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Using Social Network Analysis to Describe Communication Practices in Healthcare

The Electronic Medical Record (EMR) serves different purposes including documentation of care and billing. The EMR is used to document care delivery, monitor ongoing clinical conditions, and it is also the repository of the patients’ healthcare story. Many people, including healthcare providers, nurses, social workers, therapists, office staff, and nurse care managers (known as the

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Challenges for the analysis of healthcare reports using natural language processing

Healthcare professionals generate, transmit, and store healthcare records as free-text documents these are the traditional“physician’s reports” or “physician’s notes”. These reports contain complex biomedical data, demographic information, location data, etc. However, free text data are a poor starting point for complex data management, aggregation and processing tasks with computational models. For data-based applications, information from

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Applying Blockchain Technology for Health Information Exchange and Persistent Monitoring for Clinical Trials

“Blockchain” is a distributed ledger technology originally applied in the financial sector. This technology ensures the integrity of transactions without third-party validation. Its functions of decentralized transaction validation, data provenance, data sharing, and data integration are a good fit for the needs of health information exchange and clinical trials. We investigated the current workflow of

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Development and Application of a high throughput multiomics pipeline to uncover molecular signatures in drought stressed Maize Nodal Roots

One of the major focus areas in agricultural research is reducing the major limiting factor of drought for agricultural production worldwide. In the maize plant, water uptake is mainly acquired by the nodal root system after the seedling stage. These roots grow from multiple stem nodes, initially from below-ground and later from above-ground nodes. In

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Using Deep learning method (CNN/RNN) for prediction of ubiquitination protein

Ubiquitination, as a post-translational modification, is a crucial biological process presented in cell signaling, death and localization. Identification of ubiquitination protein is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins

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Discovering novel risk factors for age-related eye diseases using subgroup data mining

In the United States, the Medicare cost for the treatment of cataract is continually increasing due to an increased number of cataract surgery done in each year, for example, more than 3 million cataract surgeries were performed in 2017. A study by the World Health Organization reported that a delay of 10 years of the

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eCaregiving 2.0: Feasibility, Data Quality and Cost of Collecting Continuous Self-reported and Passive Data Using a Personal Health Management System

This project aims to demonstrate if self-reported and passive data can be continuously collected from a cohort of 55 study participants using Personal Health Management Information System (PHMS).  We estimated measures of data quality, feasibility and cost of data collection after asking participants to wear Fitbit and download the app to manage data transmission from

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Immunogenomic Pathway and Survival Analysis in Colorectal Cancer Patients Based on Tumor Location and Microsatellite Status

Despite the advancement of available therapies (surgery, chemotherapy and immunotherapy, etc.), colorectal cancer (CRC) as the third most common cancer still remains the second leading cause of cancer-related death worldwide. Typically, CRC patients could be categorized into microsatellite stable (MSS, approximately80-85% in CRC) or microsatellite instability (MSI, approximately 10-15% in CRC) type. An extensive literature

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Income Inequality and Health: Expanding our Understanding of State Level Effects by using a Geospatial Big Data Approach

The income inequality hypothesis proposes that ecological income inequality is harmful for population health but findings from extant work are inconsistent across health outcomes and levels of geography. We contribute to this debate by applying a big data geospatial approach to create three innovative measures of uniformity in income inequality across space within US states.

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Using Deep learning method (CNN) for prediction of ubiquitination protein

Ubiquitination, as a post-translational modification, is a crucial biological process presented in cell signaling, death and localization. Identification of ubiquitination protein is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins

Read More