DigitalTwin.health

  • DigitalTwin.health Face Sheet

DigitalTwin.health

June 30, 2020

DigitalTwin.health presents a concept map of medical and genomic data to define an individual’s “digital twin.” It is a platform and interface for accessing genomic data relevant to the personal health record. The interface includes a map of the person’s genome and identified gene variants that may be relevant to diagnosed medical problems/conditions mapped to body systems, value sets, and clinical code sets over time.

The user can get detailed information about the person’s genome, highlighting genetic variations that may have implications for diagnosed diseases, conditions, treatments, and prescribed medications.

Users can drill down for detail on the human genome, along with detail on specific genes and gene variants that have been identified through genetic profiling. In addition, users can click on the individual gene variant to get additional detail, and see a depiction of the gene, along with the location of the variant.

Variants are described in detail along with information on the type of interaction that may be seen. References are provided for clinicians to retrieve additional information online.

Relevant gene variations are mapped to the specific diseases and conditions, which are further mapped to body systems, value sets, severity, and clinical coding systems.

Detail on the specific diseases or conditions that are identified are displayed in popup windows when the item is clicked. Current status, along with a simple graph of history are displayed.

The user can discover significant events in the patient’s longitudinal care by entering the “time machine” by clicking on the timeline. The genetically mapped medical record for that date will be displayed. A circle graph shows genetic changes from baseline over time.

Data can be analyzed and viewed in other contexts using “clustering” to identify organic groups in the Ditigaltwin.health network. When you run the clustering process, it applies the Louvain Modularity algorithm and finds the tightly knit groups characterized by a relatively high density of ties. For example, you might identify genetic variants by zip code to begin to uncover some of the relationships between social determinants of health and a person’s health risk.

Client name
LetterPress Logo
Skills
Photoshop, Adobe Illustrator
Website