Patient Similarity Analytics
- Analyze aggregated demographic, social, clinical, and financial factors along with unstructured data such as physicians’ notes
- Factor the specific health history of each individual patient into the creation of a personalized healthcare delivery plan
- Enable healthcare professionals to examine thousands of patient characteristics at once to generate personalized treatment plans
- Identify other patients with similar clinical characteristics to see what treatments were most effective or what complications they may have encountered
- Support patient-physician matching so an individual is paired with a doctor that is optimal for a specific condition
- Allow healthcare professionals to better tap into the collective memory of the care delivery system to uncover new levels of tailored insight or “early identifiers” from historical/long-term patient data
AMSTAT Consulting can analyze the broadest range of streaming data – unstructured text, video, audio, geospatial, sensor – while making decisions as events are happening. We can bring meaning to fast-moving data streams.
- Connect with virtually any data source whether structured, unstructured or streaming, and integrate with Hadoop, Spark, and other data infrastructures
- Offer a complete solution with a development environment runtime and analytics toolkits such as natural language processing, image/voice recognition, and spatial-temporal analysis
- Integrate with business solutions, built-in domain analytics like machine learning, natural language, spatial-temporal, text, acoustic, and more, to create adaptive streams applications
- Perform real-time analysis for ICU patient data streams
- Mine patient monitoring data for the discovery of early detection patterns
Radiologists and cardiologists today have to view large amounts of imaging data relatively quickly leading to eye fatigue. Further, they have only limited access to clinical information relying mostly on their visual interpretation of imaging studies for their diagnostic decisions. We can use Medical Sieve, an automated cognitive assistant for radiologists and cardiologists designed to help in their clinical decision-making. We can:
- Collect clinical, textual and imaging data of patients from electronic health records systems
- Analyze multimodal content to detect anomalies if any, and summarizes the patient record collecting all relevant information pertinent to a chief complaint
- Feed the results of anomaly detection into a reasoning engine which uses evidence from both patient-independent clinical knowledge and large-scale patient-driven similar patient statistics to arrive at potential differential diagnosis to help in clinical decision making
- Summarize all relevant information to the clinician per chief complaint
- Retain links to the raw data for detailed review providing holistic summaries of patient conditions.
Results of clinical studies in the domains of cardiology and breast radiology have already shown the promise of the system in differential diagnosis and imaging studies summarization.
Metagenomics (also referred to as environmental and community genomics) is the genomic analysis of microorganisms by direct extraction and cloning of DNA from an assemblage of microorganisms. The development of metagenomics stemmed from the ineluctable evidence that as-yet-uncultured microorganisms represent the vast majority of organisms in most environments on earth.
- Community metabolism
Using comparative gene studies and expression experiments with microarrays or proteomics, we can piece together a metabolic network that goes beyond species boundaries. We can use detailed knowledge about which versions of which proteins are coded by which species and even by which strains of which species. Therefore, community genomic information is another fundamental tool (with metabolomics and proteomics) in the quest to determine how metabolites are transferred and transformed by a community.
We can analyze metagenomic mRNA (the metatranscriptome) to provide information on the regulation and expression profiles of complex communities. We can use transcriptomics technologies to measure whole-genome expression and quantification of a microbial community.
Metagenomic sequencing is particularly useful in the study of viral communities. As viruses lack a shared universal phylogenetic marker (as 16S RNA for bacteria and archaea, and 18S RNA for eukarya), the only way to access the genetic diversity of the viral community from an environmental sample is through metagenomics. Viral metagenomes should thus provide more and more information about viral diversity and evolution.