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Alge OP, Gryak J, VanEpps JS, Najarian K. Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals. Diagnostics (Basel) 2024; 14:234. [PMID: 38337750 PMCID: PMC10854680 DOI: 10.3390/diagnostics14030234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient's quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.
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Affiliation(s)
- Olivia P. Alge
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jonathan Gryak
- Department of Computer Science, Queens College, The City University of New York, Flushing, NY 11367, USA
| | - J. Scott VanEpps
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- The Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- The Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
- Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
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Khodadadi A, Ghanbari Bousejin N, Molaei S, Kumar Chauhan V, Zhu T, Clifton DA. Improving Diagnostics with Deep Forest Applied to Electronic Health Records. SENSORS (BASEL, SWITZERLAND) 2023; 23:6571. [PMID: 37514865 PMCID: PMC10384165 DOI: 10.3390/s23146571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/08/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources' limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations.
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Affiliation(s)
- Atieh Khodadadi
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, 76133 Karlsruhe, Germany
| | | | - Soheila Molaei
- Department of Engineering Science, University of Oxford, Oxford OX1 3AZ, UK; (V.K.C.); (T.Z.); (D.A.C.)
| | - Vinod Kumar Chauhan
- Department of Engineering Science, University of Oxford, Oxford OX1 3AZ, UK; (V.K.C.); (T.Z.); (D.A.C.)
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford OX1 3AZ, UK; (V.K.C.); (T.Z.); (D.A.C.)
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford OX1 3AZ, UK; (V.K.C.); (T.Z.); (D.A.C.)
- Oxford-Suzhou Centre for Advanced Research (OSCAR), Suzhou 215123, China
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Nesaragi N, Sharma A, Patidar S, Acharya UR. Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals. Med Eng Phys 2022; 110:103811. [PMID: 35525698 DOI: 10.1016/j.medengphy.2022.103811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/31/2022] [Accepted: 04/25/2022] [Indexed: 01/18/2023]
Abstract
Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection task. A third-order tensor representing a time-frequency relationship is constructed by fusing scalograms as vertical slices of the tensor. Each scalogram is computed from the considered time frame of a given HR signal. The derived scalogram represents the heterogeneity of data as a two-dimensional map. These two-dimensional maps are stacked one after the other horizontally along the z-axis to form a 3-way tensor for each HR signal. Each two-dimensional map is represented as a vertical slice in the xy - plane. Tensor factorization of such a fused tensor for every HR signal is performed using canonical polyadic (CP) decomposition. Only the core factor is retained later, excluding the three unitary matrices to provide the latent feature set for the detection task. The resultant latent features are then fed to machine learning classifiers for binary classification. Bayesian optimization is performed in a five-fold cross-validation strategy in search of the optimal machine learning classifier. The experimental results yielded the accuracy, sensitivity, and specificity of 96.62%, 96.53%, and 96.67%, respectively, with the bagged trees ensemble method. The proposed tensor decomposition deciphered higher-order interrelations among the considered time-frequency representations of HR signals.
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Affiliation(s)
- Naimahmed Nesaragi
- Dept. of Electronics & Communication Engineering, National Institute of Technology, Goa, India
| | - Ashish Sharma
- Dept. of Electronics & Communication Engineering, National Institute of Technology, Goa, India
| | - Shivnarayan Patidar
- Dept. of Electronics & Communication Engineering, National Institute of Technology, Goa, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Science and Technology, Singapore University of Social Sciences, Singapore.
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Zhang J, Lee D, Jungles K, Shaltis D, Najarian K, Ravikumar R, Sanders G, Gryak J. Prediction of oral food challenge outcomes via ensemble learning. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med 2022; 5:171. [PMID: 36344814 PMCID: PMC9640667 DOI: 10.1038/s41746-022-00712-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
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Affiliation(s)
- Adrienne Kline
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Yikuan Li
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Saya Dennis
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Feixiong Cheng
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, 44195, OH, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA.
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Mathis MR, Engoren MC, Williams AM, Biesterveld BE, Croteau AJ, Cai L, Kim RB, Liu G, Ward KR, Najarian K, Gryak J. Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data. Anesthesiology 2022; 137:586-601. [PMID: 35950802 PMCID: PMC10227693 DOI: 10.1097/aln.0000000000004345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Postoperative hemodynamic deterioration among cardiac surgical patients can indicate or lead to adverse outcomes. Whereas prediction models for such events using electronic health records or physiologic waveform data are previously described, their combined value remains incompletely defined. The authors hypothesized that models incorporating electronic health record and processed waveform signal data (electrocardiogram lead II, pulse plethysmography, arterial catheter tracing) would yield improved performance versus either modality alone. METHODS Intensive care unit data were reviewed after elective adult cardiac surgical procedures at an academic center between 2013 and 2020. Model features included electronic health record features and physiologic waveforms. Tensor decomposition was used for waveform feature reduction. Machine learning-based prediction models included a 2013 to 2017 training set and a 2017 to 2020 temporal holdout test set. The primary outcome was a postoperative deterioration event, defined as a composite of low cardiac index of less than 2.0 ml min-1 m-2, mean arterial pressure of less than 55 mmHg sustained for 120 min or longer, new or escalated inotrope/vasopressor infusion, epinephrine bolus of 1 mg or more, or intensive care unit mortality. Prediction models analyzed data 8 h before events. RESULTS Among 1,555 cases, 185 (12%) experienced 276 deterioration events, most commonly including low cardiac index (7.0% of patients), new inotrope (1.9%), and sustained hypotension (1.4%). The best performing model on the 2013 to 2017 training set yielded a C-statistic of 0.803 (95% CI, 0.799 to 0.807), although performance was substantially lower in the 2017 to 2020 test set (0.709, 0.705 to 0.712). Test set performance of the combined model was greater than corresponding models limited to solely electronic health record features (0.641; 95% CI, 0.637 to 0.646) or waveform features (0.697; 95% CI, 0.693 to 0.701). CONCLUSIONS Clinical deterioration prediction models combining electronic health record data and waveform data were superior to either modality alone, and performance of combined models was primarily driven by waveform data. Decreased performance of prediction models during temporal validation may be explained by data set shift, a core challenge of healthcare prediction modeling. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Michael R Mathis
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan
| | - Milo C Engoren
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, Michigan
| | - Aaron M Williams
- Department of General Surgery, University of Michigan Health System, Ann Arbor, Michigan
| | - Ben E Biesterveld
- Department of General Surgery, University of Michigan Health System, Ann Arbor, Michigan
| | - Alfred J Croteau
- Department of General Surgery, Hartford HealthCare Medical Group, Hartford, Connecticut
| | - Lingrui Cai
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan
| | - Renaid B Kim
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan
| | - Gang Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan
| | - Kevin R Ward
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan; and Department of Emergency Medicine, University of Michigan Health System, Ann Arbor, Michigan
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan
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Kim RB, Alge OP, Liu G, Biesterveld BE, Wakam G, Williams AM, Mathis MR, Najarian K, Gryak J. Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system. Sci Rep 2022; 12:11347. [PMID: 35790802 PMCID: PMC9256604 DOI: 10.1038/s41598-022-15496-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/24/2022] [Indexed: 12/01/2022] Open
Abstract
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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Affiliation(s)
- Renaid B Kim
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Olivia P Alge
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gang Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ben E Biesterveld
- Department of Surgery, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Glenn Wakam
- Department of Surgery, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Aaron M Williams
- Department of Surgery, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Michael R Mathis
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.
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