Researches
Duration:
2022-07-01 to
2022-07-01
Description:
A Blockchain consists of a continuously growing list of records called blocks. Each block represents a set of transactions and is cryptographically linked to its previous block thus forming a chain. A Blockchain is managed by a peer-to-peer network of nodes that validate new blocks using a consensus algorithm.
In this research, we are developing a Blockchain-based patient record linkage system that will provide privacy for the patient's health data. Patients, Doctors, HIS (Hospital Information System), and TeleHealth are interconnected with Blockchain to store and share health records maintaining the privacy parameter of the smart contract. The key part of this research is that patient is the owner of their own data. They can decide whether an entity can read their health data or not.
Link: Visit
Duration:
2022-01-01 to
2022-01-01
Description:
Data standards are the principal informatics components necessary for information flow through the national health information infrastructure. Common data standards support the broad scope of data collection and reporting requirements, effective assimilation of new knowledge into decision support tools, and data exchange.
In this research, we are developing a standardized data dictionary with a coding system and an interoperability framework for clinical registries. The standardized data has covered a broad range of clinical data, including laboratory tests, specimens, measurement units, etc. The attribute of the data dictionary that mapped with the internationally accepted format of data exchange & interoperability standards provided by the organizations like HL7, SNOMED CT, LOINC, NHS Pathology, WHO/ICD-11. An Intelligence Data interfacing technique using machine learning will be implemented for mapping the existing clinical data with the coding system. A web service platform will be developed for managing the standardized data directory, registry resource sharing, and interfacing options with the stakeholders.
Duration:
2021-09-01 to
Current
Description:
Clinical Decision Support Systems (CDSS) have shown significant improvement in accurate disease diagnosis and reduce preventable medical errors (PMEs), a leading cause of patient mortality. However, most of the existing CDSSs are employed in a clinical setting, and outside of the clinical domain, they rely on patients recalling their syndromes which can be error-prone. Furthermore, individuals may not detect the early symptoms of diseases. To resolve these issues, we are modeling an integrated Health Decision Support System (HDSS) that integrates patient-specific clinical, non-clinical data (i.e., lifestyle, environmental, demographic, and physiological), and vital signals from wearable medical sensors (WMS) to analyze patients’ disease risk, provide health decision support, and monitor post-diagnostic wellness using machine learning and data-driven approaches.
Link: Visit
Duration:
2021-08-01 to
Current
Description:
Virtual Internship systems (VIS) are designed and implemented to offer the interns a virtual industrial environment to gain work experience similar to real-life scenarios. Though VIS has been proved to solve many internship problems like intern placement, technology matching, etc. But the measurement of the learning outcome of interns remains a challenge for all types of internships. Measuring the learning outcome based on revised Bloom’s Taxonomy has been described in previous works. This measurement is applicable for typical e-learning systems. But the learning outcome of VIS needs to be measured by the gain of professional experience based on quantitative and qualitative attributes. Existing research on the measurement of learning outcome focuses on the quantification of learners' knowledge level but no research is found to evaluate the learning outcome based on both quantitative and qualitative attributes at the board level