Editorial

. 2020 May 26;24(1):265.

doi: 10.1186/s13054-020-02962-y.

How machine learning could be used in clinical practice during an epidemic

Charles Verdonk^{1

2}, Franck Verdonk^{3

4}, Gérard Dreyfus⁵

Affiliations

¹ Department of Neurosciences and Cognitive Sciences, Unit of Neurophysiology of Stress, French Armed Forces Biomedical Research Institute, 91220, Brétigny-sur-Orge, France. verdonk.charles@gmail.com.
² ESPCI Paris - PSL University, 75005, Paris, France. verdonk.charles@gmail.com.
³ Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
⁴ Department of Anaesthesiology and Intensive Care, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris, 75012, Paris, France.
⁵ ESPCI Paris - PSL University, 75005, Paris, France.

PMID: 32456690
PMCID: PMC7250254
DOI: 10.1186/s13054-020-02962-y

Editorial

How machine learning could be used in clinical practice during an epidemic

Charles Verdonk et al. Crit Care. 2020.

. 2020 May 26;24(1):265.

doi: 10.1186/s13054-020-02962-y.

Authors

Charles Verdonk^{1

2}, Franck Verdonk^{3

4}, Gérard Dreyfus⁵

Affiliations

¹ Department of Neurosciences and Cognitive Sciences, Unit of Neurophysiology of Stress, French Armed Forces Biomedical Research Institute, 91220, Brétigny-sur-Orge, France. verdonk.charles@gmail.com.
² ESPCI Paris - PSL University, 75005, Paris, France. verdonk.charles@gmail.com.
³ Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
⁴ Department of Anaesthesiology and Intensive Care, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris, 75012, Paris, France.
⁵ ESPCI Paris - PSL University, 75005, Paris, France.

PMID: 32456690
PMCID: PMC7250254
DOI: 10.1186/s13054-020-02962-y

No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

**Fig. 1**
Machine learning-based decision support systems can help clinical practice during an epidemic. Efficient diagnostic and accurate prediction of patient outcomes can ultimately lead to effective medical resource management. In contrast to traditional approaches, machine learning algorithms enable feature selection and design of non-linear models that improve prediction of clinical outcomes, and on-line training techniques allow upgrading of decision support systems, as the data regarding the epidemic increases

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How machine learning could be used in clinical practice during an epidemic

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How machine learning could be used in clinical practice during an epidemic

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Conflict of interest statement

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