Artificial Intelligence Space Search Reduction in Mental Health
Advances in technology regarding Artificial Intelligence came along with human and business benefits. Some of the benefits are the reduction of search space applied with Natural Language Processing (NLP), a common Artificial Intelligence technique. This has been applied to multiple fields, yet in mental health in Human Resources departments it is still a novelty.
To start, we must understand the meaning of search space reduction, how and why we use search space reduction in NLP, and what are the benefits for companies.
What is the meaning of search space?
Search space definition refers to the personalized algorithms that set all the potential solutions to a given problem. The main goal is to optimize and provide randomly selected viable solutions to achieve a feasible space in order to increase overall efficiency.
In addition, this technique can be used in multiple kinds of engineering, such as software, civil, and chemistry, as well as in different areas of artificial intelligence, such as data mining, fuzzy logic and NLP.
How to use search space reduction with NLP?
Within the former technique, NLP is oriented to companies whose objective is to collect data for analyzing employees’ human language in order to understand their mental health state. To obtain this data, companies use text-based corporate tools, where employees express themselves.
Hirschberg and Manning have mentioned in the past that this method of high performance allows better language understanding, thanks to the ability of data to perform speech analysis by identifying syntax, semantic information and context.
In addition, this way of getting information is bias-free, as NLP doesn’t turn to a specific question, but analyzes the language as a whole.
Why use space search reduction with NLP?
There are a variety of motives for which to use search space reduction with NLP; from efficiency improvements to time and financial saving costs. NLP allows direct language analysis without depending on an individual’s question answer in order to find out about their state. In other words, NLP is based on the conversation held by individuals through corporate communication tools, for which is bias-free. However, Human Resources is still managing surveys and questionnaires that require correction and scoring that indicate who are the employees that present a risk of suffering mental health problems. Nevertheless, even if this questionnaire based methodology has worked for small companies, in medium to big companies hasn’t been successful, since it is highly complicated to detect through this kind of surveys and questionnaires which employees are outliers.
So, this problem can be solucionated by applying artificial intelligence technology, specifically NLP, given that it can learn, interpret and manipulate human language, thus targeting those employees who are at risk of suffering from a mental health problem.
What are the benefits for companies?
Reducing search space to know which employees have a risk of suffering a mental health problem such as burnout, anxiety, or depression, by only applying corporate communication tools allows companies to save time in distributing questionnaires.
Providing this information to companies allows them to help employees adopt preventive measures, such as psychological therapy. This provides an additional benefit which is that the employee stays in the company, and therefore the company saves on turnover cost.
Conclusion
To conclude, having artificial intelligence technology that is NLP based in companies reduces search space for detecting mental health problems in employees, allows save time in survey administration and saves costs in turnover.
