Lexenco Clinical Language Encoder (CLE)™ is a clinical Natural Language Processing (NLP) engine deployed as SaaS used to automatically, accurately and consistently harvest clinical information and medical codes from unstructured text, with supporting clinical concept, relevance, source location, coding advice, relationships and hierarchy.
Section Inclusion/Exclusion: Select the medical record section types to process, e.g. include “History of Present Illness” and exclude “Past Medical History”
Clinical Semantic Compression: Clinical terms within source text are semantically compressed to candidate clinical concepts, such as “amobarbital”, “Amsal”, and hundreds of other terms for “barbiturates” in ICD-10-CM T42.3X1 “Poisoning by barbiturates, accidental”
Section Aware: The section of the clinical concept term is used to improve clinical code extraction, such as ICD-10-CM Z80.6 “Family history of leukemia” vice C91 through C95 codes
Negation Aware: Negation expression detection with corresponding clinical concept term tagged, used to negate output and extract clinical codes with negation, such as ICD-10-CM A37.90 “Whooping cough, unspecified species without pneumonia”
Source Text Input Formats: Text field, and text, Word and PDF documents
Context Aware: Experiencer and temporality aware processing provides context understanding of the extracted clinical concepts
Code Concept Decomposition: Category type code sets such as ICD-10-CM have compounded definitions that are broken down to atomic clinical concepts then matched separately against a respective ontology, and composed across a section, such as S72.001B “Fracture of unspecified part of neck of right femur, initial encounter for open fracture type I or II” – “minimal/ moderate soft tissue injury”, etc.
Returned Information: Options for returned information of the extracted code includes relevance, category, source location and section, coding advice, and clinical concept
Clinical Code Navigation: Navigate across clinical code set hierarchy by clinical code, get code by clinical concept, and get tree for a set of clinical codes