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Wang Y, Stroh JN, Hripcsak G, Low Wang CC, Bennett TD, Wrobel J, Der Nigoghossian C, Mueller SW, Claassen J, Albers DJ. A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation. J Biomed Inform 2023; 148:104547. [PMID: 37984547 PMCID: PMC10802138 DOI: 10.1016/j.jbi.2023.104547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVE Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.
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Affiliation(s)
- Yanran Wang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, 3rd Floor, Mail Stop B119, Aurora, CO 80045, United States of America; Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America.
| | - J N Stroh
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America; Department of Biomedical Engineering, University of Colorado, 12705 East Montview Boulevard, Suite 100, Aurora, CO 80045, United States of America
| | - George Hripcsak
- Biomedical Informatics, Columbia University, 622 W. 168th Street, PH20, New York, NY 10032, United States of America
| | - Cecilia C Low Wang
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado School of Medicine, 12801 East 17th Avenue, 7103, Aurora, CO 80045, United States of America
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd, NE Atlanta, GA 30322, United States of America
| | - Caroline Der Nigoghossian
- Columbia University School of Nursing, 560 West 168th Street, New York, NY 10032, United States of America
| | - Scott W Mueller
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 East Montview Boulevard, Aurora, CO 80045, United States of America
| | - Jan Claassen
- The Neurological Institute of New York, Columbia University Irving Medical Center, 710 West 168th Street, New York NY 10032, United States of America
| | - D J Albers
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, 3rd Floor, Mail Stop B119, Aurora, CO 80045, United States of America; Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America; Department of Biomedical Engineering, University of Colorado, 12705 East Montview Boulevard, Suite 100, Aurora, CO 80045, United States of America; Biomedical Informatics, Columbia University, 622 W. 168th Street, PH20, New York, NY 10032, United States of America
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Albers D, Sirlanci M, Levine M, Claassen J, Nigoghossian CD, Hripcsak G. Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data. J Biomed Inform 2023; 145:104477. [PMID: 37604272 DOI: 10.1016/j.jbi.2023.104477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 08/08/2023] [Accepted: 08/18/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE Prediction of physiological mechanics are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because medicine is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes. Computational methods can increase prediction accuracy, but prediction with clinical data is difficult because the data are sparse, noisy and nonstationary. This paper focuses on predicting physiological processes given sparse, non-stationary, electronic health record data in the intensive care unit using data assimilation (DA), a broad collection of methods that pair mechanistic models with inference methods. METHODS A methodological pipeline embedding a glucose-insulin model into a new DA framework, the constrained ensemble Kalman filter (CEnKF) to forecast blood glucose was developed. The data include tube-fed patients whose nutrition, blood glucose, administered insulins and medications were extracted by hand due to their complexity and to ensure accuracy. The model was estimated using an individual's data as if they arrived in real-time, and the estimated model was run forward producing a forecast. Both constrained and unconstrained ensemble Kalman filters were estimated to compare the impact of constraints. Constraint boundaries, model parameter sets estimated, and data used to estimate the models were varied to investigate their influence on forecasting accuracy. Forecasting accuracy was evaluated according to mean squared error between the model-forecasted glucose and the measurements and by comparing distributions of measured glucose and forecast ensemble means. RESULTS The novel CEnKF produced substantial gains in robustness and accuracy while minimizing the data requirements compared to the unconstrained ensemble Kalman filters. Administered insulin and tube-nutrition were important for accurate forecasting, but including glucose in IV medication delivery did not increase forecast accuracy. Model flexibility, controlled by constraint boundaries and estimated parameters, did influence forecasting accuracy. CONCLUSION Accurate and robust physiological forecasting with sparse clinical data is possible with DA. Introducing constrained inference, particularly on unmeasured states and parameters, reduced forecast error and data requirements. The results are not particularly sensitive to model flexibility such as constraint boundaries, but over or under constraining increased forecasting errors.
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Affiliation(s)
- David Albers
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA; Department of Biomedical Engineering, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA; Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA; Department of Biomedical Informatics, Columbia University, New York, 10032, NY, USA.
| | - Melike Sirlanci
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA
| | - Matthew Levine
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, 91125, CA, USA
| | - Jan Claassen
- Division of Critical Care Neurology, Department of Neurology, Columbia University, New York, 10032, NY, USA
| | | | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, 10032, NY, USA
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Wang Y, Stroh JN, Hripcsak G, Low Wang CC, Bennett TD, Wrobel J, Der Nigoghossian C, Mueller S, Claassen J, Albers DJ. A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.15.23287315. [PMID: 37662404 PMCID: PMC10473766 DOI: 10.1101/2023.03.15.23287315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Objective Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.
