Techniques for Suicidal Ideation Prediction: a Qualitative Systematic Review

Beriwal, Muskaan; Agrawal, Sarvesh

doi:10.1109/inista52262.2021.9548444

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…The social stigma related to having suicidal ideations has a particularly significant effect. The fear of social stigma has been shown to discourage individuals at risk of suicide from discussing their experiences in person and seeking support [ 22 , 24 , 25 , 26 ]. Further, it obstructs the extant suicide-risk screening methods, such as questionnaires and interviews, since they require patients to explicitly disclose their intentions to commit suicide [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile, ref. [ 22 ] reviewed eight studies to highlight the feature extraction methods and classifier algorithms used for suicidal ideation prediction. The authors summarized the available datasets constructed from Twitter, Reddit, Vkontakte (Saint Petersburg, Russia), and Tumblr (New York, NY, USA) data, as well as datasets consisting of interview and questionnaire responses.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

Yeskuatov

Chua

Foo

2022

IJERPH

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Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critical for suicide prevention. However, there are challenges associated with conventional suicide-risk screening methods. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between 2018 and 2022. The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

Yeskuatov

Chua

Foo

2022

IJERPH

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“…As there is no one-model-fits-all solution, we used a diverse set of ML algorithms, including those that are widely used in the medical informatics field, as shown in previous systematic reviews: decision tree (DT) [77], RF, support vector machine (SVM) [77][78][79], and Logit [78,79] algorithms. Moreover, we used other ML algorithms to increase the diversity of the models: Gaussian Naive Bayes, K-nearest neighbor (KNN), support vector classifier (SVC), AdaBoost, extra tree, multilayer perceptron (MLP) [71], light gradient boosting machine (LGBM) [80], CatBoost [81], and gradient boost (GB) [82].…”

Section: Development and Validation Of The Modelsmentioning

confidence: 99%

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

Ahmed¹,

Ahmed²

2023

JMIR Form Res

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Background Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial. Additionally, due to the requirement of running systems in the background for prolonged periods, existing systems can be resource inefficient. As a result, these systems can be infeasible in low-resource settings. Objective Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Another objective was to explain the machine learning (ML) models that were best for identifying depression. Methods We developed a fast tool that retrieves the past 7 days’ app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data and responses to the Patient Health Questionnaire-9. To identify depressed and nondepressed students, we developed a diverse set of ML models: linear, tree-based, and neural network–based models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches: filter, wrapper, and embedded methods. We developed and validated the models using the nested cross-validation method. Additionally, we explained the best ML models through the Shapley additive explanations (SHAP) method. Results Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). Feature importance analysis suggested app usage behavioral markers containing diurnal usage patterns as being more important than aggregated data-based markers. In addition, a SHAP analysis of our best models presented behavioral markers that were related to depression. For instance, students who were not depressed spent more time on education apps on weekdays, whereas those who were depressed used a higher number of photo and video apps and also had a higher deviation in using photo and video apps over the morning, afternoon, evening, and night time periods of the weekend. Conclusions Due to our system’s fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed and take steps for intervention.

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“…As there is no one-model-fit-for-all solution, we used a diverse set of ML algorithms including the algorithms which are widely used in the medical informatics area as shown in the systematic reviews: Decision Trees [34], RF, Support Vector Machine (SVM) [34,35,36], Logistic Regression (Logit) [35,36] algorithms. In addition, we used other classification ML algorithms to increase the diversity of the models: Gaussian Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), AdaBoost, Extra Tree, Multilayer Perceptron (MLP) [79], Light Gradient Boosting Machine (GBM) [77], CatBoost [80], Gradient Boost (GB) [81].…”

Section: Development and Validation Of The Modelsmentioning

confidence: 99%

A Minimal and Faster System to Identify Depression Through Smartphone: An Explainable Machine Learning-Based Approach

Ahmed¹,

Ahmed²

2023

Preprint

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Background: The robust pervasive device-based existing systems to detect depression developed in recent years requiring data collected over a long period of time may not be effective in cases where early detection is crucial. In addition, due to the requirement of running systems in the background for prolonged periods, the existing promising systems may not be resource efficient. As a result, these systems can be infeasible in low-resource settings.Objective: Our main objective was to develop a minimal system to identify depression that works on data retrieved in the fastest possible time. Another objective of this study was to explain the machine learning (ML) models which performed best in identifying depression.Methods: We developed a faster tool that retrieves the past 7 days’ app usage data in a second (mean=0.31 second, SD=1.10 second) . In our study, 100 students from Bangladesh participated and our tool collected their app usage data and responses to the Patient Health Questionnaire-9 (PHQ-9) scale. To identify the depressed and non-depressed participants, we developed a diverse set of ML models including linear, tree-based, and neural network-based models. We selected the important features by the Stable approach along with the 3 main types of feature selection (FS) approaches: Filter, Wrapper, and Embedded. We developed and validated the models using nested cross validation method. Additionally, we explained the best ML models through the SHapley Additive exPlanations (SHAP) method.Results: Leveraging only the app usage data retrieved in a second, our Light GBM model using the Stable approach selected features identified 82.4% depressed correctly (precision=75%, F1 score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious Stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and had a maximum precision of 77.4% (balanced accuracy=77.95%). Feature importance analysis presents app usage behavioral markers containing the diurnal usage patterns as more important compared to the aggregated data-based markers. Apart from these, SHAP analysis on our best models presented the behavioral markers that have a relation with depression. For instance, the non-depressed students’ spending time on Education apps was higher on weekdays while depressed students used a higher number of Photo & Video apps and also had a higher deviation in using Photo & Video apps over the day of the weekend.Conclusions: Due to the faster and minimalistic nature, our system may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of findings can facilitate the development of resource insensitive systems, in better understanding the depressed students and taking steps in intervention.

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Techniques for Suicidal Ideation Prediction: a Qualitative Systematic Review

Cited by 10 publications

References 13 publications

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

A Minimal and Faster System to Identify Depression Through Smartphone: An Explainable Machine Learning-Based Approach

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