Machine Learning for Mental Healthcare Treatments

How Machine Learning could help design more customized Mental Healthcare Treatments

Mental illness is a kind of health condition that affects a person’s mind, emotions, or behavior (or all three). It also has an impact on an individual’s physical health. Mental health issues like depression, schizophrenia, attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), etc., are highly prevalent today. Exerts peg the number of people worldwide who suffer from mental illnesses at 450 million. It is not just adults, children and adolescents under 18 years also are at the risk of mental health disorders. Mental health illnesses are also considered the leading cause of prevalent public health problems. For example, depression is the leading cause of disability and can increase the risk for suicidal ideation and suicide attempts.

Machine Learning and AI in today’s world are transforming the healthcare industry in different ways, and neuroscientists and clinicians across the globe are finding ways to leverage machine learning in the treatment of mental problems. The first step of such a process is identifying the early onset of mental disorders through AI. Unlike the diagnosis of other chronic conditions based on laboratory tests and measurements, mental illnesses are typically diagnosed using an individual’s self-report to specific questionnaires designed to detect specific patterns of feelings or social interactions.

Due to the increasing availability of data on individuals’ mental health status, artificial intelligence (AI) and machine learning (ML) technologies are now being applied. They could help improve our understanding of mental health conditions and have been engaged to assist mental health providers for improved clinical decision-making.

AI as a means of personalized care, increased access, and support

Mental health innovation has reached heights in several ways through the genetic revolution, which helps us comprehend some keys to understanding mental health conditions. On the other hand, neuroimaging has helped us understand the brain’s working. All these processes generate humongous structured and unstructured data which could be used for making personalized care provisions.

AI systems could help providers navigate these data resources and collect clinically actionable targets to improve patient care. By doing that, providers may offer more personalized and preventive care in a more targeted way.

AI-driven chatbots can interact with an individual in real-time. They are available 24/7, at no cost, and they reduce stigma regarding seeking professional help. Whether they are used as stand-alone treatment agents or adjuncts to more traditional counseling, chatbots provide added therapeutic content. During our online research, we came across Woebot, which is based on cognitive behavioral therapy principles. It has an empathy component tailored to the messages that the individual sends. It is designed to target cravings and urges and help individuals build self-awareness regarding their patterns of thinking, mood-related thinking, anxiety, depression, and the urge and craving to use.

Machine Learning and Mental Health: How does it go hand in hand?

Healthcare data scientists worldwide are leveraging machine learning models to develop treatment care plans for patients to detect the onset of any disorder that may set in. In addition, this could effectively be used to calculate the risks of an individual being diagnosed with any disorder.

What makes machine learning so helpful today is that understanding diagnosis was based on group averages and statistics over populations in the past. However, machine learning allows clinicians to personalize through patterned data.

Two main ways in which machine learning is changing the front of mental health treatment are:

1. Identifying Biomarkers and Treatment Plans Development

Treatment of mental health issues follows a repetitive trial and error process before deciding on the consolidated and right action plans. Of course, a medical diagnosis should not follow such repetitive rounds of trials and errors. However, since the sources of mental issues are varied across each patient, including symptoms and the disorders, it is only natural that such a process will follow suit in mental health disorder diagnosis.

The human body has physical biomarkers monitored to see if the body is functioning normally. Similarly, behavioral biomarkers to identify conditions like depression and anxiety.

Machine learning algorithms could help us determine key behavioral biomarkers to aid mental health professionals in deciding if a patient is at the risk of developing a mental health disorder. Additionally, the algorithms may assist in tracking the effectiveness of a treatment plan.

It comes down to every patient’s biology, triggers, and reactions to stress and conditions like depression. The problem is that many of the symptoms of mental health disorders overlap. Although some critical indicators for mental health disorders are known, a trial and error treatment plan is hardly acceptable. Machine learning algorithms provide psychiatrists and mental health professionals a means to identify even the sub-types of various disorders and develop more specific treatment plans and medication dosages.

2. Predicting Crises

In the case of mental health, certain conditions are more prone to situational crises like panic attacks, maniac states, psychosis, etc. Also, certain conditions such as bi-polarity and Schizophrenia have higher risks of situational crises.

Machine learning algorithms can ingest a combination of self-provided data, and passive data from smartphones and social media to determine if an episode is on the cards. There are a few indicators that predict an episode, through a pattern of stress, isolation, or exposure to triggers.

Challenges

ML and AL are relatively new in treatments related to mental health, and it is only reasonable that we ensure it does good and not harms!

  • One main challenge in AI-based mental health applications is patient engagement which becomes a critical factor in the success of any such applications. It is challenging to make sure people return regularly to interact in such applications.
  • Additionally, developers and researchers need to make sure they equip these tools with appropriate protections for high-risk patients. Patterns determining such high-risk patients are often random, making such deductions spurious.
  • Data used to train AI models is crucial to their clinical utility. Data quality could be particularly challenging when it comes to mental healthcare.
  • It is also required to have diverse samples to train these models because using only one region, one clinic, or one population could create skews in the model. These tools must be built from the ground up with a very diverse approach — one has to work with the patients and consider input from clinicians, data scientists, and regulators.

Do you like our blogs on AI-ML and Healthcare? Here are a few more suggested reads.

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Ideas2IT

We’re a product engineering firm. Our work is cutting-edge, be it in AI-ML, Cloud, DevOps, or IIoT, for an enviable set of clients. Visit www.ideas2it.com.