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s' roles in an interdisciplinary clinic model correlates with increased attendance to initial PCP visits, a surrogate for patient engagement. Disease-state education and medication education are both important activities in improving this measure; however, additional research is necessary to determine specific pharmacist interventions associated with patient engagement. As research in patient engagement continues, the positive effects of pharmacist involvement in this area could support their value in ambulatory care services.This review analyses the relationship between instrumental and human data used to assess the mouthfeel of solid oral dosage forms to provide recommendations on the most appropriate methods to use in future studies.Human epidermal growth factor receptor 2 (HER2), a tyrosine kinase receptor with a molecular mass of 185kDa, is overexpressed in several cancers, such as breast, gastric, ovary, prostate, and lung. HER2 is a promising target in cancer therapy because of its crucial role in cell migration, proliferation, survival, angiogenesis, and metastasis through various intracellular signaling cascades. This receptor is an ideal target for the delivery of chemotherapeutic agents because of its accessibility to the extracellular domain. In this review, we highlight different HER2-targeting strategies and various approaches for HER2-targeted delivery systems to improve outcomes for cancer therapy.
Pediatric acute-onset neuropsychiatric syndrome (PANS) is a complex neuropsychiatric syndrome characterized by an abrupt onset of obsessive-compulsive symptoms and/or severe eating restrictions, along with at least two concomitant debilitating cognitive, behavioral, or neurological symptoms. A wide range of pharmacological interventions along with behavioral and environmental modifications, and psychotherapies have been adopted to treat symptoms and underlying etiologies. Our goal was to develop a data-driven approach to identify treatment patterns in this cohort.
In this cohort study, we extracted medical prescription histories from electronic health records. We developed a modified dynamic programming approach to perform global alignment of those medication histories. Our approach is unique since it considers time gaps in prescription patterns as part of the similarity strategy.
This study included 43 consecutive new-onset pre-pubertal patients who had at least 3 clinic visits. Our algorithm identified six clusters with distinct medication usage history which may represent clinician's practice of treating PANS of different severities and etiologies i.e., two most severe groups requiring high dose intravenous steroids; two arthritic or inflammatory groups requiring prolonged nonsteroidal anti-inflammatory drug (NSAID); and two mild relapsing/remitting group treated with a short course of NSAID. The psychometric scores as outcomes in each cluster generally improved within the first two years.
Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs.
Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs.Temporal medical data are increasingly integrated into the development of data-driven methods to deliver better healthcare. Searching such data for patterns can improve the detection of disease cases and facilitate the design of preemptive interventions. For example, specific temporal patterns could be used to recognize low-prevalence diseases, which are often under-diagnosed. However, searching these patterns in temporal medical data is challenging, as the data are often noisy, complex, and large in scale. In this work, we propose an effective and efficient solution to search for patients who exhibit conditions that resemble the input query. PP242 price In our solution, we propose a similarity notion based on the Longest Common Subsequence (LCSS), which is used to measure the similarity between the query and the patient's temporal medical data and to ensure robustness against noise in the data. Our solution adopts locality sensitive hashing techniques to address the high dimensionality of medical data, by embedding the recorded clinical events (e.g., medications and diagnosis codes) into compact signatures. To perform pattern search in large EHR datasets, we propose a filtering approach based on tandem patterns, which effectively identifies candidate matches while discarding irrelevant data. The evaluations conducted using a real-world dataset demonstrate that our solution is highly accurate while significantly accelerating the similarity search.Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.