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Article ; Online: Encouraging responsible intensive care data sharing.

Thoral, Patrick / Elbers, Paul

2023 Volume 49, Issue 8, Page(s) 1027–1028

Language	English
Publishing date	2023-06-13
Publishing country	United States
Document type	Letter
ZDB-ID	80387-x
ISSN	1432-1238 ; 0340-0964 ; 0342-4642 ; 0935-1701
ISSN (online)	1432-1238
ISSN	0340-0964 ; 0342-4642 ; 0935-1701
DOI	10.1007/s00134-023-07113-9
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Article: The ESICM datathon and the ESICM and ICMx data science strategy.

Elbers, Paul / Thoral, Patrick / Bos, Lieuwe D J / Greco, Massimiliano / Wendel-Garcia, Pedro D / Ercole, Ari

Intensive care medicine experimental

2024 Volume 12, Issue 1, Page(s) 29

Language	English
Publishing date	2024-03-12
Publishing country	Germany
Document type	Editorial
ZDB-ID	2740385-3
ISSN	2197-425X
ISSN	2197-425X
DOI	10.1186/s40635-024-00615-w
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Article ; Online: Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation.

Edinburgh, Tom / Eglen, Stephen J / Thoral, Patrick / Elbers, Paul / Ercole, Ari

GigaByte (Hong Kong, China)

2022 Volume 2022, Page(s) gigabyte45

Abstract: Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ...

Abstract	Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ICU databases are available for observational research and the robust identification of septic patients in such data sets is crucial for research purposes, particularly for comparative studies between critical care sub-populations which may vary around the world. However, data structures are poorly standardised due to inevitable variances in clinical electronic health record system vendor and implementation as well as research database design choices. Robust and well-documented cohort selection (such as patients with sepsis) is crucial for reproducible research. In this work, we operationalise the Sepsis-3 definition on the AmsterdamUMCdb, a recently published large European ICU database, publishing open-access code for wider use by critical care researchers.
Language	English
Publishing date	2022-03-15
Publishing country	China
Document type	Journal Article
ISSN	2709-4715
ISSN (online)	2709-4715
DOI	10.46471/gigabyte.45
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Article ; Online: Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

Siepel, Sander / Dam, Tariq A / Fleuren, Lucas M / Girbes, Armand R J / Hoogendoorn, Mark / Thoral, Patrick J / Elbers, Paul W G / Bennis, Frank C

Journal of intensive care medicine

2023 Volume 38, Issue 7, Page(s) 612–629

Abstract: Background: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal ...

Abstract	Background: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19. Methods: We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked. Results: The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype. Conclusions: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.
MeSH term(s)	Humans ; COVID-19/therapy ; SARS-CoV-2 ; Unsupervised Machine Learning ; Critical Care ; Intensive Care Units ; Inflammation ; Phenotype ; Critical Illness/therapy
Language	English
Publishing date	2023-02-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	632828-3
ISSN	1525-1489 ; 0885-0666
ISSN (online)	1525-1489
ISSN	0885-0666
DOI	10.1177/08850666231153393
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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi.

de Groot, Rowdy / Püttmann, Daniel P / Fleuren, Lucas M / Thoral, Patrick J / Elbers, Paul W G / de Keizer, Nicolette F / Cornet, Ronald

International journal of medical informatics

2023 Volume 178, Page(s) 105200

Abstract: Introduction: Hospitals generate large amounts of data and this data is generally modeled and labeled in a proprietary way, hampering its exchange and integration. Manually annotating data element names to internationally standardized data element ... ...

