Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review
In the field of healthcare, as well as many others, textual descriptions of events are logged. With the use of Natural Language Processing (NLP), these texts are used to train event prediction machine learning algorithms. In this review the aim was to assess the state-of-the-art within current liter...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442522000478 |
| _version_ | 1811201493574877184 |
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| author | Oscar Hoekstra William Hurst Joep Tummers |
| author_facet | Oscar Hoekstra William Hurst Joep Tummers |
| author_sort | Oscar Hoekstra |
| collection | DOAJ |
| description | In the field of healthcare, as well as many others, textual descriptions of events are logged. With the use of Natural Language Processing (NLP), these texts are used to train event prediction machine learning algorithms. In this review the aim was to assess the state-of-the-art within current literature concerning prediction of events on textual records. Thus, this study follows a standard Systematic Literature Review (SLR) process. Primary articles are selected from PubMed, IEEE and WebOfScience with a search query, and then exclusion and quality assessment criteria are used to select the articles that are relevant to this study. Published performance metrics for the prediction algorithms used in the studies were then extracted from the included articles and used to assess the different methods. The general-purpose neural network algorithms: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) demonstrate the highest F1-scores amongst all the methods in this review, of 98.5%, 98% and 90.13% respectively. The algorithms that were designed specifically for NLP such as word2vec and BERT also performed well with F1-scores of 88.93% and 91.50%. This review does not give a comparison between methods but gives an indication about which machine learning methods perform well according to the authors of the selected studies. Not enough performance results are published under comparable circumstances to give conclusive results about which methods perform the best. More research needs to be done in comparing algorithms on the same dataset to proof the performance of the methods. |
| first_indexed | 2024-04-12T02:22:34Z |
| format | Article |
| id | doaj.art-a743c57272c44cca954885310120e944 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-04-12T02:22:34Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-a743c57272c44cca954885310120e9442022-12-22T03:52:05ZengElsevierHealthcare Analytics2772-44252022-11-012100107Healthcare related event prediction from textual data with machine learning: A Systematic Literature ReviewOscar Hoekstra0William Hurst1Joep Tummers2Information Technology Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The NetherlandsCorresponding author.; Information Technology Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The NetherlandsInformation Technology Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The NetherlandsIn the field of healthcare, as well as many others, textual descriptions of events are logged. With the use of Natural Language Processing (NLP), these texts are used to train event prediction machine learning algorithms. In this review the aim was to assess the state-of-the-art within current literature concerning prediction of events on textual records. Thus, this study follows a standard Systematic Literature Review (SLR) process. Primary articles are selected from PubMed, IEEE and WebOfScience with a search query, and then exclusion and quality assessment criteria are used to select the articles that are relevant to this study. Published performance metrics for the prediction algorithms used in the studies were then extracted from the included articles and used to assess the different methods. The general-purpose neural network algorithms: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) demonstrate the highest F1-scores amongst all the methods in this review, of 98.5%, 98% and 90.13% respectively. The algorithms that were designed specifically for NLP such as word2vec and BERT also performed well with F1-scores of 88.93% and 91.50%. This review does not give a comparison between methods but gives an indication about which machine learning methods perform well according to the authors of the selected studies. Not enough performance results are published under comparable circumstances to give conclusive results about which methods perform the best. More research needs to be done in comparing algorithms on the same dataset to proof the performance of the methods.http://www.sciencedirect.com/science/article/pii/S2772442522000478Systematic literature reviewEvent predictionNatural Language ProcessingTextual dataMachine LearningHealthcare |
| spellingShingle | Oscar Hoekstra William Hurst Joep Tummers Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review Healthcare Analytics Systematic literature review Event prediction Natural Language Processing Textual data Machine Learning Healthcare |
| title | Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review |
| title_full | Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review |
| title_fullStr | Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review |
| title_full_unstemmed | Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review |
| title_short | Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review |
| title_sort | healthcare related event prediction from textual data with machine learning a systematic literature review |
| topic | Systematic literature review Event prediction Natural Language Processing Textual data Machine Learning Healthcare |
| url | http://www.sciencedirect.com/science/article/pii/S2772442522000478 |
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