Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research.

BackgroundJuvenile idiopathic arthritis is the most common rheumatic disease in children. Chronic uveitis is a common and serious comorbid condition of juvenile idiopathic arthritis, with insidious presentation and potential to cause blindness. Knowledge of clinical associations will improve risk stratification. Based on clinical observation, we hypothesized that allergic conditions are associated with chronic uveitis in juvenile idiopathic arthritis patients.MethodsThis study is a retrospective cohort study using Stanford's clinical data warehouse containing data from Lucile Packard Children's Hospital from 2000-2011 to analyze patient characteristics associated with chronic uveitis in a large juvenile idiopathic arthritis cohort. Clinical notes in patients under 16 years of age were processed via a validated text analytics pipeline. Bivariate-associated variables were used in a multivariate logistic regression adjusted for age, gender, and race. Previously reported associations were evaluated to validate our methods. The main outcome measure was presence of terms indicating allergy or allergy medications use overrepresented in juvenile idiopathic arthritis patients with chronic uveitis. Residual text features were then used in unsupervised hierarchical clustering to compare clinical text similarity between patients with and without uveitis.ResultsPreviously reported associations with uveitis in juvenile idiopathic arthritis patients (earlier age at arthritis diagnosis, oligoarticular-onset disease, antinuclear antibody status, history of psoriasis) were reproduced in our study. Use of allergy medications and terms describing allergic conditions were independently associated with chronic uveitis. The association with allergy drugs when adjusted for known associations remained significant (OR 2.54, 95% CI 1.22-5.4).ConclusionsThis study shows the potential of using a validated text analytics pipeline on clinical data warehouses to examine practice-based evidence for evaluating hypotheses formed during patient care. Our study reproduces four known associations with uveitis development in juvenile idiopathic arthritis patients, and reports a new association between allergic conditions and chronic uveitis in juvenile idiopathic arthritis patients

Pattern discovery in adverse event data

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Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients

Rett syndrome (RTT) is a rare neurological disorder that predominantly affects girls. Research on RTT has mostly centered around gene mutations and possibility of cure using gene therapy. In this thesis we perform the first large scale systematic study of RTT patient records. The thesis has two major goals. One is to identify behavioral groups and the other is to study the association of medications and behavior or conditions. To achieve the first goal we apply standard clustering techniques like non-negative matrix factorization and k-means. We identify behavioral groups which could be used by clinicians for formulating better treatments. For the second goal we start with the most popular existing technique, disproportionality analysis, and make necessary adaptations for our data set. We then generalize this method and suggest an alternate approach which efficiently answers which medication caused the most change in a behavior. We test both approaches and show that the medications shown to decrease seizures the most are indeed those prescribed for the same. Using this as a tool, clinicians can identify possible side effects of medications

Patients centred pharmacovigilance

In recent years, periodically high peaks of attention and publications have documented severe adverse reactions to new molecules, which have raised many questions about the efficacy- efficiency of traditional methodological tools of phannacovigilance, as well as about the role of regulatory systems. Drugs cannot be considered as an independent variable: the evaluation of all of their effects must take into account the real context in which they are used, and in which they are expected have a role, not only in terms of efficacy, but also of tolerability and safety. Specific emphasis is given to recent and promising developments, which are focused on the participation of patient populations as key actors in producing knowledge that can also technically integrate what has been produced so far, and can allow the evolution of surveillance from a role of control to one of the promotion of rights. The replacement of phannacovigilance in an epidemiological context is the main aim of this project. This is applied across the development of various projects realised in different scenarios (e.g. hospital, community) using different methodologies (e.g. administrative database linkage, prospective studies, qualitative projects), and through the direct involvement of all of the actors involved in the process of care (e.g. clinicians, general practitioners, patients). In particular, despite the many recommendations, patient participation can be considered as an exception in the health care setting: for this reason the project was developed with the intention to give voice to patients. Promotion of the use of a more narrative style between health professionals and citizen-patients in phannacovigilance should be considered the most important outcome of a renewed phannacovigilance

Can the Heinrich ratio be used to predict harm from medication errors?

The purpose of this study was to establish whether, for medication errors, there exists a fixed Heinrich ratio between the number of incidents which did not result in harm, the number that caused minor harm, and the number that caused serious harm. If this were the case then it would be very useful in estimating any changes in harm following an intervention. Serious harm resulting from medication errors is relatively rare, so it can take a great deal of time and resource to detect a significant change. If the Heinrich ratio exists for medication errors, then it would be possible, and far easier, to measure the much more frequent number of incidents that did not result in harm and the extent to which they changed following an intervention; any reduction in harm could be extrapolated from this

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table