Cargando…
Modern Learning from Big Data in Critical Care: Primum Non Nocere
Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identifi...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071245/ https://www.ncbi.nlm.nih.gov/pubmed/35513752 http://dx.doi.org/10.1007/s12028-022-01510-6 |
_version_ | 1784700810874060800 |
---|---|
author | Gravesteijn, Benjamin Y. Steyerberg, Ewout W. Lingsma, Hester F. |
author_facet | Gravesteijn, Benjamin Y. Steyerberg, Ewout W. Lingsma, Hester F. |
author_sort | Gravesteijn, Benjamin Y. |
collection | PubMed |
description | Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12028-022-01510-6. |
format | Online Article Text |
id | pubmed-9071245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-90712452022-05-06 Modern Learning from Big Data in Critical Care: Primum Non Nocere Gravesteijn, Benjamin Y. Steyerberg, Ewout W. Lingsma, Hester F. Neurocrit Care Big Data in Neurocritical Care Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12028-022-01510-6. Springer US 2022-05-05 2022 /pmc/articles/PMC9071245/ /pubmed/35513752 http://dx.doi.org/10.1007/s12028-022-01510-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Big Data in Neurocritical Care Gravesteijn, Benjamin Y. Steyerberg, Ewout W. Lingsma, Hester F. Modern Learning from Big Data in Critical Care: Primum Non Nocere |
title | Modern Learning from Big Data in Critical Care: Primum Non Nocere |
title_full | Modern Learning from Big Data in Critical Care: Primum Non Nocere |
title_fullStr | Modern Learning from Big Data in Critical Care: Primum Non Nocere |
title_full_unstemmed | Modern Learning from Big Data in Critical Care: Primum Non Nocere |
title_short | Modern Learning from Big Data in Critical Care: Primum Non Nocere |
title_sort | modern learning from big data in critical care: primum non nocere |
topic | Big Data in Neurocritical Care |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071245/ https://www.ncbi.nlm.nih.gov/pubmed/35513752 http://dx.doi.org/10.1007/s12028-022-01510-6 |
work_keys_str_mv | AT gravesteijnbenjaminy modernlearningfrombigdataincriticalcareprimumnonnocere AT steyerbergewoutw modernlearningfrombigdataincriticalcareprimumnonnocere AT lingsmahesterf modernlearningfrombigdataincriticalcareprimumnonnocere |