Machine Learning, Predictions, Psychological Multimodal Therapies

[Frédéric]

I did not know in which thread to put this "UFO" info, so I created one. I hope I gave it a good title. If I understood correctly, we use artificial intelligence to predict whether this or that treatment (rather psychologically oriented) will be effective on this or that person. Honestly, I rarely express a prejudice, but here I wonder how useful this study is, perhaps to save money if we know that such treatment will be ineffective on such person. What are your thoughts?

Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables

Abstract
Background
Chronic tinnitus is a complex condition that can be associated with considerable distress. Whilst cognitive-behavioral treatment (CBT) approaches have been shown to be effective, not all patients benefit from psychological or psychologically anchored multimodal therapies. Determinants of tinnitus-related distress thus provide valuable information about tinnitus characterization and therapy planning.

Objective
The study aimed to develop machine learning models that use variables (or "features") obtained before treatment to characterize patients' tinnitus-related distress status after treatment. Whilst initially all available variables were considered for model training, the final model was required to achieve highest predictive performance using only a small number of features.

Methods
1,416 tinnitus patients (decompensated tinnitus: 32%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, CBT, physiotherapy and informational counseling were included in the analysis. At baseline, patients were assessed using 205 features from 10 questionnaires comprising sociodemographic and clinical information. A data-driven workflow was developed consisting of (a) an initial exploratory correlation analysis, (b) supervised machine learning to predict tinnitus-related distress after treatment (T1) using baseline data only (T0), and (c) post-hoc analysis of the best model to facilitate model inspection and understanding. Classification methods were embedded in a feature elimination wrapper that iteratively learned on features found to be important for the model in the preceding iteration, in order to keep the performance stable while successively reducing the model complexity. 10-fold cross-validation with area under the curve (AUC) as performance measure was implemented for model generalization error estimation.

Results
The best machine learning classifier (gradient boosted trees) can predict tinnitus-related distress in T1 with AUC = 0.890 using 26 features. Subjectively perceived tinnitus-related impairment, depressivity, sleep problems, physical health-related impairments in quality of life, time spent to complete questionnaires and educational level exhibited a high attribution towards model prediction.

Conclusions
Machine learning can reliably identify baseline features recorded prior to treatment commencement that characterize tinnitus-related distress after treatment. The identification of key features can contribute to an improved understanding of multifactorial contributors to tinnitus-related distress and thereon based multimodal treatment strategies.

Full article: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228037

 

[Frédéric]

Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics

Abstract:
Tinnitus is a complex condition that is associated with major psychological and economic impairments – partly through various comorbidities such as depression. Understanding the interaction between tinnitus and depression may thus improve either symptom cluster's prevention, diagnosis and treatment. In this study, we developed and validated a machine learning model to predict depression severity after outpatient therapy (T1) based on variables obtained before therapy (T0). 1,490 patients with chronic tinnitus (comorbid major depressive disorder: 52.2%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, cognitive behavioural therapy, physiotherapy and informational counselling were included. 185 variables were extracted from self-report questionnaires and socio-demographic data acquired at T0. We used 11 classification methods to train models that reliably separate between subclinical and clinical depression at T1 as measured by the general depression questionnaire. To ensure highly predictive and robust classifiers, we tuned algorithm hyperparameters in a 10-fold cross-validation scheme. To reduce model complexity and improve interpretability, we wrapped model training around an incremental feature selection mechanism that retained features that contributed to model prediction. We identified a LASSO model that included all 185 features to yield highest predictive performance (AUC = 0.87 ± 0.04). Through our feature selection wrapper, we identified a LASSO model with good trade-off between predictive performance and interpretability that used only 6 features (AUC = 0.85 ± 0.05). Thus, predictive machine learning models can lead to a better understanding of depression in tinnitus patients, and contribute to the selection of suitable therapeutic strategies and concise and valid questionnaire design for patients with chronic tinnitus with or without comorbid major depressive disorder.

Full article: https://www.nature.com/articles/s41598-020-61593-z

 

[Frédéric]

Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus

Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEG-based tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%–94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.

 

[Pete88]

Sorry, so what's the take away from this? Thanks!

 

[Frédéric]

@Pete88: to get a biomarker of tinnitus + to predict if a particular treatment will benefit a particular tinnitus patient (before spending money to apply it to the tinnitus patient).

 

[Pete88]

@Pete88: to get a biomarker of tinnitus + to predict if a particular treatment will benefit a particular tinnitus patient (before spending money to apply it to the tinnitus patient).

Ahh ok, cool. Thank you very much for the explanation!