Letter to Editor (Response from author): Toward a universal electronic health record system.

Health information exchange (HIE) has mostly emerged as centralized data hubs that can pass data requests from one subscribing healthcare institution to another. Using traditional health information systems (HISs) with different technologies in hospitals leads to usability and incompatibility issues because of islands of information. This paper discusses shifting from HIE into an integrated universal health information infrastructure. Migration to such integrated universal electronic health records architecture could support real-time HIE and advanced modern big data analytics. However, there are various standards and technologies to facilitate HIS integration, a significant amount of efforts is still needed.

Standardized electronic health record data modeling and persistence: A comparative review.

With the extensive adoption of electronic health records (EHRs) by several healthcare organizations, more efforts are needed to manage and utilize such massive, various, and complex healthcare data. Databases' performance and suitability to health care tasks are dramatically affected by how their data storage model and query capabilities are well-adapted to the use case scenario. On the other hand, standardized healthcare data modeling is one of the most favorable paths for achieving semantic interoperability, facilitating patient data integration from different healthcare systems. This paper compares the state-of-the-art of the most crucial database management systems used for storing standardized EHRs data. It discusses different database models' appropriateness for meeting different EHRs functions with different database specifications and workload scenarios. Insights into relevant literature show how flexible NoSQL databases (document, column, and graph) effectively deal with standardized EHRs data's distinctive features, especially in the distributed healthcare system, leading to better EHR.

A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks.

Supervised learning with the restriction of a few existing training samples is called Few-Shot Learning. FSL is a subarea that puts deep learning performance in a gap, as building robust deep networks requires big training data. Using transfer learning in FSL tasks is an acceptable way to avoid the challenge of building new deep models from scratch. Transfer learning methodology considers borrowing the architecture and parameters of a previously trained model on a large-scale dataset and fine-tuning it for low-data target tasks. But practically, fine-tuning pretrained models in target FSL tasks suffers from overfitting. The few existing samples are not enough to correctly adjust the pretrained model's parameters to provide the best fit for the target task. In this study, we consider mitigating the overfitting problem when applying transfer learning in few-shot Handwritten Character Recognition (HCR) tasks. A data augmentation approach based on Conditional Generative Adversarial Networks is introduced. CGAN is a generative model that can create artificial instances that appear more real and indistinguishable from the original samples. CGAN helps generate extra samples that hold the possible variations of human handwriting instead of applying traditional image transformations. These transformations are low-level, data-independent operations, and only produce augmented samples with limited diversity. The introduced approach was evaluated in fine-tuning the three pretrained models: AlexNet, VGG-16, and GoogleNet. The results show that the samples generated by CGAN can enhance transfer learning performance in few-shot HCR tasks. This is by achieving model fine-tuning with fewer epochs and by increasing the model's [Formula: see text] and decreasing the Generalization Error [Formula: see text].

Effect of combination lipid-modifying therapy on the triglyceride lowering effect of fish oil.

BACKGROUND: Marine fish oil supplements are frequently administered with other lipid medications for treatment of hypertriglyceridemia. The efficacy of fish oil may be reduced in the presence of other lipid agents, particularly fibrates that also act as PPARalpha agonists. We therefore sought to determine the efficacy of fish-oil supplements when coadministered with other lipid-modifying agents. METHODS: Patients receiving fish oil supplements were identified from the computer database of a large governmental HMO. Change in plasma lipoprotein levels after administration of fish oil was compared between patients receiving fish oil as their only treatment and those for whom fish oil was added to other drugs. RESULTS: A total of 166 evaluable records were identified, 66 from patients treated with fish oil alone and 100 from patients for whom fish oil was added to another agent or other agents. Fish oil effectively reduced triglyceride levels to an equal extent in the fish oil only and fish oil added groups (-30% versus -27% respectively; P = 0.84). CONCLUSION: Fish oil effectively reduces plasma triglyceride levels when administered with concomitant lipid medications. These findings suggest the presence of additional and even complementary mechanisms of action of fish oil to lower triglyceride when added to other lipid drugs. These findings validate the common clinical practice of combining fish oil supplements with other lipid-lowering medications in patients with hypertriglyceridemia.