Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study

Patel, Rashmi; Lloyd, Theodore; Jackson, Roy; Ball, Michael; Shetty, Hitesh; Broadbent, Matthew; Geddes, John; Stewart, Robert; McGuire, Philip; Taylor, Matthew

doi:10.1016/j.eurpsy.2016.01.551

Cited by 4 publications

(2 citation statements)

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“…Sentiment analysis can be used to determine the underlying emotional tone and identify individuals at risk of depression using large-scale data sets. Numerous studies have applied sentiment analysis and text classification in the area of mental health [28][29][30] to detect effectively depressive feelings and depression [31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Jamali,

Berger,

Spiteri

2023

JMIR AI

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Background Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and society. Early detection of momentary depressive feelings is highly beneficial in reducing this burden and improving the quality of life for affected individuals. To this end, the abundance of data exemplified by X (formerly Twitter) presents an invaluable resource for discerning insights into individuals’ mental states and enabling timely detection of these transitory depressive feelings. Objective The objective of this study was to automate the detection of momentary depressive feelings in posts using contextual language approaches. Methods First, we identified terms expressing momentary depressive feelings and depression, scaled their relevance to depression, and constructed a lexicon. Then, we scraped posts using this lexicon and labeled them manually. Finally, we assessed the performance of the Bidirectional Encoder Representations From Transformers (BERT), A Lite BERT (ALBERT), Robustly Optimized BERT Approach (RoBERTa), Distilled BERT (DistilBERT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and machine learning (ML) algorithms in detecting momentary depressive feelings in posts. Results This study demonstrates a notable distinction in performance between binary classification, aimed at identifying posts conveying depressive sentiments and multilabel classification, designed to categorize such posts across multiple emotional nuances. Specifically, binary classification emerges as the more adept approach in this context, outperforming multilabel classification. This outcome stems from several critical factors that underscore the nuanced nature of depressive expressions within social media. Our results show that when using binary classification, BERT and DistilBERT (pretrained transfer learning algorithms) may outperform traditional ML algorithms. Particularly, DistilBERT achieved the best performance in terms of area under the curve (96.71%), accuracy (97.4%), sensitivity (97.57%), specificity (97.22%), precision (97.30%), and F1-score (97.44%). DistilBERT obtained an area under the curve nearly 12% points higher than that of the best-performing traditional ML algorithm, convolutional neural network. This study showed that transfer learning algorithms are highly effective in extracting knowledge from posts, detecting momentary depressive feelings, and highlighting their superiority in contextual analysis. Conclusions Our findings suggest that contextual language approaches—particularly those rooted in transfer learning—are reliable approaches to automate the early detection of momentary depressive feelings and can be used to develop social media monitoring tools for identifying individuals who may be at risk of depression. The implications are far-reaching because these approaches stand poised to inform the creation of social media monitoring tools and are pivotal for identifying individuals susceptible to depression. By intervening proactively, these tools possess the potential to slow the progression of depressive feelings, effectively mitigating the societal load of depression and fostering improved mental health. In addition to highlighting the capabilities of automated sentiment analysis, this study illuminates its pivotal role in advancing global public health.

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Section: Introductionmentioning

confidence: 99%

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Jamali,

Berger,

Spiteri

2023

JMIR AI

View full text Add to dashboard Cite

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“…Digital health promises to improve the way healthcare is delivered by providers, through the use of information and communications technologies to monitor and improve the health and wellbeing of patients [10]. Recent advances, including those in Artificial Intelligence (AI) and Natural Language Processing (NLP), make it possible to bring together clinical notes from different professionals and avail meaningful insights from the Electronic Health Record (EHR) [11,12]. This work assessed the feasibility of applying NLP and AI in the management of depression to recognise patterns of patient behaviour that require intervention, thus providing a foundation for aiding the healthcare professional with indicators of important features from past care.…”

Section: Introductionmentioning

confidence: 99%

Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression

Msosa,

Grauslys,

Zhou

et al. 2023

IEEE J. Biomed. Health Inform.

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Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good

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Natural Language Processing for Computer-Assisted Chart Review to Assess Documentation of Substance use and Psychopathology in Heart Failure Patients Awaiting Cardiac Resynchronization Therapy

Yusufov¹,

Pirl²,

Braun³

et al. 2022

Journal of Pain and Symptom Management

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Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study

Cited by 4 publications

References 0 publications

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression

Natural Language Processing for Computer-Assisted Chart Review to Assess Documentation of Substance use and Psychopathology in Heart Failure Patients Awaiting Cardiac Resynchronization Therapy

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