Depression Diagnosis using Text-based AI Methods - A Systematic Review

Abstract

Recent years have seen increasing use of artificial intelligence in the domain of healthcare and mental health is no exception. This study is focused on the particular aspect of depression, which afects a significant percentage of the population and is an important concern globally. This systematic review analyzes diferent methods based on artificial intelligence to diagnose depression, highlighting the global trends of this domain like the huge share of natural language processing algorithms and neural networks on one hand, and illustrating the key issues and future lines of research in applying artificial intelligence in the domain of mental health, on the other hand.

Keywords

• eol>Machine Learning
• Pattern Recognition
• Mental Health
• Depression

1. Introduction

Artificial Intelligence (AI) is one of the branches of the computer sciences that is in charge of solving complex, nonlinear problems that usually need human interaction. AI seeks to emulate human behavior in order to automate tasks in such a way they can be solved with a similar eficiency but faster [ 1 ].

In order to be able to emulate that human behavior, AI algorithms use big sets of data, called datasets [ 2 ]. While bigger, more complete, and more heterogeneous are these datasets, these algorithms may infer better the relationship between the data so they can generate rules that before the appearance of new data they can predict how they are going to behave [ 3, 4 ].

Due to the increase in computational power and the availability of more data, AI has increased its involvement in diferent fields during the last years [ 5 ]. One of the domains where it has even more involvement is healthcare [ 6 ]. It has increased its participation in the area of mental health but at a lower rate [ 7 ]. Although the ethical aspects of its use are still in debate [ 8 ], the benefits that come from its application seem quite promising [ 6, 9 ], including the speed of diagnosis and the fact of eliminating the expert subjectivity and replacing it with a science-based objective method.

Although there exist objective and parameterized techniques for the diagnosis of mental health issues, and, in particular, for depression [ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ], the use of AI methods not only ofers the process of making the diagnosis, transforming data corresponding to symptoms into an output corresponding to a disease, but also helps to find those symptoms by transforming colloquial expressions into objective symptoms, and discover relationships between diferent types of symptoms as well.

Depression is a mental illness that is characterized by producing mood disorders over long periods of time, which can be for several weeks or more [ 21, 22 ]. It afects a significant percentage of the population [ 23 ], people of any age and it can be grouped into two large groups: major depressive disorder and persistent depressive disorder. There exist other forms that are also common but they happen with less frequency, such as postpartum depression, premenstrual dysphoric disorder, seasonal afective disorder, and psychotic disorder.

The usage of AI methods to diagnose depression using the analysis of data generated by patients have a high degree of efectiveness according to the present research.

This work intends to obtain a state of the art about the usage of AI methods to diagnose depression using text datasets. In order to define the state of the art, a Systematic Mapping Study (SMS) is made. The SMS is a standardized research process whose goal is to gather existent evidence about a particular topic [ 24 ] searching studies about it and summarizing them in order to obtain a conclusion.

2. Goals

The goal of the present study is to identify which research lines are open on the usage of AI techniques for depression diagnosis using text datasets, so eventually develop a new method that allows efectively diagnosing with the purpose of helping with the disease treatment.

An SMS is made following the process proposed by Petersen et al.[ 24 ] in order to determine the state of the art on this subject. This process establishes as the first step the definition of the questions that will lead the investigation. The research questions are the following: • RQ1: Which AI method(s) are used to solve the problem? • RQ2: What kind of learning is used to adjust the solution? • RQ3: Which results are obtained after applying each method? • RQ4: How are the results validated by each method? • RQ5: What are the future open research lines?

In order to answer each one of these questions set as research goals for the present study, a systematic review related to depression diagnosis using text-based AI methods was performed. The following sources were used: • IEEE Xplore1 • PubMed2 • Scopus3

1https://ieeexplore.ieee.org/Xplore/home.jsp 2https://pubmed.ncbi.nlm.nih.gov/ 3https://www.scopus.com/home.uri

3. Results

A search and synthesis tool has been developed4 in order to automate and standardize the search on these sources. The tool connects to each one of those sources using their public API and performs the search using the following terms: ("artificial intelligence" OR "machine learning" OR "deep learning") AND ("depression diagnos*" OR "depression detection" OR "depression estimation")

Then, a template was built and used as the foundation for the extraction formulary where each study appears along with their metadata: name, authors, published date, and link (wherever applicable). In order to perform a specific selection of the studies to be included in this review, the SMS establishes the definition of inclusion and exclusion criteria. The inclusion criteria are the following: • Magazine articles, conference articles, or book chapters. • Published year equal to or greater than 2012. • Studies must use AI to solve the problem.

• Studies must diagnose depression.

