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Using Operative Reports to Predict Heart Transplantation Survival

Klang, Marcus LU orcid ; Medved, Dennis LU orcid ; Nugues, Pierre LU orcid ; Nilsson, Johan LU orcid and Diaz, Daniel (2022) 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2022-July. p.2258-2261
Abstract

Heart transplantation is a difficult procedure compared with other surgical operations, with a greater outcome uncertainty such as late rejection and death. We can model the success of heart transplants from predicting factors such as the age, sex, diagnosis, etc., of the donor and recipient. Although predictions can mitigate the uncertainty on the transplantation outcome, their accuracy is far from perfect. In this paper, we describe a new method to predict the outcome of a transplantation from textual operative reports instead of traditional tabular data. We carried out an experiment on 300 surgical reports to determine the survival rates at one year and five years. Using a truncated TF-IDF vectorization of the texts and logistic... (More)

Heart transplantation is a difficult procedure compared with other surgical operations, with a greater outcome uncertainty such as late rejection and death. We can model the success of heart transplants from predicting factors such as the age, sex, diagnosis, etc., of the donor and recipient. Although predictions can mitigate the uncertainty on the transplantation outcome, their accuracy is far from perfect. In this paper, we describe a new method to predict the outcome of a transplantation from textual operative reports instead of traditional tabular data. We carried out an experiment on 300 surgical reports to determine the survival rates at one year and five years. Using a truncated TF-IDF vectorization of the texts and logistic regression, we could reach a macro Fl of 59.1 %, respectively, 54.9% with a five-fold cross validation. While the size of the corpus is relatively small, our experiments show that the operative textual sources can discriminate the transplantation outcomes and could be a valuable additional input to existing prediction systems. Clinical Relevance- Heart transplantation involves a significant number of written reports including in the preoperative examinations and operative documentation. In this paper, we show that these written reports can predict the outcome of the transplantation at one and five years with macro 1s of 59.1 % and 54.9 %, respectively and complement existing prediction methods.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
series title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
volume
2022-July
pages
4 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
conference location
Glasgow, United Kingdom
conference dates
2022-07-11 - 2022-07-15
external identifiers
  • pmid:36086591
  • scopus:85138128392
ISSN
1557-170X
ISBN
9781728127828
DOI
10.1109/EMBC48229.2022.9871788
language
English
LU publication?
yes
additional info
Funding Information: *This work was partially supported by the Heart Lung Foundation, registration number 2019-0623 and Vetenskaprådet, the Swedish Research Council, registration number 2021-04533. Publisher Copyright: © 2022 IEEE.
id
77b5b710-130a-4ba7-af1c-ce3588fdea0b
date added to LUP
2022-12-29 13:19:02
date last changed
2025-02-07 23:13:53
@inproceedings{77b5b710-130a-4ba7-af1c-ce3588fdea0b,
  abstract     = {{<p>Heart transplantation is a difficult procedure compared with other surgical operations, with a greater outcome uncertainty such as late rejection and death. We can model the success of heart transplants from predicting factors such as the age, sex, diagnosis, etc., of the donor and recipient. Although predictions can mitigate the uncertainty on the transplantation outcome, their accuracy is far from perfect. In this paper, we describe a new method to predict the outcome of a transplantation from textual operative reports instead of traditional tabular data. We carried out an experiment on 300 surgical reports to determine the survival rates at one year and five years. Using a truncated TF-IDF vectorization of the texts and logistic regression, we could reach a macro Fl of 59.1 %, respectively, 54.9% with a five-fold cross validation. While the size of the corpus is relatively small, our experiments show that the operative textual sources can discriminate the transplantation outcomes and could be a valuable additional input to existing prediction systems. Clinical Relevance- Heart transplantation involves a significant number of written reports including in the preoperative examinations and operative documentation. In this paper, we show that these written reports can predict the outcome of the transplantation at one and five years with macro 1s of 59.1 % and 54.9 %, respectively and complement existing prediction methods.</p>}},
  author       = {{Klang, Marcus and Medved, Dennis and Nugues, Pierre and Nilsson, Johan and Diaz, Daniel}},
  booktitle    = {{44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022}},
  isbn         = {{9781728127828}},
  issn         = {{1557-170X}},
  language     = {{eng}},
  pages        = {{2258--2261}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}},
  title        = {{Using Operative Reports to Predict Heart Transplantation Survival}},
  url          = {{http://dx.doi.org/10.1109/EMBC48229.2022.9871788}},
  doi          = {{10.1109/EMBC48229.2022.9871788}},
  volume       = {{2022-July}},
  year         = {{2022}},
}