Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care

Hernandez, Larry; Kim, Renaid B.; Tokcan, Neriman; Derksen, Harm; Biesterveld, Ben E.; Croteau, Alfred; Williams, Aaron M.; Mathis, Michael R.; Najarian, Kayvan; Gryak, Jonathan

doi:10.1016/j.artmed.2021.102032

Cited by 17 publications

(32 citation statements)

References 38 publications

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“…This section details the creation of the predictive models for hemodynamic decompensation, which builds upon our previous work 14 – 16 . A graphical schematic of the method is presented in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, we include two additional adverse events that may not necessarily results from hemodynamic decompensation but are clinically significant enough to warrant detection by an algorithm: re-intubation and mortality, as an algorithm missing these two events while detecting other decompensation events would be clinically less meaningful. The exact definitions, details, and rationale for inclusion of these events as agreed by our clinical team are available in Hernandez et al 14 .…”

Section: Methodsmentioning

confidence: 99%

“…We identified peaks within the filtered ECG signal using the peak detection method defined in Hernandez et al 14 . We compute the difference in time between subsequent peaks to produce HRV.…”

Section: Methodsmentioning

confidence: 99%

“…Taut String (TS) 17 has previously been used to capture hemodynamic instability in ECG 13 , 14 , and we utilize these same TS features in our method with varying values for .…”

Section: Methodsmentioning

confidence: 99%

“…Belle et al 13 showed that HRV and electrocardiogram (ECG) features could better predict hemodynamic stability compared to standard biomarkers used in hemodynamic assessment. Hernandez et al 14 took a multimodal approach, using ECG, HRV, arterial blood pressure (ABP) waveform from an arterial line, pulse plethysmography (PPG) waveform from a pulse oximeter, and EHR data to predict hemodynamic decompensation.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system

Kim

Alge

Liu

et al. 2022

Sci Rep

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Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for early and timely interventions that can significantly improve patient outcomes. We utilize multimodal features derived from digital signal processing techniques and tensor formation, as well as the electronic health record (EHR), to create machine learning models that predict the occurrence of several life-threatening complications up to 4 hours prior to the event. In order to ensure that our models are generalizable across different surgical cohorts, we trained the models on a cardiac surgery cohort and tested them on vascular and non-cardiac acute surgery cohorts. The best performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.94 on training and 0.94 and 0.82, respectively, on testing for the 0.5-hour interval. The AUROCs only slightly dropped to 0.93, 0.92, and 0.77, respectively, for the 4-hour interval. This study serves as a proof-of-concept that EHR data and physiologic waveform data can be combined to enable the early detection of postoperative deterioration events.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Taut String (TS) 17 has previously been used to capture hemodynamic instability in ECG 13 , 14 , and we utilize these same TS features in our method with varying values for .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system

Kim

Alge

Liu

et al. 2022

Sci Rep

Self Cite

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Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare

Wang

Feng

et al. 2023

MedComm – Future Medicine

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Large-scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real-world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be changed because of advancements in graphics processing unit (GPU) programming and parallel computing. More importantly, leveraging existing large-scale AIs such as GPT-4 and Med-PaLM and integrating them into multiagent models (e.g., Visual-ChatGPT) will facilitate real-world implementations. This review aims to raise awareness of the potential applications of these models in healthcare. We provide a general overview of several advanced large-scale AI models, including language models, vision-language models, graph learning models, language-conditioned multiagent models, and multimodal embodied models. We discuss their potential medical applications in addition to the challenges and future directions. Importantly, we stress the need to align these models with human values and goals, such as using reinforcement learning from human feedback, to ensure that they provide accurate and personalized insights that support human decisionmaking and improve healthcare outcomes.

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Harnessing Multi-modality and Expert Knowledge for Adverse Events Prediction in Clinical Notes

Postiglione,

Esposito,

Izzo

et al. 2024

Lecture Notes in Computer Science

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Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care

Cited by 17 publications

References 38 publications

Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system

Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system

Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare

Harnessing Multi-modality and Expert Knowledge for Adverse Events Prediction in Clinical Notes

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