Abstract	Introduction: Hospitals generate large amounts of data and this data is generally modeled and labeled in a proprietary way, hampering its exchange and integration. Manually annotating data element names to internationally standardized data element identifiers is a time-consuming effort. Tools can support performing this task automatically. This study aimed to determine what factors influence the quality of automatic annotations. Methods: Data element names were used from the Dutch COVID-19 ICU Data Warehouse containing data on intensive care patients with COVID-19 from 25 hospitals in the Netherlands. In this data warehouse, the data had been merged using a proprietary terminology system while also storing the original hospital labels (synonymous names). Usagi, an OHDSI annotation tool, was used to perform the annotation for the data. A gold standard was used to determine if Usagi made correct annotations. Logistic regression was used to determine if the number of characters, number of words, match score (Usagi's certainty) and hospital label origin influenced Usagi's performance to annotate correctly. Results: Usagi automatically annotated 30.5% of the data element names correctly and 5.5% of the synonymous names. The match score is the best predictor for Usagi finding the correct annotation. It was determined that the AUC of data element names was 0.651 and 0.752 for the synonymous names respectively. The AUC for the individual hospital label origins varied between 0.460 to 0.905. Discussion: The results show that Usagi performed better to annotate the data element names than the synonymous names. The hospital origin in the synonymous names dataset was associated with the amount of correctly annotated concepts. Hospitals that performed better had shorter synonymous names and fewer words. Using shorter data element names or synonymous names should be considered to optimize the automatic annotating process. Overall, the performance of Usagi is too poor to completely rely on for automatic annotation.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Netherlands
Language	English
Publishing date	2023-08-29
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2023.105200
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Book ; Online: Out-of-Distribution Detection for Medical Applications

Zadorozhny, Karina / Thoral, Patrick / Elbers, Paul / Cinà, Giovanni

Guidelines for Practical Evaluation

2021

Abstract: Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of uncertainty quantification techniques, there is a lack of evaluation ... ...

Abstract	Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of uncertainty quantification techniques, there is a lack of evaluation guidelines on how to select OOD detection methods in practice. This gap impedes implementation of OOD detection methods for real-world applications. Here, we propose a series of practical considerations and tests to choose the best OOD detector for a specific medical dataset. These guidelines are illustrated on a real-life use case of Electronic Health Records (EHR). Our results can serve as a guide for implementation of OOD detection methods in clinical practice, mitigating risks associated with the use of machine learning models in healthcare.
Keywords	Computer Science - Machine Learning
Publishing date	2021-09-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Yet Another ICU Benchmark

van de Water, Robin / Schmidt, Hendrik / Elbers, Paul / Thoral, Patrick / Arnrich, Bert / Rockenschaub, Patrick

A Flexible Multi-Center Framework for Clinical ML

2023

Abstract: Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of available data from electronic health records. Models have been ... ...

Abstract	Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of available data from electronic health records. Models have been proposed to address numerous ICU prediction tasks like the early detection of complications. While authors frequently report state-of-the-art performance, it is challenging to verify claims of superiority. Datasets and code are not always published, and cohort definitions, preprocessing pipelines, and training setups are difficult to reproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular framework that allows researchers to define reproducible and comparable clinical ML experiments; we offer an end-to-end solution from cohort definition to model evaluation. The framework natively supports most open-access ICU datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future ICU datasets. Combined with a transparent preprocessing pipeline and extensible training code for multiple ML and deep learning models, YAIB enables unified model development. Our benchmark comes with five predefined established prediction tasks (mortality, acute kidney injury, sepsis, kidney function, and length of stay) developed in collaboration with clinicians. Adding further tasks is straightforward by design. Using YAIB, we demonstrate that the choice of dataset, cohort definition, and preprocessing have a major impact on the prediction performance - often more so than model class - indicating an urgent need for YAIB as a holistic benchmarking tool. We provide our work to the clinical ML community to accelerate method development and enable real-world clinical implementations. Software Repository: https://github.com/rvandewater/YAIB. Comment: Main benchmark: https://github.com/rvandewater/YAIB, Cohort generation: https://github.com/rvandewater/YAIB-cohorts, Models: https://github.com/rvandewater/YAIB-models
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-06-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning in intensive care medicine: ready for take-off?

Fleuren, Lucas M / Thoral, Patrick / Shillan, Duncan / Ercole, Ari / Elbers, Paul W G

Intensive care medicine

2020 Volume 46, Issue 7, Page(s) 1486–1488

MeSH term(s)	Critical Care ; Humans ; Intensive Care Units ; Machine Learning ; Medicine
Language	English
Publishing date	2020-05-12
Publishing country	United States
Document type	Letter
ZDB-ID	80387-x
ISSN	1432-1238 ; 0340-0964 ; 0342-4642 ; 0935-1701
ISSN (online)	1432-1238
ISSN	0340-0964 ; 0342-4642 ; 0935-1701
DOI	10.1007/s00134-020-06045-y
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Jg. 1995 - 2021: Lesesall (1.OG)
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Article ; Online: Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study.

van der Meijden, Siri L / de Hond, Anne A H / Thoral, Patrick J / Steyerberg, Ewout W / Kant, Ilse M J / Cinà, Giovanni / Arbous, M Sesmu

JMIR human factors

2023 Volume 10, Page(s) e39114

Abstract: Background: Artificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools.: Objective!# ...

