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.

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.

Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable information regarding ADEs and hence are an important source for detecting ADE signals. However, EHR texts tend to be noisy. Yet applying off-the-shelf tools for EHR text preprocessing jeopardizes the subsequent ADE detection performance, which depends on a well tokenized text input. OBJECTIVE: In this paper, we report our experience with the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0), which aims to promote deep innovations on this subject. In particular, we have developed rule-based sentence and word tokenization techniques to deal with the noise in the EHR text. METHODS: We propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. We experiment with different word embedding methods commonly used in word-level classification tasks and demonstrate the impact of an integrated usage of both domain-specific and general-purpose pre-trained word embedding for detecting ADEs from EHRs. RESULTS: Our system was ranked first for the named entity recognition task in the MADE1.0 challenge, with a micro-averaged F1-score of 0.8290 (official score). CONCLUSION: Our results indicate that the integration of two widely used sequence labeling techniques that complement each other along with dual-level embedding (character level and word level) to represent words in the input layer results in a deep learning architecture that achieves excellent information extraction accuracy for EHR notes.

A Prospective Controlled Trial of an Electronic Hand Hygiene Reminder System.

Background. The use of electronic hand hygiene reminder systems has been proposed as an approach to improve hand hygiene compliance among healthcare workers, although information on efficacy is limited. We prospectively assessed whether hand hygiene activities among healthcare workers could be increased using an electronic hand hygiene monitoring and reminder system. Methods. A prospective controlled clinical trial was conducted in 2 medical intensive care units (ICUs) at an academic medical center with comparable patient populations, healthcare staff, and physical layout. Hand hygiene activity was monitored concurrently in both ICUs, and the reminder system was installed in the test ICU. The reminder system was tested during 3 administered phases including: room entry/exit chimes, display of real-time hand hygiene activity, and a combination of the 2. Results. In the test ICU, the mean number of hand hygiene events increased from 1538 per day at baseline to 1911 per day (24% increase) with the use of a combination of room entry/exit chimes, real-time displays of hand hygiene activity, and manager reports (P < .001); in addition, the ratio of hand hygiene to room entry/exit events also increased from 26.1% to 36.6% (40% increase, P < .001). The performance returned to baseline (1473 hand hygiene events per day) during the follow-up phase. There was no significant change in hand hygiene activity in the control ICU during the course of the trial. Conclusions. In an ICU setting, an electronic hand hygiene reminder system that provided real-time feedback on overall unit-wide hand hygiene performance significantly increased hand hygiene activity.

Value and relation display: interactive visual exploration of large data sets with hundreds of dimensions.

Few existing visualization systems can handle large data sets with hundreds of dimensions, since high-dimensional data sets cause clutter on the display and large response time in interactive exploration. In this paper, we present a significantly improved multidimensional visualization approach named Value and Relation (VaR) display that allows users to effectively and efficiently explore large data sets with several hundred dimensions. In the VaR display, data values and dimension relationships are explicitly visualized in the same display by using dimension glyphs to explicitly represent values in dimensions and glyph layout to explicitly convey dimension relationships. In particular, pixel-oriented techniques and density-based scatterplots are used to create dimension glyphs to convey values. Multidimensional scaling, Jigsaw map hierarchy visualization techniques, and an animation metaphor named Rainfall are used to convey relationships among dimensions. A rich set of interaction tools has been provided to allow users to interactively detect patterns of interest in the VaR display. A prototype of the VaR display has been fully implemented. The case studies presented in this paper show how the prototype supports interactive exploration of data sets of several hundred dimensions. A user study evaluating the prototype is also reported in this paper.

Measuring data abstraction quality in multiresolution visualizations.

Data abstraction techniques are widely used in multiresolution visualization systems to reduce visual clutter and facilitate analysis from overview to detail. However, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the abstractions. In this paper, we define two data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the Histogram Difference Measure and the Nearest Neighbor Measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the acceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level. Also, analysts can compare different abstraction methods using the measures to see how well relative data density and outliers are maintained, and then select an abstraction method that meets the requirement of their analytic tasks.