Model can more naturally detect depression in conversations

a post by Rob Matheson for the Big Think blog

Neural network learns speech patterns that predict depression in clinical interviews

To diagnose depression, clinicians interview patients, asking specific questions — about, say, past mental illnesses, lifestyle, and mood — and identify the condition based on the patient’s responses.

In recent years, machine learning has been championed as a useful aid for diagnostics. Machine-learning models, for instance, have been developed that can detect words and intonations of speech that may indicate depression. But these models tend to predict that a person is depressed or not, based on the person’s specific answers to specific questions. These methods are accurate, but their reliance on the type of question being asked limits how and where they can be used.

In a paper being presented at the Interspeech conference, MIT researchers detail a neural-network model that can be unleashed on raw text and audio data from interviews to discover speech patterns indicative of depression. Given a new subject, it can accurately predict if the individual is depressed, without needing any other information about the questions and answers.

The researchers hope this method can be used to develop tools to detect signs of depression in natural conversation. In the future, the model could, for instance, power mobile apps that monitor a user’s text and voice for mental distress and send alerts. This could be especially useful for those who can’t get to a clinician for an initial diagnosis, due to distance, cost, or a lack of awareness that something may be wrong.

“The first hints we have that a person is happy, excited, sad, or has some serious cognitive condition, such as depression, is through their speech,” says first author Tuka Alhanai, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “If you want to deploy [depression-detection] models in scalable way … you want to minimize the amount of constraints you have on the data you’re using. You want to deploy it in any regular conversation and have the model pick up, from the natural interaction, the state of the individual.”

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I found this particularly interesting since I have recently had two phone assessments and the second one was to query the answers that I had put on the standard questionnaire. That one that asks how many days in the last two weeks have you ... ?
I find that particularly difficult. Did I feel miserable all day just once, did I feel miserable and unable to cope for part of every day etc?

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Art therapy improves mood, and reduces pain and anxiety when offered at bedside during acute hospital treatment

an article by Tamara A. Shella (Arts and Medicine Institute, Cleveland, OH, USA) published in The Arts in Psychotherapy Volume 57 (February 2018)

Highlights

• A chart review, of the impact of art therapy at the bedside, with patients (N = 195) admitted for acute care at a large, urban, teaching hospital.
• Analysis of results demonstrated significant improvements in pain, mood, and anxiety levels within all patients regardless of gender, age, or diagnosis.
• Art therapy may be a safe and cost effective intervention as an adjunct to traditional medical management.

Abstract

Art therapists can engage medical inpatients in the creation of art to encourage emotional and physical healing. Utilizing a chart review, the impact of art therapy sessions at the bedside with patients (N = 195) in a large urban teaching hospital was reviewed.

The sample was predominantly female (n = 166) as more women than men agreed to participate in an art therapy session. As a routine part of regular clinical practice patients were asked to rate their perception of mood, anxiety, and pain using a 5-point faces scale before and after an art therapy session conducted by a registered art therapist.

Multiple diagnoses were included in this chart review, making this study more representative of the variety of medical issues leading to hospitalization. Analysis of pre and post results demonstrated significant improvements in pain, mood, and anxiety levels of art therapy sessions for all patients regardless of gender, age, or diagnosis (all p < 0.001).

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Cycling, car, or public transit: a study of stress and mood upon arrival at work

an article by Stéphane Brutus, Roshan Javadian and Alexandra Joelle Panaccio, (Concordia University, Montreal, Canada) published in International Journal of Workplace Health Management Volume 10 Issue 1 (2017)

Abstract

Purpose
The purpose of this paper is to investigate the impact of various commuting modes on stress and mood upon arrival at work.

Design/methodology/approach
Data on stress and mood were collected after 123 employees arrived at work by bike, car, or public transit. In order to account for the natural fluctuation of stress and mood throughout the day, the assessment of the dependent variables was made within the first 45 minutes of arrival at work.

Findings
As hypothesized, those who cycled to work were less stressed than their counterparts who arrived by car. However, there was no difference in mood among the different mode users.

Practical implications
A lower level of early stress among cyclists offers further evidence for the promotion of active commute modes.

Originality/value
This study underscores the importance of being sensitive to time-based variations in stress and mood levels when investigating the impact of commute modes.

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