Carsten Eickhoff
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Explore the profile of Carsten Eickhoff including associated specialties, affiliations and a list of published articles.
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40
Citations
352
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Recent Articles
1.
Berman E, Sundberg Malek H, Bitzer M, Malek N, Eickhoff C
J Med Internet Res
. 2025 Mar;
27:e64364.
PMID: 40053768
Background: Molecular tumor boards (MTBs) require intensive manual investigation to generate optimal treatment recommendations for patients. Large language models (LLMs) can catalyze MTB recommendations, decrease human error, improve accessibility to...
2.
Eisinger F, Holderried F, Mahling M, Stegemann-Philipps C, Herrmann-Werner A, Nazarenus E, et al.
J Med Internet Res
. 2025 Jan;
27:e67143.
PMID: 39836954
Background: For hospitalized patients, the discharge letter serves as a crucial source of medical information, outlining important discharge instructions and health management tasks. However, these letters are often written in...
3.
Golovanevsky M, Schiller E, Nair A, Han E, Singh R, Eickhoff C
Pac Symp Biocomput
. 2024 Dec;
30:580-593.
PMID: 39670397
Multimodal models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to disease diagnosis. Despite the importance of multimodal learning, existing efforts focus on...
4.
Kalra A, Bachina P, Shou B, Hwang J, Barshay M, Kulkarni S, et al.
JTCVS Open
. 2024 Nov;
21:140-167.
PMID: 39534333
Background: Venovenous extracorporeal membrane oxygenation (VV-ECMO) is associated with acute brain injury (ABI), including central nervous system (CNS) ischemia (defined as ischemic stroke or hypoxic-ischemic brain injury [HIBI]) and intracranial...
5.
Sears I, Garcia-Agundez A, Zerveas G, Rudman W, Mercurio L, Ventetuolo C, et al.
Comput Cardiol (2010)
. 2024 Nov;
50.
PMID: 39526226
In response to the 2023 George B. Moody PhysioNet Challenge, we propose an automated, unsupervised pre-training approach to boost the performance of models that predict neurologic outcomes after cardiac arrest....
6.
Mercurio L, Garcia A, Ruest S, Duffy S, Eickhoff C
Subst Use Addctn J
. 2024 Nov;
46(2):328-336.
PMID: 39511779
Background/significance: Alcohol use carries significant morbidity and mortality, yet accurate identification of alcohol use disorder (AUD) remains a multi-layered problem for both researchers and clinicians. Objective: To fine-tune a language...
7.
Hinrichs N, Meyer A, Koehler K, Kaas T, Hiddemann M, Spethmann S, et al.
Front Cardiovasc Med
. 2024 Oct;
11:1457995.
PMID: 39371396
Background: Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier...
8.
Kalra A, Bachina P, Shou B, Hwang J, Barshay M, Kulkarni S, et al.
JTCVS Open
. 2024 Sep;
20:64-88.
PMID: 39296456
Objective: We aimed to determine if machine learning can predict acute brain injury and to identify modifiable risk factors for acute brain injury in patients receiving venoarterial extracorporeal membrane oxygenation....
9.
Hinrichs N, Roeschl T, Lanmueller P, Balzer F, Eickhoff C, OBrien B, et al.
PLOS Digit Health
. 2024 Sep;
3(9):e0000598.
PMID: 39264979
Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ...
10.
Holderried F, Stegemann-Philipps C, Herrmann-Werner A, Festl-Wietek T, Holderried M, Eickhoff C, et al.
JMIR Med Educ
. 2024 Aug;
10:e59213.
PMID: 39150749
Background: Although history taking is fundamental for diagnosing medical conditions, teaching and providing feedback on the skill can be challenging due to resource constraints. Virtual simulated patients and web-based chatbots...