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- Klang, MarcusLund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Robotik och Semantiska System,Institutionen för datavetenskap,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,Robotics and Semantic Systems,Department of Computer Science,Faculty of Engineering, LTH (author)
Using Operative Reports to Predict Heart Transplantation Survival
- Article/chapterEnglish2022
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- 2022
- 4 s.
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- LIBRIS-ID:oai:lup.lub.lu.se:77b5b710-130a-4ba7-af1c-ce3588fdea0b
- https://lup.lub.lu.se/record/77b5b710-130a-4ba7-af1c-ce3588fdea0bURI
- https://doi.org/10.1109/EMBC48229.2022.9871788DOI
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- Language:English
- Summary in:English
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- 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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- Medved, DennisLund University,Lunds universitet,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Artificiell intelligens och bioinformatik inom thoraxkirurgisk vetenskap,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups,Artificial Intelligence and Bioinformatics in Cardiothoracic Sciences (AIBCTS)(Swepub:lu)csz-dem (author)
- Nugues, PierreLund University,Lunds universitet,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Robotik och Semantiska System,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups,Robotics and Semantic Systems,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)cs-pnu (author)
- Nilsson, JohanLund University,Lunds universitet,Thoraxkirurgi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Hjärt- och lungtransplantation,Forskargrupper vid Lunds universitet,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Artificiell intelligens och bioinformatik inom thoraxkirurgisk vetenskap,Heparinbindande protein inom thoraxkirurgi,Thoracic Surgery,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Heart and Lung transplantation,Lund University Research Groups,Artificial Intelligence in CardioThoracic Sciences (AICTS),Artificial Intelligence and Bioinformatics in Cardiothoracic Sciences (AIBCTS),Heparin bindning protein in cardiothoracic surgery,Skåne University Hospital(Swepub:lu)thor-jni (author)
- Diaz, DanielLund University (author)
- Institutionen för reglerteknikInstitutioner vid LTH (creator_code:org_t)
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- In:44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 20222022-July, s. 2258-22611557-170X9781728127828
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