Skip to main content

Diagnosing Depression in Casual Conversation? Neural Networks Lead The Way

Asiket Singh Dhillon



Neural Networking and pattern recognition are a rising force in technological sector. Once we can teach machines to ‘learn’, the avenues that machines can be used in are ever expanding. Improved neural networks could possibly yield higher efficiency rates in automatic screening of individuals who may have cognitive and behavioural abnormalities.
Earlier this year Tulka Anhanai, Mohammed Ghassemi and James Glass came together to present their paper titled Detecting Depression with Audio/Text Sequence Modeling of Interviews. It puts forward a model of detecting depression in individuals which rather than relying on traditional iterations of uses, what they call “context-free” detection. Traditional models may analyse a set of answers to predetermined questions. For example, “How often do feel low/depressed?” / “Does your family have a history of any mental diagnoses?”. The model knows what a depressed person would say and compares the individual’s answers to see where they fit.
Al Hanai’s model analyses the content as well as the manner of expression of answers. Therefore, it is not constrained to the specific conversation but can utilise patterns recognised in other individuals that may exhibit similar content and exhibition to reach its assessment. Depressed individuals may speak slower and with lower voice modulation in comparison to healthy individuals. The model recognises certain patterns which emerge and then presents its diagnosis. This is known as sequence modelling which allows it to not be constrained to a case-by-case analysis and use recognisable patterns and apply those to a variety of contexts. Sequencing allows for the analysis to not be restricted to pointed questions and direct answers but rather for entire conversations and detect the severity of depression as well.
Out of a total of 142 subjects that the system analysed it labelled 28 individuals as those with depression. In terms of precision the system scored 71%. Out of the 142 some were used as training for the model and others for development which left 47 (25%) of the subjects which the model had to assess. While this is far from ideal, it is a learning step. The implications of such a model means that it would be possible to not rely only on self-reports and manual analysis which may be hindered by the assessor’s biases. The system will be able to analyse casual conversation and provide assistance. Furthermore, individuals that cannot physically be present at the practitioner’s office will also fall under the purview of the system. Clinicians are then, no longer restricted to asking specific questions and direct answers within specific context.
What the researchers found upon poring over the results was that the system requires over 4 times more audio interactions (30) to identify patterns as compared to text (7).  I believe that this may be due to the fact that audio would require far more categorisations than text. Such as modulation, tone, pauses between words and tempo. The textual analysis is fairly constrained to the content of the text as well as arrangement. While the parameters that the system works on are understood, “How” the system reaches its conclusions as well as what patterns it recognises are hard to discern. This is due to the nature of neural networks and their functioning, i.e the individual nodes and their function in the larger network is largely unknown. So we know the input which is the audio and transcripts and the output which is the model’s assessment.  Figuring out how it reached that conclusion is the next step forward.
The researchers hope that this model paves the way for different, specialised versions which can diagnose other impairments such as Dementia. The way forward for the model is a symbiotic relation between the clinician and the model. Improving the accuracy of the model as well as increase the understanding that we have of the inner workings of the neural networks.

Matheson, Rob. (2018, August 29). Model can more naturally detect depression in conversations. MIT News. Retrieved from http://news.mit.edu/2018/neural-network-model-detect-depression-conversations-0830
Al Hanai, T., Ghassemi, M., Glass, J. (2018) Detecting Depression with Audio/Text Sequence Modeling of Interviews. Proc. Interspeech 2018, 1716-1720, DOI: 10.21437/Interspeech.2018-2522.

Labels:

Comments

Post a Comment

Popular posts from this blog

Hardin's trauma

“After”, is a 2019 teen romantic drama directed by jenny gage that revolves around the love of  Tessa, an inexperienced teenage girl, with Hardin, a mysterious ‘bad boy’ . Hardin, the main male character, never had a secure relationship with his father. When Hardin was young, his father used to be an alcoholic with a lot of debt. When he was just eight years old, intruders broke into his home looking for his father for money, however, there was only Hardin and his mother. The intruders forced themselves on Hardin’s mother, and Hardin, who was sleeping then, came downstairs to see what was wrong. To Hardin’s shock, his mother was being raped by three men, one by one. Hardin’s mother told him to leave, however, one man forced him to watch everything.  I would assume that Hardin has PTSD as a result of this incident, and in this paper I will try to prove it. Symptoms of PTSD and scenes that prove Hardin had it: The person subsequently re-experiences the event through both intrusi...
Post a Comment
Read more

Analysing “Anniyan”: Dissociative Identity Disorder meets Personality Disorders

Pranaya Prakash In the movie “Anniyan” (Shankar, 2005), the protagonist Ramanujam Iyengar, also known as Ambi, is the host of his alters: Remo and Anniyan. While the focus of the movie is only on Dissociative Identity Disorder (DID), also known as Multiple Personality Disorder (MPD), the host, Ambi, and the alter, Anniyan, show symptoms of Obsessive-Compulsive Personality Disorder (OCPD) and Antisocial Personality Disorder (ASPD), respectively. In this blog post, I attempt to critically analyse the portrayal of DID and the possibility of the protagonist having comorbid Personality Disorders. While it is highly unlikely for individuals with DID to have comorbidities with Personality Disorders ( Antisocial Personality Disorder ), especially with OCPD and ASPD (Fink, 1991), it is interesting to think of the possibility and analyse the developmental trajectory of these individuals.  The movie starts with the character development of Ambi, a lawyer who is meticulous and very particular ...
Post a Comment
Read more

The Psychological Depth of Good Will Hunting

Shorya Sehgal Good Will Hunting revolves around the interaction of two rebellious characters, Sean Maguire and Will Hunting, who, to a certain extent, help each other to conform slightly. Will is a brilliant but troubled, young adult. He suffers from post traumatic stress disorder (PTSD), inferiority complex, defence mechanisms and attachment disorder. A mathematical genius, Will had a complete disrespect for authority and for his own considerable talents. As the story progressed, I was able to see Will’s personal growth as he developed an extremely strong relationship with his therapist, Sean. Sean had fought his own battles in life which had made him tough. This significantly helped Will and Sean to connect to each other on a deep, emotional level. What struck me the most in the movie is how coherently Will's journey is tied together. His transition from an inexpressive, troubled young guy to a mature and responsible one is shown very exquisitely. By the end, he was able to be...
Post a Comment
Read more