This paper assesses topic coherence and human topic ranking of uncovered latent topics from scientific publications when utilizing the topic model latent Dirichlet allocation (LDA) on abstract and full-text data. The coherence of a topic, used as a proxy for topic quality, is based on the distributional hypothesis that states that words with similar meaning tend to co-occur within a similar context. Although LDA has gained much attention from machine-learning researchers, most notably with its adaptations and extensions, little is known about the effects of different types of textual data on generated topics. Our research is the first to explore these practical effects and shows that document frequency, document word length, and vocabulary size have mixed practical effects on topic coherence and human topic ranking of LDA topics. We furthermore show that large document collections are less affected by incorrect or noise terms being part of the topic-word distributions, causing topics to be more coherent and ranked higher. Differences between abstract and full-text data are more apparent within small document collections, with differences as large as 90% high-quality topics for full-text data, compared to 50% high-quality topics for abstract data.

Objective To examine the effect of optimising drug treatment on drug related hospital admissions in older adults with multimorbidity and polypharmacy admitted to hospital. Design Cluster randomised controlled trial. Setting 110 clusters of inpatient wards within university based hospitals in four European countries (Switzerland, Netherlands, Belgium, and Republic of Ireland) defined by attending hospital doctors. Participants 2008 older adults (≥70 years) with multimorbidity (≥3 chronic conditions) and polypharmacy (≥5 drugs used long term). Intervention Clinical staff clusters were randomised to usual care or a structured pharmacotherapy optimisation intervention performed at the individual level jointly by a doctor and a pharmacist, with the support of a clinical decision software system deploying the screening tool of older person’s prescriptions and screening tool to alert to the right treatment (STOPP/START) criteria to identify potentially inappropriate prescribing. Main outcome measure Primary outcome was first drug related hospital admission within 12 months. Results 2008 older adults (median nine drugs) were randomised and enrolled in 54 intervention clusters (963 participants) and 56 control clusters (1045 participants) receiving usual care. In the intervention arm, 86.1% of participants (n=789) had inappropriate prescribing, with a mean of 2.75 (SD 2.24) STOPP/START recommendations for each participant. 62.2% (n=491) had ≥1 recommendation successfully implemented at two months, predominantly discontinuation of potentially inappropriate drugs. In the intervention group, 211 participants (21.9%) experienced a first drug related hospital admission compared with 234 (22.4%) in the control group. In the intention-to-treat analysis censored for death as competing event (n=375, 18.7%), the hazard ratio for first drug related hospital admission was 0.95 (95% confidence interval 0.77 to 1.17). In the per protocol analysis, the hazard ratio for a drug related hospital admission was 0.91 (0.69 to 1.19). The hazard ratio for first fall was 0.96 (0.79 to 1.15; 237 v 263 first falls) and for death was 0.90 (0.71 to 1.13; 172 v 203 deaths). Conclusions Inappropriate prescribing was common in older adults with multimorbidity and polypharmacy admitted to hospital and was reduced through an intervention to optimise pharmacotherapy, but without effect on drug related hospital admissions. Additional efforts are needed to identify pharmacotherapy optimisation interventions that reduce inappropriate prescribing and improve patient outcomes. Trial registration ClinicalTrials.gov NCT02986425 .

BackgroundPolypharmacy poses threats to patients’ health. The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) is a drug optimization process for conducting medication reviews in primary care. To effectively and efficiently incorporate this method into daily practice, the STRIP Assistant—a decision support system that aims to assist physicians with the pharmacotherapeutic analysis of patients’ medical records—has been developed. It generates context-specific advice based on clinical guidelines.ObjectiveThe aim of this study was to validate the STRIP Assistant’s usability as a tool for physicians to optimize medical records for polypharmacy patients.MethodsIn an online experiment, 42 physicians were asked to optimize medical records for two comparable polypharmacy patients, one in their usual manner and one using the STRIP Assistant. Changes in effectiveness were measured by comparing respondents’ optimized medicine prescriptions with medication prepared by an expert panel of two geriatrician-pharmacologists. Efficiency was operationalized by recording the time the respondents took to optimize the two cases. User satisfaction was measured with the System Usability Scale (SUS). Independent and paired t tests were used for analysis.ResultsMedication optimization significantly improved with the STRIP Assistant. Appropriate decisions increased from 58 % without the STRIP Assistant to 76 % with it (p < 0.0001). Inappropriate decisions decreased from 42 % without the STRIP Assistant to 24 % with it (p < 0.0001). Participants spent significantly more time optimizing medication with the STRIP Assistant (24 min) than without it (13 min; p < 0.0001). They assigned it a below-average SUS score of 63.25.ConclusionThe STRIP Assistant improves the effectiveness of medication reviews for polypharmacy patients.

Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results have been obtained using classical techniques, such as bag-of-words features, in combination with statistical models. Recently however Deep Learning techniques, such as Word Embeddings and Recurrent Neural Networks, have shown to possibly have even greater potential. In this work, we apply several Deep Learning and classical machine learning techniques to the task of predicting violence incidents during psychiatric admission using clinical text that is already registered at the start of admission. For this purpose, we use a novel and previously unexplored dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. Results show that predicting violence incidents with state-of-the-art performance is possible, and that using Deep Learning techniques provides a relatively small but consistent improvement in performance. We finally discuss the potential implication of our findings for the psychiatric practice.