Calculate patients score for each abstract to indicate possible use of patient material.
calculate_score_patients(
df,
keywords = patients_keywords,
case = FALSE,
threshold = NULL,
indicate = FALSE,
discard = FALSE,
col.abstract = Abstract
)
Data frame containing abstracts.
Character vector. Vector containing keywords. The score is
calculated based on these keywords. How much weight a keyword in keywords
carries is determined by how often it is present in keywords
, e.g. if
a keyword is mentioned twice in keywords
and it is mentioned only once in
an abstract, it adds 2 points to the score.
The predefined keywords can be accessed via miRetrieve::patients_keywords
.
Boolean. If case = TRUE
, terms contained in keywords
are case
sensitive. If case = FALSE
, terms contained in keywords
are case insensitive.
Integer. Optional. Threshold to decide if use of patient tissue is
present in an abstract or not. If indicate = TRUE
or discard = TRUE
and threshold
not specified, threshold
is automatically set to 1
.
Boolean. If indicate = TRUE
, an extra column is added. This
extra column contains "Yes" or "No", indicating the use of patient tissue
in abstracts.
Boolean. If discard = TRUE
, only abstracts are kept where
use of patient tissue is present.
Symbol. Column containing abstracts.
Data frame with calculated patient scores.
If discard = FALSE
, adds extra columns
to the original data frame with the calculated patient tissue scores.
If discard = TRUE
, only abstracts with use of patient tissue
are kept.
Calculate patient score for each abstract to indicate possible
use of patient material. This score is added to the data frame as an additional
column Patient_score
, containing the calculated patients score.
To decide which abstracts are considered to contain patient material, a threshold
can be set via the threshold
argument. Furthermore, an additional
column can be added, verbally indicating the general use of patient material.
Choosing the right threshold can be facilitated using plot_score_patients()
.
Other score functions:
assign_topic()
,
calculate_score_animals()
,
calculate_score_biomarker()
,
calculate_score_topic()
,
plot_score_animals()
,
plot_score_biomarker()
,
plot_score_patients()
,
plot_score_topic()