Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms

1 INTRODUCTION . Nowadays , deep learning techniques have been frequently used in sentiment analysis ( Tan et al. , 2013 ; Mukhtar & Khan , 2018 ; Liao et al. , 2021 ) , particularly in movie recommendations and product reviews and ratings . In the healthcare domain , sentiment analysis with deep learning provides many benefits such as utilizing medical information to achieve the best result to increase healthcare quality ( Abirami & Askarunisa , 2017 ; Bi et al. , 2020 ) . We focus our work on applications related to mental health such as using deep learning to make a diagnostic prediction . There is a pressing need to find a convenient and automated method to assess depression severity . Worldwide , more than 300 million people are suffering depression ( Organization et al. , 2017 ) . Apart from the high prevalence of Major Depressive Disorder , overall 85 % of depressed individuals are underdiagnosed ( Falagas et al. , 2007 ) . Research has shown that about 30 % of patients suffering from an episode of major depression do not seek treatment , with only 10 % of them being adequately treated ( Falagas et al. , 2007 ) . We validate our approach based on its application to the diagnosis of MDD . We demonstrate that our approach can result in more accurate predictions of the severity of depression , which can enhance automated health care decision making . Medical research ( Tsakalidis et al. , 2018 ) shows that text sentiment analysis methods can be effective in making inferences about people ’ s mental states , such as MDD . For example , depressed people use more first person pronouns than non-depressed people because they are more focused on themselves and less connected with others . These may not be reflected in a person ’ s visual appearance , i.e. , visual behaviors . Visual data alone will therefore be insufficient for capturing depressive symptoms that are only manifested in a person ’ s verbal utterances . Mental disorders are difficult to detect . In practice , clinicians first measure the severity of depressive symptoms in patients and then identify depression in them . During in-person clinical interviews , clinicians widely use a structured Patient Health Questionnaire ( PHQ ) — a clinically validated tool determining the severity of depressive symptoms across several personal dimensions ( Kroenke & Spitzer , 2002 ; Arseniev-Koehler et al. , 2018 ) . Inspired by the observation of the provided data set of such interviews , we formulated the hypothesis that the context of words in a transcript can be used to generate PHQ scores ( Kroenke et al. , 2009 ) . This PHQ metric for depressive disorder forms a context constructed with eight items , such as “ sleep problem ” , “ anxiety problem ” , “ fatigue problem ” , “ depression problem ” , and “ no motivation or interest in things ” ( Arseniev-Koehler et al. , 2018 ) . We observed that each text ( see figure 1 ) covers information relating to at least one of these eight topics . This motivates use of a deep learning model that represents these eight PHQ topics , thereby introducing an inductive bias into the learning process . Thus the contextual information about these eight PHQ topics should be encoded to determine depression states . We motivate generating graph-level representations for transcripts ( as inputs ) to encode contextual information from these transcripts ( we assume a transcript encodes facts representing depressive symptoms ) . To achieve this , we propose a novel form for node attributes within a GNN based model that captures node-specific embeddings for every word in the vocabulary . The representations of each word is shared globally and can be updated according to associations among words in a transcript . We summarize the representations of all the words in the transcript to predict depression states . Intuitively , the generated node-level embeddings maintain records linking certain existing facts that indicate the known symptoms of a subject . When subsequent facts are discovered which indicate additional depressive symptoms , records will be updated by aggregating both old and new facts . In other words , those records will be updated via a message passing mechanism over the transcript context until ideally all major depressive features ( characterizing depressive symptoms ) have been discovered . In this way , we can represent the most relevant contextual information which is universal across all transcripts – possibly involving the context of eight PHQ topics we discussed above . Using graph structures to capture context-level features is an innovative pathway which we hypothesise can be used to measure different levels of depression . In our experiments , our approach realized a good generalization on a limited , incomplete , and unevenly distributed dataset ( see figure 3a ) . 1.1 RELATED WORK . Research in measuring the severity of depressive symptoms aims to train a regression model to predict depression scores ( Valstar et al. , 2016 ; Alhanai et al. , 2018 ; Tsakalidis et al. , 2018 ) . Some prior work ( Ringeval et al. , 2017 ; Alhanai et al. , 2018 ; Haque et al. , 2018 ) applies a sequencelevel deep learning model to capture implicit depressive signals . Such models , in general , use a multi-modal sentence embedding , rather than a mapping of a whole interview , to predict a PHQ score . Other prior work ( Valstar et al. , 2016 ; Cummins et al. , 2017 ) implements statistical functions ( e.g. , max , min ) on short-term features over an entire interview , but this may fail to preserve useful temporal information across an entire interview , such as some short-term signs of regret , anxiety , etc . Current machine learning methods ( Valstar et al. , 2016 ; Alhanai et al. , 2018 ; Song et al. , 2018 ; Du et al. , 2019 ) concentrate on learning features from multi-modalities –– as determinants for estimating the severity of depressive disorders . The method ( Lin et al. , 2020 ; Solieman & Pustozerov , 2021 ) applies a multi-modal fusion network that summarizes all depressive features from both text and audio . The work ( Lam et al. , 2019 ) leverages a 1D CNN model to extract text-level