Mobile Communication Log Time Series to Detect Depressive Symptoms.

Major Depressive Disorder (MDD) is highly prevalent and characterized by often debilitating behavioral and cognitive symptoms. MDD is poorly understood, likely due to considerable heterogeneity and self-report-driven symptomatology. While researchers have been exploring the ability of machine learning to screen for MDD, much less attention has been paid to individual symptoms. We posit that understanding the relationship between objective data streams and individual depression symptoms is important for understanding the considerable heterogeneity in MDD. Thus, we conduct a comprehensive comparative study to explore the ability of machine learning to predict nine self-reported depressive symptoms with call and text logs. We created time series from the logs of over 300 participants by aggregating communication attributes- average length, count, or contacts- every 4, 6, 12, or 24 hours. We were most successful predicting movement irregularities with a balanced accuracy of 0.70. Further, we predicted suicidal ideation with a balanced accuracy of 0.67. Outgoing texts proved to be the most useful log type. This study provides valuable insights for future mobile health research aimed at personalizing assessment and intervention for MDD.

Automated Construction of Lexicons to Improve Depression Screening With Text Messages.

Given that depression is one of the most prevalent mental illnesses, developing effective and unobtrusive diagnosis tools is of great importance. Recent work that screens for depression with text messages leverage models relying on lexical category features. Given the colloquial nature of text messages, the performance of these models may be limited by formal lexicons. We thus propose a strategy to automatically construct alternative lexicons that contain more relevant and colloquial terms. Specifically, we generate 36 lexicons from fiction, forum, and news corpuses. These lexicons are then used to extract lexical category features from the text messages. We utilize machine learning models to compare the depression screening capabilities of these lexical category features. Out of our 36 constructed lexicons, 14 achieved statistically significantly higher average F1 scores over the pre-existing formal lexicon and basic bag-of-words approach. In comparison to the pre-existing lexicon, our best performing lexicon increased the average F1 scores by 10%. We thus confirm our hypothesis that less formal lexicons can improve the performance of classification models that screen for depression with text messages. By providing our automatically constructed lexicons, we aid future machine learning research that leverages less formal text.

Ensembles of BERT for Depression Classification.

Depression is among the most prevalent mental health disorders with increasing prevalence worldwide. While early detection is critical for the prognosis of depression treatment, detecting depression is challenging. Previous deep learning research has thus begun to detect depression with the transcripts of clinical interview questions. Since approaches using Bidirectional Encoder Representations from Transformers (BERT) have demonstrated particular promise, we hypothesize that ensembles of BERT variants will improve depression detection. Thus, in this research, we compare the depression classification abilities of three BERT variants and four ensembles of BERT variants on the transcripts of responses to 12 clinical interview questions. Specifically, we implement the ensembles with different ensemble strategies, number of model components, and architectural layer combinations. Our results demonstrate that ensembles increase mean F1 scores and robustness across clinical interview data. Clinical relevance- This research highlights the potential of ensembles to detect depression with text which is important to guide future development of healthcare application ecosystems.

Depression Screening from Text Message Reply Latency.

Depression is both debilitating and prevalent. While treatable, it is often undiagnosed. Passive depression screening is crucial, but leveraging data from Smartphones and social media has privacy concerns. Inspired by the known relationship between depression and slower information processing speed, we hypothesize the latency of texting replies will contain useful information in screening for depression. Specifically, we extract nine reply latency related features from crowd-sourced text message conversation meta-data. By considering text metadata instead of content, we mitigate the privacy concerns. To predict binary screening survey scores, we explore a variety of machine learning methods built on principal components of the latency features. Our findings demonstrate that an XGBoost model built with one principal component achieves an F1 score of 0.67, AUC of 0.72, and Accuracy of 0.69. Thus, we confirm that reply latency of texting has promise as a modality for depression screening.

Anomalous Antimicrobial Susceptibility Trend Identification.

Antibiotic resistant bacterial infections are a growing global health crisis. Antibiograms, aggregate antimicrobial resistance reports, are critical for tracking antibiotic susceptibility and prescribing antibiotics. This research leverages fifteen years of the expansive Massachusetts statewide antibiogram dataset curated by the Massachusetts Department of Public Health. Given the lengthy annual antibiogram creation process, data are not timely. Our prior research involved forecasting the current antimicrobial susceptibility given historic antibiograms. The objective for this research is to expand upon this prior work by identifying which antibiotic-bacteria combinations have resistance trends that are not well forecasted. For that, our proposed Previous Year Anomalous Trend Identification (PYATI) strategy employs a cluster driven outlier detection solution to identify the trends to remove before forecasting. Employing PYATI to remove antibiotic-bacteria combinations with anomalous trends statistically significantly reduces the forecasting error for the remaining combinations. As antibiotic resistance is furthered by prescribing ineffective antibiotics, PYATI can be leveraged to improve antibiotic prescribing.

Screening For Depression With Retrospectively Harvested Private Versus Public Text.

Depression is the leading cause of disability, often undiagnosed, and one of the most treatable mood disorders. As such, unobtrusively diagnosing depression is important. Many studies are starting to utilize machine learning for depression sensing from social media and Smartphone data to replace the survey instruments currently employed to screen for depression. In this study, we compare the ability of a privately versus a publicly available modality to screen for depression. Specifically, we leverage between two weeks and a year of text messages and tweets to predict scores from the Patient Health Questionnaire-9, a prevalent depression screening instrument. This is the first study to leverage the retrospectively-harvested crowd-sourced texts and tweets within the combined Moodable and EMU datasets. Our approach involves comprehensive feature engineering, feature selection, and machine learning. Our 245 features encompass word category frequencies, part of speech tag frequencies, sentiment, and volume. The best model is Logistic Regression built on the top ten features from two weeks of text data. This model achieves an average F1 score of 0.806, AUC of 0.832, and recall of 0.925. We discuss the implications of the selected features, temporal quantity of data, and modality.