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Sirlanci M, Levine ME, Low Wang CC, Albers DJ, Stuart AM. A simple modeling framework for prediction in the human glucose-insulin system. CHAOS (WOODBURY, N.Y.) 2023; 33:073150. [PMID: 37486667 PMCID: PMC10368459 DOI: 10.1063/5.0146808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/31/2023] [Indexed: 07/25/2023]
Abstract
Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model's predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.
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Affiliation(s)
- Melike Sirlanci
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125, USA
| | - Matthew E Levine
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125, USA
| | - Cecilia C Low Wang
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, USA
| | - David J Albers
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, USA
| | - Andrew M Stuart
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125, USA
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Zhou Y, Shi J, Stein R, Liu X, Baldassano RN, Forrest CB, Chen Y, Huang J. Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research. J Am Med Inform Assoc 2023; 30:1246-1256. [PMID: 37337922 PMCID: PMC10280351 DOI: 10.1093/jamia/ocad066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/20/2023] [Accepted: 04/08/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVES The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods. MATERIALS AND METHODS We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data. RESULTS When missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression. DISCUSSION AND CONCLUSION Missing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.
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Affiliation(s)
- Yizhao Zhou
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jiasheng Shi
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Ronen Stein
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Xiaokang Liu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert N Baldassano
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christopher B Forrest
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jing Huang
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Karamched BR, Hripcsak G, Leibel RL, Albers D, Ott W. Delay-induced uncertainty in the glucose-insulin system: Pathogenicity for obesity and type-2 diabetes mellitus. Front Physiol 2022; 13:936101. [PMID: 36117719 PMCID: PMC9476552 DOI: 10.3389/fphys.2022.936101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
We have recently shown that physiological delay can induce a novel form of sustained temporal chaos we call delay-induced uncertainty (DIU) (Karamched et al. (Chaos, 2021, 31, 023142)). This paper assesses the impact of DIU on the ability of the glucose-insulin system to maintain homeostasis when responding to the ingestion of meals. We address two questions. First, what is the nature of the DIU phenotype? That is, what physiological macrostates (as encoded by physiological parameters) allow for DIU onset? Second, how does DIU impact health? We find that the DIU phenotype is abundant in the space of intrinsic parameters for the Ultradian glucose-insulin model—a model that has been successfully used to predict glucose-insulin dynamics in humans. Configurations of intrinsic parameters that correspond to high characteristic glucose levels facilitate DIU onset. We argue that DIU is pathogenic for obesity and type-2 diabetes mellitus by linking the statistical profile of DIU to the glucostatic theory of hunger.
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Affiliation(s)
- Bhargav R. Karamched
- Department of Mathematics, Florida State University, Tallahassee, FL, United States
- Institute of Molecular Biophysics, Florida State University, Tallahassee, FL, United States
- Program in Neuroscience, Florida State University, Tallahassee, FL, United States
- *Correspondence: William Ott, ; Bhargav R. Karamched,
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Rudolph L. Leibel
- Division of Molecular Genetics, Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, NY, NY, United States
- Naomi Berrie Diabetes Center, Columbia University Irving Medical Center, NY, NY, United States
| | - David Albers
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
- Section of Informatics and Data Science, Department of Pediatrics, Department of Biomedical Engineering, and Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - William Ott
- Department of Mathematics, University of Houston, Houston, TX, United States
- *Correspondence: William Ott, ; Bhargav R. Karamched,
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Alhaddad AY, Aly H, Gad H, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection. Front Bioeng Biotechnol 2022; 10:876672. [PMID: 35646863 PMCID: PMC9135106 DOI: 10.3389/fbioe.2022.876672] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | - Hoda Gad
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Rayaz A. Malik
- Weill Cornell Medicine - Qatar, Doha, Qatar
- *Correspondence: Rayaz A. Malik,
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Hripcsak G, Albers DJ. Evaluating Prediction of Continuous Clinical Values: A Glucose Case Study. Methods Inf Med 2022; 61:e35-e44. [PMID: 35196735 PMCID: PMC9246512 DOI: 10.1055/s-0042-1743170] [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] [Indexed: 10/26/2022]
Abstract
BACKGROUND It would be useful to be able to assess the utility of predictive models of continuous values before clinical trials are performed. OBJECTIVE The aim of the study is to compare metrics to assess the potential clinical utility of models that produce continuous value forecasts. METHODS We ran a set of data assimilation forecast algorithms on time series of glucose measurements from neurological intensive care unit patients. We evaluated the forecasts using four sets of metrics: glucose root mean square (RMS) error, a set of metrics on a transformed glucose value, the estimated effect on clinical care based on an insulin guideline, and a glucose measurement error grid (Parkes grid). We assessed correlation among the metrics and created a set of factor models. RESULTS The metrics generally correlated with each other, but those that estimated the effect on clinical care correlated with others the least and were generally associated with their own independent factors. The other metrics appeared to separate into those that emphasized errors in low glucose versus errors in high glucose. The Parkes grid was well correlated with the transformed glucose but not the estimation of clinical care. DISCUSSION Our results indicate that we need to be careful before we assume that commonly used metrics like RMS error in raw glucose or even metrics like the Parkes grid that are designed to measure importance of differences will correlate well with actual effect on clinical care processes. A combination of metrics appeared to explain the most variance between cases. As prediction algorithms move into practice, it will be important to measure actual effects.
