Electronic health records

Ms. Jones is a married, 28 year old female. She is a second year resident. Her chief complaint is depression. She has anxiety and feels constantly run down.

Concepts

Ms. Jones is a married status, 28 year old age female gender. She is a second year resident occupation. Her chief complaint is depression symptom. She has anxiety symptom and feels constantly run down symptom.

Decision support

Patient Profile

Name:

Ms. Jones

Age / gender:

28 / F

Occupation:

resident

Symptoms:

depression, anxiety, fatigue

K-Extractorâ„¢: Transform health records to structured knowledge

Customer Scenario
NEED

Summarize Electronic Health Records (EHR) of a patient to support decision making for treatment or insurance quoting. Analyzing EHR in bulk to find cohorts or recognize outliers for fraud detection.

CHALLENGE

Usage of various expressions to describe the same finding, for example, both 'feeling worn out' and 'exhausted' refer to 'fatigue'. Smallest details matter: findings can have attributes like dosage, onset, time course, etc. Negative findings and findings related to family history need to be recognized correctly.

Lymba's Solution
SOLUTION

K-Extractor trained for medical domain recognizes important medical concepts and their attributes in EHR. The key findings are grouped together and represented as a structured profile.

INPUT

Electronic Health Records.

OUTPUT

Structured representation of symptoms, signs, conditions, diseases, taken medication, procedures, etc.

Key Features

Ontologies:

K-Extractor recognizes concepts from existing medical lexical resources including UMLS Metathesaurus, SNOMED-CT, & Medline (health signs, symptoms, diseases, medication, etc) as well as socio-demographic characteristics and relations (race, gender, age, nationality, insurance, financial status, education level) and semantic relations between these concepts.

Attributes Recognition:

Recognition and linking of various attributes: onset, severity, time course, symptom quality, alleviating or aggravating factors, .

Similarity Recognition:

The system resolves duplicates expressed with different words, groups related findings together.

Check out K-Extractor for more details on the product and its features.