Identification of maternal depression risk from natural language collected in a mobile health app

Krishnamurti, Tamar; Allen, Kristen; Hayani, Laila; Rodriguez, Samantha N.; Davis, Alexander

doi:10.1016/j.procs.2022.09.092

Cited by 8 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, some studies focus on detecting depressive content early using NLP on user posts on social media platforms, which is an important research area [20,21]. For instance, Krishnamurti et al [22] utilized NLP techniques and features from other patient-reported data as indicators of depression risk to detect depressive symptoms during pregnancy. Bhandarkar et al [23] developed an NLP-based artificial intelligence model to predict suicide-related events within 30 days based on textual messages from patients.…”

Section: Nlpmentioning

confidence: 99%

Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media

Wu,

Jeng,

Chen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In the transition from television to mobile devices, short videos have emerged as the primary content format, possessing tremendous potential in various fields such as marketing, promotion, education, advertising, and so on. However, from the available literature, there is a lack of studies investigating the elements necessary for the success of short videos, specifically regarding what factors need to be considered during production to increase viewership. Therefore, this study proposed the NLP-QFD model, integrating Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), and Quality Function Deployment (QFD) methods. Real short videos from mainstream Western media (CNN) and regional media (Middle East Eye) will be employed as case studies. In addition to analyzing the content of short videos and audiences’ reviews, we will utilize the NLP-QFD model to identify the key success factors (KSFs) of short videos, providing guidance for future short video creators, especially for small-scale businesses, to produce successful short videos and expand their influence through social media. The results indicate that the success factors for short videos include the movie title, promotion, reviews, and social media. For large enterprises, endorsements by famous individuals are crucial, while music and shooting are key elements for the success of short videos for small businesses.

show abstract

Section: Nlpmentioning

confidence: 99%

Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media

Wu,

Jeng,

Chen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“… Kimball et al (2019) proved that LIWC is a sensitive tool in screening for anxiety and depression in tinnitus patients, even when the self-assessment fails to indicate relevant levels of anxiety and depression symptoms. Furthermore, a longitudinal study in which LIWC assessment combined with other Coefficient for Naturalistic Language processing (NLP) tools such as SentiWordNet, LDA Topic and Word2Vec, indicated that language processing and analysis moderately predicts depression risk onset among pregnant persons, 30 days and 60 days after giving birth, respectively ( Krishnamurti et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

Trifu,

Nemeș,

Herta

et al. 2024

Front. Psychol.

View full text Add to dashboard Cite

IntroductionThe identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts.AimThis study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count).Materials and methodsA sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them.Assessment tools(1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 – Linguistic Inquiry and Word Count – a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use.ResultsDepressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form – not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money.ConclusionDepressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.

show abstract

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data

Krishnamurti,

Rodriguez,

Wilder

et al. 2024

Arch Womens Ment Health

View full text Add to dashboard Cite

Purpose To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression. Methods A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction). Results Among participants, 78% identified as white with an average age of 30 [IQR 26–34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74–0.89, sensitivity 0.35–0.81, specificity 0.78–0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables). Conclusion A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.

show abstract

Identification of maternal depression risk from natural language collected in a mobile health app

Cited by 8 publications

References 21 publications

Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media

Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data

Contact Info

Product

Resources

About