Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service

Liu, Zhaolu; Peach, Robert L.; Lawrance, Emma; Noble, Ariele; Ungless, Mark A.; Barahona, Mauricio

doi:10.3389/fdgth.2021.779091

Cited by 9 publications

(15 citation statements)

References 53 publications

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“…Table 6 reports the LRAP value used to compare the agreement between the MUQUBIA probability estimates with the National Institute on Aging and Alzheimer’s Association protocol 54 for neuropathological assessment of 9 patients in our test group. The LRAP metric is classically used in multilabel ranking problems 55 . It determines the percentage of higher-ranked labels that resemble the true labels for each of the given samples.…”

Section: Resultsmentioning

confidence: 99%

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

De Francesco,

Crema,

Archetti

et al. 2023

Sci Rep

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Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

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Section: Resultsmentioning

confidence: 99%

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

De Francesco,

Crema,

Archetti

et al. 2023

Sci Rep

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“…For example, changes in linguistic patterns or the sentiment of texts can be indicative of signs of mental health disorders [ 46 ]. NLP enables AI systems to analyze, process, and understand texts and speech similarly to humans [ 55 ]. NLP allows AI systems to respond in human language [ 56 ].…”

Section: Reviewmentioning

confidence: 99%

“…From there, chatbots can offer appropriate support or intervention [ 57 ]. NLP can be applied directly to individual patient data to predict suicide risk and identify disorders and comorbidities for example Boamente Program uses user's text data via smart phone application to predict suicide ideation [ 55 ]. NLP can also be used in health records to automate chart reviews, classify patients, and predict patient-specific outcomes or overall population trends [ 55 ].…”

Section: Reviewmentioning

confidence: 99%

“…NLP can be applied directly to individual patient data to predict suicide risk and identify disorders and comorbidities for example Boamente Program uses user's text data via smart phone application to predict suicide ideation [ 55 ]. NLP can also be used in health records to automate chart reviews, classify patients, and predict patient-specific outcomes or overall population trends [ 55 ]. NLP-powered chatbots are AI-driven systems that engage in individual conversations and can offer human-like, empathetic responses and guidance.…”

Section: Reviewmentioning

confidence: 99%

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The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review

Zafar,

Fakhare Alam,

Vivas

et al. 2024

Cureus

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This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.

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“…They trained and tested three supervised ML algorithms to classify each thread as containing a hypoglycemia incident (positive) or not (negative). Liu et al [ 14 ] trained and tested a deep learning approach—Longformer-masked language models using Hugging Face—to classify conversation stages of messages and behaviors present in messages from both texters and volunteers. Stenner et al [ 13 ] developed PASTE (Patient-Centered Automated SMS Tagging Engine), a rule-based NLP system for encoding medication-related messages from MyMediHealth, which is a medication management system for scheduling and administering medications and sending reminders to patient cell phones.…”

Section: Introductionmentioning

confidence: 99%

Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study

Davoudi¹,

Lee²,

Luong³

et al. 2022

J Med Internet Res

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Background Free-text communication between patients and providers plays an increasing role in chronic disease management, through platforms varying from traditional health care portals to novel mobile messaging apps. These text data are rich resources for clinical purposes, but their sheer volume render them difficult to manage. Even automated approaches, such as natural language processing, require labor-intensive manual classification for developing training data sets. Automated approaches to organizing free-text data are necessary to facilitate use of free-text communication for clinical care. Objective The aim of this study was to apply unsupervised learning approaches to (1) understand the types of topics discussed and (2) learn medication-related intents from messages sent between patients and providers through a bidirectional text messaging system for managing participant blood pressure (BP). Methods This study was a secondary analysis of deidentified messages from a remote, mobile, text-based employee hypertension management program at an academic institution. We trained a latent Dirichlet allocation (LDA) model for each message type (ie, inbound patient messages and outbound provider messages) and identified the distribution of major topics and significant topics (probability >.20) across message types. Next, we annotated all medication-related messages with a single medication intent. Then, we trained a second medication-specific LDA (medLDA) model to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n=1-3 words) using spaCy, clinical named entities using Stanza, and medication categories using MedEx; we then applied chi-square feature selection to learn the most informative features associated with each medication intent. Results In total, 253 participants and 5 providers engaged in the program, generating 12,131 total messages: 46.90% (n=5689) patient messages and 53.10% (n=6442) provider messages. Most patient messages corresponded to BP reporting, BP encouragement, and appointment scheduling; most provider messages corresponded to BP reporting, medication adherence, and confirmatory statements. Most patient and provider messages contained 1 topic and few contained more than 3 topics identified using LDA. In total, 534 medication messages were annotated with a single medication intent. Of these, 282 (52.8%) were patient medication messages: most referred to the medication request intent (n=134, 47.5%). Most of the 252 (47.2%) provider medication messages referred to the medication question intent (n=173, 68.7%). Although the medLDA model could identify a majority intent within each topic, it could not distinguish medication intents with low prevalence within patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class. Conclusions LDA can be an effective method for generating subgroups of messages with similar term usage and facilitating the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated, deep, medication intent classification. International Registered Report Identifier (IRRID) RR2-10.1101/2021.12.23.21268061

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Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service

Cited by 9 publications

References 53 publications

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review

Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study

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