While the exclusion criteria are:

• Studies must not perform prognosis nor predictions. • Studies must not use non-text-based datasets.

• Studies must not be written in any other language than English.

The search was performed using the tool, meeting all the inclusion criteria and exclusion criteria, The initial search found a total of 192 articles and after applying the exclusion criteria, 45 articles were obtained. Then, for each article, the present work has been designed, attempting to answer (Table 1) the research questions defined above.

SDVTM,KNN, LR. SL

Cong et al. [27] NLP, NN SL

RQ2 RQ3 SL (0F.613) (P), 0.57 (R), 0.6 0.69 (P), 0.53 (R), 0.6 (F1) RQ4 SelfInformed

Experts

SelfInformed

RQ5

Extend model

Gerych et al. [28]

Deshpande and Rao [29]

Wang et al. [30]

Malviya et al. [31]

Hassan et al. DT, KNN, LR, [32] NB, SVM

Kumar et al. DT, KNN, LR, [33] NB, SVM

SL SL SL SL SL SL SL

Uddin et al. [34]

Victor et al. [35]

Chiong et al. [36]

Arun et al. [37]

Raihan et al.

[ 38 ]

NLP, NN DT, KNN, NB, NLP, RF, SL SVM AB, BP, DT, GB, LR, NLP, SL NN, RF, SVM XGB AB, NN, RF NB: 0.84 (P), 0.83 (R), 0.83 (F1) SVM: 0.8 (P), Keywords 0.79 (R), 0.8 (F1) 0.97 (ACC), 0.97 (F1), 0.99 (P), 0.95 (R) KNN: 0.79 (ACC), 0.6 (R), 0.72 (P), 0.65 (F1) LR: 0.77 (ACC), 0.5 (R), 0.39 (P), 0.44 (F1) SVM: Question0.77 (ACC), 0.5 (R), 0.39 naires (P), 0.44 (F1) NB: 0.77 (ACC), 0.5 (R), 0.39 (P), 0.44 (F1) (0KA.N8C8NC(+A)LCRDC+T)S+VNMB:+SV0M.9: ISneflof-rmed 0.86 (ACC) 0.9 (ACC) AB+BP+GB+RF: 0.98 (ACC) DT+LR+NN+SVM: 0.96 (ACC) NN, SVM

UL 0.92 (AUC), 0.91 (F1)

Questionnaires NB, SVM

NLP, NN NLP,

Experts Question- naires

Experts

Increase dataset

DT, NB, NLP 0.97 (ACC)

Govindasamy and Palanichamy [39]

Al Asad e t al. NB, [40] SVM NLP,

Tadesse et al.

[ 41 ] AB, LR, RF, NLP, NN, SL SVM

Shah et al. [42] Bhat et al. [43]

NLP, NN NLP, NN

Santana et al. GA, [44] RF, SVM KNN,

Opuku Asare et al. [45]

Hemmatirad et al. [46]

Zogan et al.

[ 47 ] DT, KNN, LR, RF, SVM, SL XGB NLP, SVM NLP, NN

Sentiment Determine Analysis depression level

Question- Include naires languages other

Study relationKeywords ship with personality

Self

Informed ASennatliymseisnt nQauieresstion- nQauieresstion- Increase dataset

Add more models

Improve valKeywords idation and increase dataset SL

SL SL SL SL SL SL 0.74 (ACC), 1 (P), 0.6 (R)

Haque et al. DT, NB, RF, [48] XGB SL

Chiong et al.

[ 49 ] AB, BP, DT, GB, LR, NLP, SL NN, RF, SVM

Narziev et al.

[ 50 ] RF, SVM nQauieresstion- Increase dataset

Experts

Increase dataset 0.79 (ACC), 0.81 (P), Question0.85 (R), 0.83 (F1) naires Tool creation

Alsagri and Ykhlef [55] DT, NB, NLP, SVM

Xezonaki et al. NLP, [56] SVM NN,

Ramiandrisoa and Mothe [57]

Burdisso et al. [58] LR, NLP, RF ES, NLP

Stankevich et al. [59] NLP, SVM

RF,

Choi et al. [60]

Khan et al. [61] GMM, LR NLP, NN

Zhang et al. LR, NLP, RF, [62] SVM Ren et al. [63] NLP, NN

Amanat et al. [64] NLP, NN SL

SL SL SL SL SL SL SL SL SSL, UL: 3.105 (ANOVA), UL 2.732 (ANOVA) DT: 0.78 (ACC), 0.59 (R), 0.62 (F1), 0.78 (P), 0.6 (AUC) NB: 0.8 (ACC), 0.81 (R), 0.72 (F1), 0.65 (P), Keywords 0.67 (AUC) SVM: 0.83 (ACC), 0.85 (R), 0.79 (F1), 0.74 (P), 0.78 (AUC)

Almars [65]

NLP, NN

Inkpen at al.

