Thinking about the problems together with managing lithium-sulfur battery packs through "mapping" in

Author : Griffith Forsyth | Published On : 19 Apr 2025

Mapping local terminologies to standardized terminologies facilitates secondary use of electronic health records (EHR). Penn Medicine comprises multiple hospitals and facilities within the Philadelphia Metropolitan area providing services from primary to quaternary care. Our Penn Medicine (PennMed) data include medications collected during both inpatient and outpatient encounters at multiple facilities. Our goal was to map 941,198 unique medication terms to RxNorm, a standardized drug nomenclature from the National Library of Medicine (NLM). We chose three popular tools for mapping NLM's RxMix and RxNav-in-a-Box, OHDSI's Usagi and Mayo Clinic's MedXN. We manually reviewed 400 mappings obtained from each tool and evaluated their performance for drug name, strength, form, and route. RxMix performed the best with an F1 score of 90% for drug name versus Usagi's 82% and MedXN's 74%. We discuss the strengths and limitations of each method and tips for other institutions seeking to map a local terminology to RxNorm.In this paper, we investigate the task of spatial role labeling for extracting spatial relations from chest X-ray reports. Previous works have shown the usefulness of incorporating syntactic information in extracting spatial relations. We propose syntax-enhanced word representations in addition to word and character embeddings for extracting radiologyspecific spatial roles. We utilize a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) as the baseline model to capture the word sequence and employ additional Bi-LSTMs to encode syntax based on dependency tree substructures. Our focus is on empirically evaluating the contribution of each syntax integration method in extracting the spatial roles with respect to a SPATIAL INDICATOR in a sentence. The incorporation of syntax embeddings to the baseline method achieves promising results, with improvements of 1.3, 0.8, 4.6, and 4.6 points in the average F1 measures for TRAJECTOR, LANDMARK, DIAGNOSIS, and HEDGE roles, respectively.Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision. Further, we build models that predict the likelihood of bacterial susceptibility to sets of antibiotics. These models contain decision thresholds that separate subgroups of patients whose susceptibility rates to narrow-spectrum antibiotics equal overall susceptibility rates to broader-spectrum drugs. Retroactively analyzing these thresholds on our one year test set, we find that 14% of patients infected with Escherichia coli and empirically treated with piperacillin/tazobactam could have been treated with ceftriaxone with coverage equal to the overall susceptibility rate ofpiperacillin/tazobactam. Similarly, 13% of the same cohort could have been treated with cefazolin - a first generation cephalosporin.Asthma is a prevalent chronic respiratory condition, and acute exacerbations represent a significant fraction of the economic and health-related costs associated with asthma. We present results from a novel study that is focused on modeling asthma exacerbations from data contained in patients' electronic health records. This work makes the following contributions (i) we develop an algorithm for phenotyping asthma exacerbations from EHRs, (ii) we determine that models learned via supervised learning approaches can predict asthma exacerbations in the near future (AUC ≈ 0.77), and (iii) we develop an approach, based on mixtures of semi-Markov models, that is able to identify subpopula-tions of asthma patients sharing distinct temporal and seasonal patterns in their exacerbation susceptibility.Clinical decision support tools that automatically disseminate patterns of clinical orders have the potential to improve patient care by reducing errors of omission and streamlining physician workflows. However, it is unknown if physicians will accept such tools or how their behavior will be affected. In this randomized controlled study, we exposed 34 licensed physicians to a clinical order entry interface and five simulated emergency cases, with randomized availability of a previously developed clinical order recommender system. With the recommender available, physicians spent similar time per case (6.7 minutes), but placed more total orders (17.1 vs. 15.8). The recommender demonstrated superior recall (59% vs 41%) and precision (25% vs 17%) compared to manual search results, and was positively received by physicians recognizing workflow benefits. Further studies must assess the potential clinical impact towards a future where electronic health records automatically anticipate clinical needs.Although experts have identified benefits to replacing paper with electronic consent (eConsent) for research, a comprehensive understanding of strategies to overcome barriers to adoption is unknown. To address this gap, we performed a scoping review of the literature describing eConsent in academic medical centers. Of 69 studies that met inclusion criteria, 81% (n=56) addressed ethical, legal, and social issues; 67% (n=46) described user interface/user experience considerations; 39% (n=27) compared electronic versus paper approaches; 33% (n=23) discussed approaches to enterprise scalability; and 25% (n=17) described changes to consent elections. Findings indicate a lack of a leading commercial eConsent vendor, as articles described a myriad of homegrown systems and extensions of vendor EHR patient portals. Opportunities appear to exist for researchers and commercial software vendors to develop eConsent approaches that address the five critical areas identified in this review.In most electronic health record (EHR) systems, clinicians record diagnoses using interface terminologies, such as Intelligent Medical Objects (IMO). When extracting data from EHRs for collaborative research, local codes are often transformed to standard terminologies for consistent analyses despite the potential for loss of fidelity. check details EHR diagnosis codes may be standardized directly during the Extract-Transform-Load (ETL) process to the "Meaningful Use" clinical data exchange standard, SNOMED-CT, or to the International Classification of Diseases (ICD) terminologies commonly used for billing. We examined the performance of ETL standardization via the direct IMO mapping to SNOMED-CT, and via IMO mapping to ICD-9-CM or ICD-10-CM followed by UMLS mapping to SNOMED-CT. We found that for both ICD-9-CM and ICD-10-CM, only 24-27% of diagnosis codes map to the same SNOMED-CT code selected by the direct IMO-SNOMED crosswalk. We identified that differences in mapping lead to loss in the granularity and laterality of the initial diagnosis.