Mental health treatment is becoming increasingly crucial in the lives of most individuals. However, several barriers limit access to and efficient care for people suffering from mental illnesses. These obstacles include determining when and where to seek treatment, obtaining a local therapist who takes patients, and acquiring financial means and transportation to appointments.
Now, a research team might have helped reduce all these problems. They have unveiled machine-learning algorithms to aid in diagnosing and monitoring symptom changes. The method specializes in patients suffering from a severe depressive disorder or other mental health condition. The technology has immense potential within Mental Health Technology Market and might make it easier for patients to access correct treatment.
Machine learning refers to a type of Artificial Intelligence technology in which a machine can become quite excellent at autonomously doing a task. All it needs is a lot of data and examples of appropriate behavior (i.e., what output to create when it encounters a specific input). It can also assist in identifying relevant patterns that people may not have been able to uncover as quickly without the assistance of the machine.
For the evaluation of the approach, the team collected data through several things related to participants. This included skin conductance and temperature, personal assessment of depression, activity levels, socializing, heart rate, sleep patterns. Everything was accumulated through wearable devices and smartphones.
The team aimed to create machine learning algorithms that could process this massive quantity of data and turn it into useful information. This would primarily include identifying when someone is struggling and what can help them. They anticipate that, in the future, these algorithms can provide clinicians and patients with helpful information regarding individual disease progression and treatment options.
They're considering how machine-learning algorithms might deliver their findings to consumers through a new device, a smartphone app. It could also be through a technique of contacting a selected doctor or family member about how to best help the patient.
What would be helpful is a gadget that could tell one who they are. It can suggest reasons like a person is feeling depressed as their sleep schedule has changed or their social activity data has changed. Further, it could even be because of less time spent with friends or reduced physical activity. The idea is to find a method to help increase things that a person likes and feels good in doing. Data privacy and informed consent are also top priorities for the team.
Machine-learning algorithms and artificial intelligence can help discover connections and identify patterns in vast datasets that people aren't as skilled at noticing. The present research is based on a similar outlook.
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