encouraging thought patterns ++

There are several outputs mentioned.

  • Virtual LED lights that react to emotions
  • Guidelines
  • Educational resources
  • Medication reminder
  • Meeting reminder ++

There are several pages mentioned, which would contain both inputs and outputs.

  • Achievement board
  • Dream chart
  • To-do list
  • Thinking patterns ++
  • Questionnaire
  • Daily thoughts ++
  • Techniques and activities ++
  • Counselling scheduler ++
  • Mental health chatbot

Results of Analysis

These pages may be useful but were not explicitly mentioned.

  • Medication check-off ++
  • Proximity stop-go lights ++
  • Contact block recommendations ++

The full set of requirements in the question would require a large project scope, probably requiring years of development and involving a sizable team.

  • An excellent systems architect
  • AI engineers
  • General mobile developers
  • A data base manager
  • Middle tier programmers
  • A high level clinician
  • A product manager
  • A good project manager
  • Team leaders
  • Marketing specialists
  • Web designers
  • A security specialist
  • Access to a health care legal expert
  • Technically savvy senior management

It is feasible to succeed in this domain, but there are already many mobile health care companies that may compete in this particular space. A market advantage will be necessary, which is likely either a very large sum of investment capital or a particularly innovative and efficient approach with an only somewhat large sum of investment capital.

To gain financial traction for a project like this, a set of page markups will not suffice. A more convincing proof of concept will likely be necessary. Consider beginning in phases, starting with ideas that will differentiate the product from others and that are easiest to implement. See the double plus signs above.

The highest degree of difficulty are the chatbot, the to-do list, and the cognition modelling (CBT) capabilities. Avoid placing those in your project scope at the beginning. The ideas are not new, and no one has, with millions in their budget, accomplished much that meets the minimum standards of effectiveness yet.

Don't use a cloud service unless you do your own strong encryption. You don't want legal exposure because someone hacked the cloud security layers and gained access to medical data. There are teams of people who go to work every day just to hack those layers, and some of the attacks succeed nearly every week.

Medical mobile applications are highly proprietary. There are no open source solutions and eBooks will not exist this early in the development of this space because the expertise is narrow and in demand. Those who know how to do it well are constrained by nondisclosure agreements, which is why I can't go into further detail down specific roads.

Expanding on Analysis

These are some thoughts that are not constrained by legal agreements.

The first principle to understand when developing a new product or service is that the simplest models are ones where the correlation between easy to acquire data and various states can be modeled without much computing power. Also, health care related mobile apps will not be used if they require much writing. Everything must be quick and easy, or the usage patterns will suffer. There must also be accountability and incentive, which is part of the creative design of the product or service.

This is one example of such a system, which can be implemented with relative ease compared to the rather large project scoped in the question.

  • Clinicians can sign up and then invite their patients to sign up.
  • Patients can sign up and agree to the legalese prepared by legal talent to comply with medical record legislation.
  • clinicians can write, at the end of sessions, suggestions to the patient.
  • Patients can interact with the suggested items in extremely simple and quick ways.
  • The clinicians can label the data, meaning that they can assign scores in specific categories to what the patients entered.
  • The AI, invisible to all users, then learns what scores are assigned to what kinds of entries so that, at a superficial level, the clinician's expertise as specifically applies to that one patient is trained into an artificial network.
  • Iconic rewards and warnings can then be sent to the patient as a real time guidance device.
  • Proximity stop-go lights can be lit in addition to rewards and warnings. As a patient approaches a location or begins to contact a person that is either helpful or a hindrance, the color of the light indicates whether to go ahead, approach with caution, or stop.
  • Trends across patients and clinicians can be learned so that various locations, contacts, and other easy to identify specifics can be learned as helpful or dangerous without further clinician entries.

Iconic rewards and warnings and stop-go lights are more directly functional than mood lights because mood as a consequence of action is easy to model. Although actions may stem from moods, the measurement of mood poses practical difficulty and is therefore much more difficult to model in a mobile application system, even with the best AI has to offer today.

Very little of this is likely done yet, and to do it well might be highly effective and a market differentiator. It is not patentable because it does not meet the burden of patentability. Specifically a typical solution that any of those experienced in the field would arrive at using normal methods in that field cannot be considered a new invention. Therefore, the first person who reads this and implements may gain the market advantage.

The reason that a medication check-off is better than merely a reminder is because people may or may not heed the reminder immediately for practical reasons, so checking off that the medication was taken is key, just in the same way a week long pill box works.

When a particular contact leads to signs of depression, an LSTM network or a GRU network can learn those correlations and make contact block recommendations, since most social networks, text messaging clients, and phone services provide contact blocking.

Commit to Learning Key Technology Without Delay

To learn about AI, there are several Q&A pages here.

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