Literature DB >> 26262328

Follow-up Recommendation Detection on Radiology Reports with Incidental Pulmonary Nodules.

Lucas Oliveira1, Ranjith Tellis1, Yuechen Qian1, Karen Trovato1, Gabe Mankovich1.   

Abstract

The management of follow-up recommendations is fundamental for the appropriate care of patients with incidental pulmonary findings. The lack of communication of these important findings can result in important actionable information being lost in healthcare provider electronic documents. This study aims to analyze follow-up recommendations in radiology reports containing pulmonary incidental findings by using Natural Language Processing and Regular Expressions. Our evaluation highlights the different follow-up recommendation rates for oncology and non-oncology patient cohorts. The results reveal the need for a context-sensitive approach to tracking different patient cohorts in an enterprise-wide assessment.

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Mesh:

Year:  2015        PMID: 26262328

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  7 in total

Review 1.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

2.  Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods.

Authors:  Gaurav Trivedi; Charmgil Hong; Esmaeel R Dadashzadeh; Robert M Handzel; Harry Hochheiser; Shyam Visweswaran
Journal:  Int J Med Inform       Date:  2019-06-06       Impact factor: 4.046

3.  Augmented Radiology: Looking Over the Horizon.

Authors:  Christie M Lincoln; Ritodhi Chatterjee; Marc H Willis
Journal:  Radiol Artif Intell       Date:  2019-01-30

4.  Determining Follow-Up Imaging Study Using Radiology Reports.

Authors:  Sandeep Dalal; Vadiraj Hombal; Wei-Hung Weng; Gabe Mankovich; Thusitha Mabotuwana; Christopher S Hall; Joseph Fuller; Bruce E Lehnert; Martin L Gunn
Journal:  J Digit Imaging       Date:  2020-02       Impact factor: 4.056

Review 5.  Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Authors:  Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi
Journal:  Radiol Artif Intell       Date:  2022-02-02

6.  Automated Organ-Level Classification of Free-Text Pathology Reports to Support a Radiology Follow-up Tracking Engine.

Authors:  Jackson M Steinkamp; Charles M Chambers; Darco Lalevic; Hanna M Zafar; Tessa S Cook
Journal:  Radiol Artif Intell       Date:  2019-08-07

Review 7.  The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine.

Authors:  Louise I T Lee; Senthooran Kanthasamy; Radha S Ayyalaraju; Rakesh Ganatra
Journal:  BJR Open       Date:  2019-10-16
  7 in total

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