Predicting early psychiatric readmission with natural language processing of narrative discharge summaries

The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a cohort of individuals admitted to a psychiatric inpatient unit between 1994 and 2012 with a principal diagnosis of major depressive disorder, and extracted inpatient psychiatric discharge narrative notes. Using these data, we trained a 75-topic Latent Dirichlet Allocation (LDA) model, a form of natural language processing, which identifies groups of words associated with topics discussed in a document collection. The cohort was randomly split to derive a training (70%) and testing (30%) data set, and we trained separate support vector machine models for baseline clinical features alone, baseline features plus common individual words and the above plus topics identified from the 75-topic LDA model. Of 4687 patients with inpatient discharge summaries, 470 were readmitted within 30 days. The 75-topic LDA model included topics linked to psychiatric symptoms (suicide, severe depression, anxiety, trauma, eating/weight and panic) and major depressive disorder comorbidities (infection, postpartum, brain tumor, diarrhea and pulmonary disease). By including LDA topics, prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved from baseline (area under the curve 0.618) to baseline+1000 words (0.682) to baseline+75 topics (0.784). Inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort. Topic modeling and related approaches offer the potential to improve prediction using EHRs, if generalizability can be established in other clinical cohorts

Making sense of violence risk predictions using clinical notes

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in psychiatric patients using such notes, with acceptable performance. However, they do not explain why classification works and how it can be improved. We explore two methods to better understand the quality of a classifier in the context of clinical note analysis: random forests using topic models, and choice of evaluation metric. These methods allow us to understand both our data and our methodology more profoundly, setting up the groundwork for improved models that build upon this understanding. This is particularly important when it comes to the generalizability of evaluated classifiers to new data, a trustworthiness problem that is of great interest due to the increased availability of new data in electronic format

Abstract

In this paper, we discuss some aspects of selectional behavior of dot objects, and present an algorithm for clustering selector contexts for dot nominals according to the selected type. The clustering algorithm is based on the notion of contextualized similarity between selector contexts and defines a similarity measure for contextual equivalents of the target nominal.

SemEval-2010 Task 7: Argument Selection and Coercion

We describe the Argument Selection and Coercion task for the SemEval-2010 evaluation exercise. This task involves characterizing the type of compositional operation that exists between a predicate and the arguments it selects. Specifically, the goal is to identify whether the type that a verb selects is satisfied directly by the argument, or whether the argument must change type to satisfy the verb typing. We discuss the problem in detail, describe the data preparation for the task, and analyze the results of the submissions

Medstract: Creating Large-scale

The automatic extraction of information from Medline articles and abstracts (commonly referred to now as the biobibliome) promises to play an increasingly critical role in aiding research while speeding up the discovery process. We have been developing robust natural language tools for the automated extraction of structured information from biomedical texts as part of a project we call Medstract