embeddings from the text . The methods ( Ray et al. , 2019 ; Mallol-Ragolta et al. , 2019 ; Zhang et al. , 2020 ; Solieman & Pustozerov , 2021 ) utilize deep learning models with pretrained global word embeddings to extract contextual features from text . They only apply their work to the depression detection task . In principle , we should be able to improve performance further using all available modalities , however feature-fusion method may lead to learn a wrong representation encoding inter-subject variability unrelated to depression ( Williamson et al. , 2016 ) . The feature-fusion approach to learning from small data may cause poor generalization : with a small sample size , the number of features should also be small to avoid the problems of dimensionality and overfitting . However , given that the dimensions of audio and video features are very large , sparse parametrization needs to be taken into account when training machine learning models for learning a joint multi-modal feature vector ( Williamson et al. , 2016 ) . 2 METHOD . We generate a node-level embedding matrix for each word of a transcript . These node attributes represent an underlying relationship between the current word and every other word in the transcript . In this way , a graph neural network ( GNN ) schematizes underlying associations among words via their internal representations . We propose to aggregate their internal representations to encode information from the context of a transcript generalizing all major depressive symptoms1 . Building a Text Graph : For a given transcript , we build a text graph . We regard all unique words appearing in a transcript as the nodes of the graph . Each edge starts from a word in the transcript and ends with a word within a fixed window either side of the word . Let G = ( V , E ) denote a text level graph . V is a set of nodes representing all the unique words in a given text , and E is a set of undirected edges between pairs of these nodes , each represented by a set of the two nodes at either end of the edge . We build edges by using a fixed size sliding window in a transcript to collect co-occurrence statistics . Each node has an attribute which is a matrix . To emphasise this representational structure of the attribute matrix , we refer to it as a ‘ schema ’ ( Dozois & Beck , 2008 ; Hammen & Zupan , 1984 ; Rudolph et al. , 1997 ; Soygüt & Savaşir , 2001 ) Ui ∈ Rn×d . The jth row of Ui is a vector of length d containing the representation that node vi has of vj ; and n denotes the total number of unique words ( the vocabulary size ) in a corpus . 2.1 SCHEMAS . We generate a schema for each word node which performs a role of recording a global context . This global context retains information from interactions between the current word and every other word . In this way , each word node maintains “ a dynamic record ” ( in the form of a schema ) of the context from the given transcript . The schemas are progressively updated by a GNN model . This resulting model produces final embeddings of the words in a transcript in relation to all words in the vocabulary , including the other words that make up the current training transcript . Schemas preserve structures that represent relationships between the identifier word and every other word . Thus we can exploit these schemas to capture context-level features in an explicit way by learning a GNN model . Our innovation is to represent word proximity through the graph structure and co-occurrence within the same transcript within the schema at each node . 2.2 SCHEMA-BASED GRAPH NEURAL NETWORK ( SGNN ) . We use multiple passes ( layers ) of the message passing mechanism ( MPM ) ( Gilmer et al. , 2017 ; Xu et al. , 2018 ) to update the schema at each node of the text graph . 1our code links to : https : //github.com/Clio-dl/Using-SGNN-for-Depression-Estimate . git/ Initialization : We first initialise an n × d1 matrix ( in our experiments , we set d1 = 32 ) as the schema U ( 1 ) i at each node vi ∈ V . The schema is all zeros apart from the row corresponding to the word associated with this node , which is a random d-dimensional vector using a linear transformation . Message passing layer : In this work , the operation of message passing is split into two steps to update the schema at each node . We first modify the schema at each node vi : Ûi ( k ) = U ( k ) i W ( k ) 1 + 1 n 11TU ( k ) i W ( k ) 2 + 1 n 1i1TU ( k ) i W ( k ) 3 ( 1 ) where 1 ∈ Rn is a vector of ones . The first term updates each row independently . The second term operates on the sum of the columns , replicated in each row . The third term operates on the row corresponding to the current word , replicated in each row . These terms are a subset of equivariant linear functions which are computed by ( Maron et al. , 2018 ) . Second we compute the message function , which is defined as : M ( k ) ( Ûi ( k ) , Ûj ( k ) ) = Ûj ( k ) +RELU ( [ Ûi ( k ) ||Ûj ( k ) ] W ( k ) 4 ) W ( k ) 5 ( 2 ) where || denotes concatenation along the second axis . ( W ( k ) m ) 1≤m≤5 are learnable parameters . This is essentially a two-layer neural network . In the next step , each node ’ s schema is updated as : Ui ( k+1 ) = ∑ j∈N ( i ) M ( k ) ( Ûi ( k ) , Ûj ( k ) ) ∈ Rn×dk+1 ( 3 ) We apply equation ( 3 ) as the sum aggregator over the k-th layer of the SGNN . After all K message-passing layers have been applied , the schemas are pooled to get representations of all nodes in the graph . We apply an elementwise max-pooling operater to extract node features . We use the READOUT ( Ying et al. , 2018 ; Xu et al. , 2018 ) function which aggregates node features by summing them together : hG = ∑ vi∈V U ( K ) i ( 4 ) We use hG to predict a PHQ score for each transcript . In our experiments , we apply a 2-layer multi-layer perceptron ( MLP ) .

Summary: This paper proposes a schema-based GNN method to measure the severity of depression. To gain a global representation of each word, the proposed method constructs word nodes and uses schema structure to capture the context-level information. The main contribution of this paper is the introduction of the schema encoder. The experimental results show the superiority of schema GNN over other SOTA models.