Abstract	Background: Artificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools. Objective: We aimed to investigate physicians' perspectives and their current decision-making behavior before implementing a discharge AI-CDS tool for predicting readmission and mortality risk after ICU discharge. Methods: We conducted a survey of physicians involved in decision-making on discharge of patients at two Dutch academic ICUs between July and November 2021. Questions were divided into four domains: (1) physicians' current decision-making behavior with respect to discharging ICU patients, (2) perspectives on the use of AI-CDS tools in general, (3) willingness to incorporate a discharge AI-CDS tool into daily clinical practice, and (4) preferences for using a discharge AI-CDS tool in daily workflows. Results: Most of the 64 respondents (of 93 contacted, 69%) were familiar with AI (62/64, 97%) and had positive expectations of AI, with 55 of 64 (86%) believing that AI could support them in their work as a physician. The respondents disagreed on whether the decision to discharge a patient was complex (23/64, 36% agreed and 22/64, 34% disagreed); nonetheless, most (59/64, 92%) agreed that a discharge AI-CDS tool could be of value. Significant differences were observed between physicians from the 2 academic sites, which may be related to different levels of involvement in the development of the discharge AI-CDS tool. Conclusions: ICU physicians showed a favorable attitude toward the integration of AI-CDS tools into the ICU setting in general, and in particular toward a tool to predict a patient's risk of readmission and mortality within 7 days after discharge. The findings of this questionnaire will be used to improve the implementation process and training of end users.
Language	English
Publishing date	2023-01-05
Publishing country	Canada
Document type	Journal Article
ISSN	2292-9495
ISSN (online)	2292-9495
DOI	10.2196/39114
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Article: Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis.

Bologheanu, Razvan / Kapral, Lorenz / Laxar, Daniel / Maleczek, Mathias / Dibiasi, Christoph / Zeiner, Sebastian / Agibetov, Asan / Ercole, Ari / Thoral, Patrick / Elbers, Paul / Heitzinger, Clemens / Kimberger, Oliver

Journal of clinical medicine

2023 Volume 12, Issue 4

Abstract: Background: The optimal indication, dose, and timing of corticosteroids in sepsis is controversial. Here, we used reinforcement learning to derive the optimal steroid policy in septic patients based on data on 3051 ICU admissions from the AmsterdamUMCdb ...

Abstract	Background: The optimal indication, dose, and timing of corticosteroids in sepsis is controversial. Here, we used reinforcement learning to derive the optimal steroid policy in septic patients based on data on 3051 ICU admissions from the AmsterdamUMCdb intensive care database. Methods: We identified septic patients according to the 2016 consensus definition. An actor-critic RL algorithm using ICU mortality as a reward signal was developed to determine the optimal treatment policy from time-series data on 277 clinical parameters. We performed off-policy evaluation and testing in independent subsets to assess the algorithm's performance. Results: Agreement between the RL agent's policy and the actual documented treatment reached 59%. Our RL agent's treatment policy was more restrictive compared to the actual clinician behavior: our algorithm suggested withholding corticosteroids in 62% of the patient states, versus 52% according to the physicians' policy. The 95% lower bound of the expected reward was higher for the RL agent than clinicians' historical decisions. ICU mortality after concordant action in the testing dataset was lower both when corticosteroids had been withheld and when corticosteroids had been prescribed by the virtual agent. The most relevant variables were vital parameters and laboratory values, such as blood pressure, heart rate, leucocyte count, and glycemia. Conclusions: Individualized use of corticosteroids in sepsis may result in a mortality benefit, but optimal treatment policy may be more restrictive than the routine clinical practice. Whilst external validation is needed, our study motivates a 'precision-medicine' approach to future prospective controlled trials and practice.
Language	English
Publishing date	2023-02-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm12041513
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