Table 1 Induced objective tasks with their definition and an example

Concept detection ¹

Assign ontology concepts to phrases in free text (i.e., entity linking or annotation)

“Systolic blood pressure” can be represented as SNOMED-CT concept 271649006 | Systolic blood pressure (observable entity) |

Event detection

Detect events in free text

“Patient visited the outpatient clinic in January 2020” is an event of type Visit.

Relationship detection

Detect semantic relationships between concepts in free text

The concept Lung cancer in “This patient was diagnosed with recurrent lung cancer” is related to the concept Recurrence.

Text normalization

Transform free text into a single canonical form

“This patient was diagnosed with influenza last year.” becomes “This patient be diagnose with influenza last year.”

Text summarization

Create a short summary of free text and possible restructure the text based on this summary

“Last year, this patient visited the clinic and was diagnosed with diabetes mellitus type 2, and in addition to his diabetes, the patient was also diagnosed with hypertension” becomes “Last year, this patient was diagnosed with diabetes mellitus type 2 and hypertension”.

Classification

Assign categories to free text

A report containing the text “This patient is not diagnosed yet” will be assigned to the category Undiagnosed.

Prediction

Create a predictive model based on free text

Predict the outcome of the APACHE score based on the (free-text) content in a patient chart.

Identification

Identify documents (e.g., reports or patient charts) that match a specific condition based on the contents of the document

Find all patient charts that describe patients with hypertension and a BMI above 30.

Software development

Develop new or build upon existing NLP software

A new algorithm was developed to map ontology concepts to free text in clinical reports.

Software evaluation

Evaluate the effectiveness of NLP software

The mapping algorithm has an F-score of 0.874.