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Affiliation(s)
- George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, United States.,Medical Informatics Services, NewYork-Presbyterian Hospital, New York, New York, United States
| | - David J Albers
- Department of Biomedical Informatics, Columbia University, New York, New York, United States.,Department of Pediatrics, University of Colorado Denver-Anschutz Medical Campus, Denver, Colorado, United States
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The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:257-261. [PMID: 34862549 DOI: 10.1007/978-3-030-85292-4_29] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The applications of artificial intelligence (AI) and machine learning (ML) in modern medicine are growing exponentially, and new developments are fast-paced. However, the lack of trust and appropriate legislation hinder its clinical implementation. Recently, there is a clear increase of directives and considerations on Ethical AI. However, most literature broadly deals with ethical tensions on a meta-level without offering hands-on advice in practice. In this article, we non-exhaustively cover basic practical guidelines regarding AI-specific ethical aspects, including transparency and explicability, equity and mitigation of biases, and lastly, liability.
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Shahi S, Marcotte CD, Herndon CJ, Fenton FH, Shiferaw Y, Cherry EM. Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing. Front Physiol 2021; 12:734178. [PMID: 34646159 PMCID: PMC8502981 DOI: 10.3389/fphys.2021.734178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/27/2021] [Indexed: 11/13/2022] Open
Abstract
The electrical signals triggering the heart's contraction are governed by non-linear processes that can produce complex irregular activity, especially during or preceding the onset of cardiac arrhythmias. Forecasts of cardiac voltage time series in such conditions could allow new opportunities for intervention and control but would require efficient computation of highly accurate predictions. Although machine-learning (ML) approaches hold promise for delivering such results, non-linear time-series forecasting poses significant challenges. In this manuscript, we study the performance of two recurrent neural network (RNN) approaches along with echo state networks (ESNs) from the reservoir computing (RC) paradigm in predicting cardiac voltage data in terms of accuracy, efficiency, and robustness. We show that these ML time-series prediction methods can forecast synthetic and experimental cardiac action potentials for at least 15–20 beats with a high degree of accuracy, with ESNs typically two orders of magnitude faster than RNN approaches for the same network size.
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Affiliation(s)
- Shahrokh Shahi
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Christopher D Marcotte
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Conner J Herndon
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Flavio H Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Yohannes Shiferaw
- Department of Physics & Astronomy, California State University, Northridge, CA, United States
| | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Tivay A, Kramer GC, Hahn JO. Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation. IEEE Trans Biomed Eng 2021; 69:666-677. [PMID: 34375275 DOI: 10.1109/tbme.2021.3103141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models. METHODS To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation. RESULTS In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population. CONCLUSION The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data. SIGNIFICANCE The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.
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Mitchell EG, Heitkemper EM, Burgermaster M, Levine ME, Miao Y, Hwang ML, Desai PM, Cassells A, Tobin JN, Tabak EG, Albers DJ, Smaldone AM, Mamykina L. From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2021; 2021:206. [PMID: 35514864 PMCID: PMC9067367 DOI: 10.1145/3411764.3445555] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.
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Affiliation(s)
| | | | - Marissa Burgermaster
- Department of Population Health, Dell Medical School, and Department of Nutritional Sciences, The University of Texas at Austin
| | - