Can Social Platforms Detect Mental Illness Using Deep Learning Models?

Imagine this: it’s a rainy day, and you’ve not been out for months due to lockdown. You’ve been feeling lethargic, and generally not in the mood to do anything but complain for the past few months. You write a post on Facebook to your friends venting about life once more. Suddenly, a Facebook notification pops up on your phone, recommending you to seek medical advice with a nearby therapist who specializes in Depression and Cognitive Behavioural Therapy, offering a free consultation.

This world could be one that we live in rather soon, where every social media post you upload is pre-screened to check in on your mental health: scientifically proven models and algorithms that can predict whether you are at risk of suffering from a mental illness. Is this a world that you think is possible? Would this be a world you’d like to live in?

The importance of mental health cannot be understated. Suicide is the second leading cause of death among teenagers in the United States, according to the Centers for Disease Control and Prevention (CDC). A separate CDC study also found that teen suicide jumped 56% from 2007 to 2017- this rise coincides with the launch and growth in adoption of many social media platforms that we know and love today.

As modern-day vulnerability and transparency become more common, users of social media platforms share more feelings or emotional states through their posts. These millions of posts are being used for capitalist purposes such as online advertising, but they could equally be used for helping the health of its users.

The Research

A recent study conducted by Kim, Lee, Park and Han (Kim et al., 2020), researchers from Sungkyunkwan University in Korea and Carnegie Mellon University, demonstrated a deep learning model that can identify a person’s mental state based on their posted information. This research extends previous studies by Gkotsis et al. (2014) that used Deep Learning models to recognize mental illness-related posts for classification purposes automatically.

By analyzing and learning posting information written by users, the proposed model by Kim et al. (2020) could accurately identify whether a user’s post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. The model provides a foundation for detecting whether a user, based on their post, is at risk of suffering from a mental disorder.

The research prompts us to ask fundamental questions to our social platforms of choice:

  • If it is possible to create a pervasive Deep Learning Model to help identify potential sufferers with mental illness?
  • Should social media platforms monitor mental health states of its users?
  • What responsibility do social media platforms have if their data suggests that a user is at severe risk of mental illness?
  • What kinds of intervention would be useful?

Before attempting to answer these, let’s take a closer look at the research.

Image for post

But first, what is Deep Learning?

There are various posts already that cover the basics of Deep Learning. If you want a short primer on it, I recommend reading this excellent medium post by Radu Raicea. I learned a lot from it, and I’m sure those that gave the 40K+ claps also agree!

In short, Deep Learning is a machine learning method that allows us to train an Artificial Intelligence (AI) to predict outputs, given a set of inputs.

More specifically, the Deep Learning method uses a Neural Network to imitate animal intelligence by creating a neural network of data and processing actions. There are three layers of neurons for neural network processing: Input Layer, Hidden Layer(s), Output Layer.

Inputs within a Deep Learning method can be either:

  • Supervised — giving the model inputs and telling it the expected output. Radu uses the example of a weather-predicting AI. It learns to predict the weather using historical data, where the model has training data inputs (pressure, humidity, wind speed) and outputs (temperature).
  • Unsupervised— using data sets with no specified structure and letting the AI make logical classifications of the data. Radu uses the example of a behaviour-predicting AI for an e-commerce website. While it won’t learn by using a labelled data set of inputs and outputs, it would create its classification of the input data and tell you which kind of users are most likely to buy different products.

Deep Learning combines the best of both worlds by training the AI, based on both Supervised and _Unsupervised _data. However, enormous datasets and computational power are needed for the models to come up with meaningful results and predictions because of the multiple hidden layers of calculations required in between.

#mental-health #data-science #social-media #deep-learning #machine-learning #deep learning

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Buddha Community

Can Social Platforms Detect Mental Illness Using Deep Learning Models?

Can Social Platforms Detect Mental Illness Using Deep Learning Models?

Imagine this: it’s a rainy day, and you’ve not been out for months due to lockdown. You’ve been feeling lethargic, and generally not in the mood to do anything but complain for the past few months. You write a post on Facebook to your friends venting about life once more. Suddenly, a Facebook notification pops up on your phone, recommending you to seek medical advice with a nearby therapist who specializes in Depression and Cognitive Behavioural Therapy, offering a free consultation.

This world could be one that we live in rather soon, where every social media post you upload is pre-screened to check in on your mental health: scientifically proven models and algorithms that can predict whether you are at risk of suffering from a mental illness. Is this a world that you think is possible? Would this be a world you’d like to live in?

The importance of mental health cannot be understated. Suicide is the second leading cause of death among teenagers in the United States, according to the Centers for Disease Control and Prevention (CDC). A separate CDC study also found that teen suicide jumped 56% from 2007 to 2017- this rise coincides with the launch and growth in adoption of many social media platforms that we know and love today.

As modern-day vulnerability and transparency become more common, users of social media platforms share more feelings or emotional states through their posts. These millions of posts are being used for capitalist purposes such as online advertising, but they could equally be used for helping the health of its users.

The Research

A recent study conducted by Kim, Lee, Park and Han (Kim et al., 2020), researchers from Sungkyunkwan University in Korea and Carnegie Mellon University, demonstrated a deep learning model that can identify a person’s mental state based on their posted information. This research extends previous studies by Gkotsis et al. (2014) that used Deep Learning models to recognize mental illness-related posts for classification purposes automatically.

By analyzing and learning posting information written by users, the proposed model by Kim et al. (2020) could accurately identify whether a user’s post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. The model provides a foundation for detecting whether a user, based on their post, is at risk of suffering from a mental disorder.

The research prompts us to ask fundamental questions to our social platforms of choice:

  • If it is possible to create a pervasive Deep Learning Model to help identify potential sufferers with mental illness?
  • Should social media platforms monitor mental health states of its users?
  • What responsibility do social media platforms have if their data suggests that a user is at severe risk of mental illness?
  • What kinds of intervention would be useful?

Before attempting to answer these, let’s take a closer look at the research.

Image for post

But first, what is Deep Learning?

There are various posts already that cover the basics of Deep Learning. If you want a short primer on it, I recommend reading this excellent medium post by Radu Raicea. I learned a lot from it, and I’m sure those that gave the 40K+ claps also agree!

In short, Deep Learning is a machine learning method that allows us to train an Artificial Intelligence (AI) to predict outputs, given a set of inputs.

More specifically, the Deep Learning method uses a Neural Network to imitate animal intelligence by creating a neural network of data and processing actions. There are three layers of neurons for neural network processing: Input Layer, Hidden Layer(s), Output Layer.

Inputs within a Deep Learning method can be either:

  • Supervised — giving the model inputs and telling it the expected output. Radu uses the example of a weather-predicting AI. It learns to predict the weather using historical data, where the model has training data inputs (pressure, humidity, wind speed) and outputs (temperature).
  • Unsupervised— using data sets with no specified structure and letting the AI make logical classifications of the data. Radu uses the example of a behaviour-predicting AI for an e-commerce website. While it won’t learn by using a labelled data set of inputs and outputs, it would create its classification of the input data and tell you which kind of users are most likely to buy different products.

Deep Learning combines the best of both worlds by training the AI, based on both Supervised and _Unsupervised _data. However, enormous datasets and computational power are needed for the models to come up with meaningful results and predictions because of the multiple hidden layers of calculations required in between.

#mental-health #data-science #social-media #deep-learning #machine-learning #deep learning

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

Michael  Hamill

Michael Hamill

1617331277

Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Noah  Rowe

Noah Rowe

1593502200

Object Detection for Robots using Deep Learning

In this post, we will enable a robot named Vector to detect and recognize a large number of objects. In the end, you will see how he mentions the objects that he detected.

Who is Vector?

Vector is a cute robot, who can be your companion, and is powered by AI. He is curious, independent and also he can make you laugh with his actions. After all, you can customize it with using AI, and we will see how to make this robot detect and recognize various objects in our day to day life. If you want to know about Vector briefly, then please go through this short video.

Vector SDK

The Vector SDK gives access to various capabilities of this robot, such as computer vision, Artificial intelligence and navigation. You can design your programs to make this robot imbibed with certain AI capabilities. Before running the module, install the vector SDK by following the information on this page: https://developer.anki.com/vector/docs/index.html.

Objects detected by Vector

Object Detection using Deep Learning

To detect objects, we will be using an object detection algorithm which is trained with [Google Open Image dataset]. The network consists of a ResNet with a Region proposal network and can detect more than 600 object categories. That means **Vector **will be able to identify a large number of objects. However, we have a few more dependencies to make Vector recognize those objects. The main dependencies are based on my testing platform using python 3.6, but you can change them according to the machine in which you will be implementing.

  1. Tensorflow — 1.12.0 (you can install both CPU or GPU version)
  2. Keras-2.2.4
  3. OpenCV3

Here is a video of Vector detecting objects.

Running the Module

  1. Please clone or download this repository into your local machine. After downloading, you need to authenticate the vector robot so that the SDK can interact with Vector. To authenticate with the robot, type the following into the Terminal window.
  • python3 -m anki_vector.configure

Please note that the robot and your computer should be connected to the same network. Now, you will be asked to enter your robot’s name, IP address and serial number, which you can find in the robot itself. Also, You will be asked for your Anki login and password which you used to set up your Vector.

  1. IF you see “SUCCESS!” then your robot is connected to your computer, and you can run this module by typing.

Note: Before running this module please download the pre-trained model from here,  and put it inside the data folder.

  • python vector_objectDetection.py

You will now see the following output, where Vector is searching for objects.

Vector grabbed this picture of me posing, and he says:

I can detect Car, Computer monitor, Human face, Computer monitor, Wheel.

The picture was taken by Vector to detect objects

Now let us go through the coding part step by step

The code below recieves the picture taken by Vector and calls the object_detection module to detect and identify various objects. Once detected, the object names are send back to vector so that he can speak out.
def get_classnames(image_path):
    """
    This function calls the object detection library to detect 600 objects
    :param image_path:
    :return: class labels
    """
    try:
        classes = object_detection(image_path)
        if len(classes) == 0:
            return 'no objects'
        class_list = []
        for class_names in classes:
           class_list.append(class_names)

        print('Labels: {}'.format(classes))
        return ', '.join(class_list)

    except Exception as e:
          print('Exception Handled', e)

#object-detection #artificial-intelligence #deep-learning #robotics #machine-learning #deep learning