Developing a Web-based Interface of Sensor Technologies for Interventionists
The widespread adoption and growth of the consumer sensor technologies, including wearable and in-home sensors, present an opportunity for ambient monitoring of activities of daily living (ADLs) for health and behavioral intervention. Yet no open-source systems exist to incorporate disparate sensor technologies for health care professionals and researchers to monitor client/patient ADLs and assist individuals with health and wellness goals. The purpose of this study is to design and develop a data interface, using a user-centered co-design approach, for health care professionals. Informatics and design researchers engage with nurses, occupational therapists, and social workers to create a user-friendly and web-based…
Artificial Intelligence in Disease Prediction using Real World Health Data: Trends, Challenges, and The Future
Recent years have seen significant progress in using artificial intelligence (AI) to develop disease prediction models, which have the potential to improve diagnosis precision, enable early disease prevention, streamline clinical decision making, and reduce healthcare costs. This progress has been supported by the availability of large and diverse biomedical data, including Electronic Health Records (EHRs), which have become a valuable resource for disease prediction. The wide adoption of structured EHR systems has enabled historical patient records to serve as one of the most valuable resources for disease prediction. Traditional research for building disease prediction models relied on experts’ ability to…
Rural populations facilitate early SARS-CoV-2 evolution and transmission
In the United States, rural populations comprise 60 million individuals and suffer from high COVID-19 disease burdens. Despite this, surveillance efforts are biased toward urban centers. Consequently, how rurally circulating SARS-CoV-2 viruses contribute toward emerging variants remains unknown. In this study, 544 urban and 435 rural COVID-19-positive respiratory specimens were collected from two healthcare systems in Missouri between July and December 2020, prior to COVID-19 vaccines. We saw high genetic diversity with 14 of 53 SARS-CoV-2 Pango lineages detected only in rural samples. The lineage diversity of SARS-CoV-2 in rural communities gradually increased whereas those in urban areas remained similar during…
Exploration of Obesity and Multimorbidity at a Single Academic Institution.
In the US, overweight and obese adults account for more than two-thirds of the total population. Obesity has been established as a risk factor in many chronic conditions; and chronic conditions account for seven out of the top 10 leading causes of death and disability in the US. Multimorbidity occurs when a patient has two or more chronic conditions at the same time (without a single predominant condition). Research has established obesity as a risk factor for many chronic conditions; however, little is known about the co-occurrence of these conditions and the role that obesity plays. This ongoing research is…
Identification of Spatially Variable Genes
Invasive species pose a unique threat to native species and habitat through direct and indirect competition of resources. Management of invasive species depends on precise identification of their current range and knowledge of how they spread. This project will utilize Plantescope Satellite Imagery to identify the presence of Callery Pear, an invasive ornamental tree species in Missouri. The unique phenology of the Callery Pear should allow for precise identification through Random Forest classification of the imagery. After identification, a logistic model will be built to predict the presence or absence of Callery Pear in the landscape based on a variety…
Modeling Pyrus calleryana spread in central Missouri using remote sensing and a non-parametric modeling approach
Invasive species pose a unique threat to native species and habitat through direct and indirect competition of resources. Management of invasive species depends on precise identification of their current range and knowledge of how they spread. This project will utilize Plantescope Satellite Imagery to identify the presence of Callery Pear, an invasive ornamental tree species in Missouri. The unique phenology of the Callery Pear should allow for precise identification through Random Forest classification of the imagery. After identification, a logistic model will be built to predict the presence or absence of Callery Pear in the landscape based on a variety…
Relationships between mortality and respiratory health, medication usage, water disappearance, and climatic conditions in commercial weaning-to-finishing pigs
Mortality in pigs is of paramount economic importance to United States pig producers. According to data compiled by MetaFarms SMS and Pork Checkoff in 2022, 21.2% of pigs die before reaching slaughter, and this statistic has increased, in general, over the last 5 years. Although only 6.8% of these losses are attributed to pigs in the wean-to-finish production phase, these pigs are more valuable due to added daily feed expenditure. Thus, reduction of weaning-to-finishing pig mortality is necessary to improve sustainability and profitability of current pig production operations. The objective of the current study was to identify health and climatic variables that are…
DeepVariant – TrioTrain: Developing a transfer learning protocol using non-human genomes
Genomic data are widely available for investigating phenotypes that impact both human and animal health. Although the investigation of human health often begins with model organisms, genomics technologies and software are often initially developed with only the human genome in mind, severely limiting their comparative applicability. Translational research requires robust systems-focused, or “One Health,” solutions that enable mutual progress across the animal, plant, and human genomics communities. Regardless of species, genomics faces a common challenge: continuous data re-processing due to a rapidly increasing sample size. The Genome Analysis Toolkit (GATK) is currently the preferred method for calling variants with short-read,…
A combined AI approach to biomedical data analysis: Knowledge representation reasoning, machine learning and explainable AI
In this talk, I will explore if and how two traditionally distinct fields of AI, that is, ontology engineering and machine learning can be combined to improve performance outcomes. Using real world examples from epilepsy neurological disorder, the talk will demonstrate the use of biomedical ontologies in machine learning workflows to address the critical challenge of feature engineering in multi-modal non-numeric phenotype data. Specifically, we will discuss how biomedical ontologies can improve the performance of machine learning models and the runtime performance of machine learning algorithms. Further, the talk will also explore the role of explainable AI in the context of analyzing electronic health…