[ 66 ]

NLP, NN

AB: AdaBoost, ACC: Accuracy, ANOVA: Analysis of Variance, AUC: Area Under the Curve, BP: Bagging Predictors, DBSCAN: Density-Based Spatial Clustering of Applications with Noise, DT: Decision Trees, EL: Ensembled Learning, ES: Expert System, F1: F-Score, GA: Genetic Algorithms, GB: GradientBoost, GMM: Gaussian Mixture Model, IF: Isolation Forest, KM: K-Means, KNN: K3.1. RQ1: Which AI method or methods are used to solve the problem? Most of the studies use more than one type of algorithm, with Neural Networks (NN) (14.2%), Support Vector Machines (14.2%), and Natural Language Processing (NLP) (20.4%) having the highest share. Rao et al. [ 25 ], Cong et al. [ 27 ], Wang et al. [ 30 ], Uddin et al. [ 34 ], Shah et al. [ 42 ], Bhat et al. [ 43 ], Zogan et al. [ 47 ], Zogan et al. [ 52 ], Khan et al. [ 61 ], Ren et al. [ 63 ], Amanat et al. [ 64 ], Almars [ 65 ], Inkpen et al. [ 66 ] and Wu et al. [ 67 ] use the combination of NLP and NN algorithms on their methods. Deshpande and Rao [ 29 ], Malviya et al. [ 31 ], Victor et al. [ 35 ], Chiong et al. [ 36 ], Govindasamy and Palanichamy [ 39 ], Al Asad et al. [ 40 ], Tadesse et al. [ 41 ], Hemmatirad et al. [ 46 ], Chiong et al. [ 49 ], Islam et al. [ 51 ], Shrestha et al. [ 54 ], Xezonaki et al. [ 56 ], Ramiandrisoa and Mothe [ 57 ], Burdisso et al. [ 58 ], Stankevich et al. [ 59 ], Zhang et al. [ 62 ], Shah et al. [ 68 ] and Stankevich et al. [ 69 ] on the other hand, use NLP along with other kind of algorithms (including NN but not exclusively). Finally, Gerych et al. [ 28 ] and Raihan et al. [ 38 ] used NN combined with other kinds of algorithms but not NLP. This way, 34 of the 45 primary studies (a 75.6%) used NN or NLP algorithms.

From the rest of the studies, the most observed combinations are Decision Trees (DT), KNearest Neighbors (KNN), Logistic Regression (LR) and Support Vector Machines (SVM) in McGinnis et al. [ 26 ], Hassan et al. [ 32 ], Kumar et al. [ 33 ][ 31 ], Victor et al. [ 35 ] and Opuku Asare et al. [ 45 ]. Figure 1 shows the algorithm type distribution and Figure 2 shows the NLP and NN prevalence.

3.2. RQ2: What kind of learning is used to adjust the solution?

An important tendency towards the use of supervised learning has been observed (91.1%). Only Gerych et al. [ 28 ] and Shresta et al. [ 54 ] have chosen to investigate methods based on unsupervised learning. Zogan et al. [ 52 ] and Choi et al. [ 60 ] use hybrid methods combining supervised and unsupervised learning, and unsupervised and semi-supervised learning respectively. 3.3. RQ3: Which results are obtained after applying each method? Both the used metrics and the obtained results vary from one study to another. Rao et al. [ 25 ], McGinnis et al. [ 26 ], Deshpande and Rao [ 29 ], Malviya et al. [ 31 ], Hassan et al. [ 32 ], Kumar et al. [ 33 ], Victor et al. [ 35 ], Chiong et al. [ 36 ], Raihan et al. [ 38 ], Tadesse et al. [ 41 ], Bhat et al. [ 43 ], Santana et al. [ 44 ], Opuku Asare et al. [ 45 ], Haque et al. [ 48 ], Islam et al. [ 51 ], Shrestha et al. [ 54 ], Alsagri and Ykhlef [ 55 ], Ramiandrisoa and Mothe [ 57 ], Stankevich et al. [ 59 ], Khan et al. [ 61 ], Zhang et al. [ 62 ], Shah et al. [ 68 ], and Stankevich et al. [ 69 ] use diferent models and compare them to see which ones work better. Govindasamy and Palanichamy [ 39 ] and Xezonaki et al. [ 56 ] also illustrate multiple results, but comparing the same model with diferent datasets; and Chiong et al. [ 49 ] use several models on two diferent datasets.

The most used metrics are accuracy (25.1%), recall (22.9%), precision (22.3%), and F1 (21.3%). Figure 3 shows the most used metrics.

