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Table 3 System, module, and database characteristics of the included articles

From: Extracting cancer concepts from clinical notes using natural language processing: a systematic review

Author

System

Module

Database

Hammami et al. [36]

Pathology reports

Oracle Data Warehouse of Fondazione IRCCS “Institute nazionale dei tumori” (Istituto Nazionale dei Tumori)

Ryu et al. [37]

EHR

Pathology reports

Oliveira et al. [41]

Cervical and anal pathology reports

Clinical pathology laboratory information system

Becker et al. [48]

EHR

Clinical note

Wang et al. [40]

Mayo clinic EHR

Clinical notes and pathology reports

Kumar et al. [33]

Pathology reports

The Dartmouth-

   

Hitchcock Medical Center (DHMC)

Wadia et al. [43]

Radiology reports (Ct)

Clinical text analysis

  

Chest CT reports

 

Bustos et al. [38]

Public registry

Free dataset

Faina Linkov et al.[47]

UPMC registry

Sada et al. [34]

Liver pathology reports, abdominal CT, and abdominal MRI reports

Veterans Affairs administrative data

Nguyen et al. [44]

  

Hoogendoorna et al. [45]

Primary care dataset originating from a network of general practitioners (GPs) centered around the Utrecht University Medical Center

Löpprich et al. [39]

Clinical report

Multiple myeloma research database

Mehrabi et al. [32]

Indiana University (IU) dataset

   

Mayo Clinic dataset

Sippo et al. [42]

EMR

Breast imaging reports

Segagni et al. [31]

HIS and biobank

FSM pathology unit hospital biobank

FSM pathology unit database

Strauss et al. [46]

EMR

Pathology report

  1. The Algorithm performance characteristics of the included articles are shown in Table 3. -: Indicates that the information are not reported in the included studies