Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within each visit. In this work, we propose Med2Vec, which not only learns distributed representations for both medical codes and visits from a large EHR dataset with over 3 million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec displays significant improvement in key medical applications compared to popular baselines such as Skipgram, GloVe and stacked autoencoder, while providing clinically meaningful interpretation.
OBJECTIVES: An efficient and reliable process for measuring harm due to medical care is needed to advance pediatric patient safety. Several pediatric studies have assessed the use of trigger tools in varying inpatient environments. Using the Institute for Healthcare Improvement’s adult-focused Global Trigger Tool as a model, we developed and pilot tested a trigger tool that would identify the most common causes of harm in pediatric inpatient environments. METHODS: After formal training, 6 academic children’s hospitals used this novel pediatric trigger tool to review 100 randomly selected inpatient records per site from patients discharged during the month of February 2012. RESULTS: From the 600 patient charts evaluated, 240 harmful events (“harms”) were identified, resulting in a rate of 40 harms per 100 patients admitted and 54.9 harms per 1000 patient days across the 6 hospitals. At least 1 harm was identified in 146 patients (24.3% of patients). Of the 240 total events, 108 (45.0%) were assessed to have been potentially or definitely preventable. The most common patient harms were intravenous catheter infiltrations/burns, respiratory distress, constipation, pain, and surgical complications. CONCLUSIONS: Consistent with earlier rates of all-cause harm in adult hospitals, harm occurs at high rates in hospitalized children. Availability and use of an all-cause harm identification tool will establish the epidemiology of harm and will provide a consistent approach to assessing the effect of interventions on harms in hospitalized children.
Application of a modified Delphi method on an expert-constructed list of 108 triggers, focusing on severity and frequency of harms as well as detectability of triggers in an electronic medical record, resulted in a final list of 51 pediatric triggers. Pilot testing this list of pediatric triggers to identify all-cause harm for pediatric inpatients is the next step to establish the appropriateness of each trigger for inclusion in a global pediatric safety measurement tool.
No abstract
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. Despite recent improvements on unconstrained PARAFAC2, its model factors are usually dense and sensitive to noise which limits their interpretability. As a result, the following open challenges remain: a) various modeling constraints, such as temporal smoothness, sparsity and non-negativity, are needed to be imposed for interpretable temporal modeling and b) a scalable approach is required to support those constraints efficiently for large datasets. To tackle these challenges, we propose a COnstrained PARAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors. To efficiently support all those constraints, COPA adopts a hybrid optimization framework using alternating optimization and alternating direction method of multiplier (AO-ADMM). As evaluated on large electronic health record (EHR) datasets with hundreds of thousands of patients, COPA achieves significant speedups (up to 36× faster) over prior PARAFAC2 approaches that only attempt to handle a subset of the constraints that COPA enables. Overall, our method outperforms all the baselines attempting to handle a subset of the constraints in terms of speed, while achieving the same level of accuracy. Through a case study on temporal phenotyping of medically complex children, we demonstrate how the constraints imposed by COPA reveal concise phenotypes and meaningful temporal profiles of patients. The clinical interpretation of both the phenotypes and the temporal profiles was confirmed by a medical expert. CCS CONCEPTS • Information systems → Data mining;
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