The accuracy rises from 0.47 to 0.98, with an average of 0.86 and a median of 0.9; recall goes from 0.33 to 0.99, with an average of 0.75 and a median of 0.79; precision goes from 0.19 to 1, with an average of 0.76 and a median of 0.83; and finally, F1 goes from 0.27 to 0.98, with an average of 0.82 and a median of 0.85.

3.4. RQ4: How are the results validated by each method?

Primarily, five diferent validation types were identified. McGinnis et al. [ 26 ], Wang et al. [ 30 ], Uddin et al. [ 34 ], Victor et al. [ 35 ], Raihan et al. [ 38 ], Haque et al. [ 48 ], Shrestha et al. [ 54 ], Xezonaki et al. [ 56 ], Zhang et al. [ 62 ], Ren et al. [ 63 ], Amanat et al. [ 64 ], Almars [ 65 ], and Shah et al. [ 68 ] use the analysis of experts to determine if a record belongs to a depression patient or not. This is the most used validation type with 30.6% of the cases.

On the other hand, the second most used validation type is the use of questionnaires. Gerych et al. [ 28 ], Hassan et al. [ 32 ], Al Asad et al. [ 40 ], Santana et al. [ 44 ], Opoku Asare et al. [ 45 ], Narziev et al. [ 50 ], Xu et al. [ 53 ], Stankevich et al. [ 59 ], Wu et al. [ 67 ] and Stankevich et al. [ 69 ] use this kind of validation (26.5% of the total).

In third place, with the 20.4% of the cases, Deshpande and Rao [ 29 ], Malviya et al. [ 31 ], Chiong et al. [ 36 ], Tadesse et al. [ 41 ], Hemmatirad et al. [ 46 ], Zogan et al. [ 47 ], Chiong et al. [ 49 ], Islam et al. [ 51 ], Zogan et al. [ 52 ] and Alsagri and Ykhlef [ 55 ] search for keywords inside the datasets to determine if a patient if depressive or not.

Also, some studies (16.3%) use datasets where the labeling is made by the participants themselves (self-informed). Rao et al. [ 25 ], Cong et al. [ 27 ], Kumar et al. [ 33 ], Shah et al. [ 42 ], Ramiandrisoa and Mothe [ 57 ], Burdisso et al. [ 58 ] and Inkpen et al. [ 66 ] use these datasets.

And, in the last instance, with 6.1% of the distribution, Govindasamy and Palanichamy [ 39 ], Bhat et al. [ 43 ] and Khan et al. [ 61 ] use sentiment analysis to label their datasets. Figure 4 shows the validation types.

3.5. RQ5: What are the future open research lines?

Among the studies that mention future work (82.2%), the most mentioned one is increasing the dataset or datasets used for the experiment. Gerych et al. [ 28 ], Wang et al. [ 30 ], Malviya et al. [ 31 ], Victor et al. [ 35 ], Raihan et al. [ 38 ], Opuku Asare et al. [ 45 ], Zogan et al. [ 47 ], Narziev et al. [ 50 ], Islam et al. [ 51 ], Zogan et al. [ 52 ], Shrestha et al. [ 54 ], Ramiandrisoa and Mothe [ 57 ], Choi et al. [ 60 ] and Almars [ 65 ] mention this possibility.

Gerych et al. [ 28 ], Burdisso et al. [ 58 ], Ren et al. [ 63 ], Amanat et al. [ 64 ] and Wu et al. [ 67 ] indicate that in the future they would be willing to extend their models to diagnose other diseases. Hassan et al. [ 32 ], Xu et al. [ 53 ], Xezonaki et al. [ 56 ], Khan et al. [ 61 ] and Wu et al. [ 67 ] propose to create a tool or a practical application of their model. Figure 5 shows all the possibilities.

4. Conclusions

This systematic review was performed to highlight the state of the art in the domain of depression diagnosis through AI using text-based methods. Analyzing the relevant literature, it is concluded that NLP and NN are the most used algorithms, used in conjunction with colloquial text-based datasets, mostly extracted from social networks. In most of the studies, the lack of suficiently big datasets was stated, illustrating the demand for larger datasets for future work. Also, the use of supervised learning preferred over using unsupervised learning has been noted, whereas, only a small section of the studies has opted for unsupervised learning. As the domain of mental health embraces AI tools for diferent purposes like diagnosis and predictions of mental health issues, aspects like the generation of significantly large databases are indispensable for better training of the algorithms. Especially in the area of depression, this also opens the possibility of studies in larger domains, providing more reliable and reusable AI models for diagnosis.

5. Acknowledgements

This work was supported and financed by the Cloudgenia group through its technical and operational initiatives.

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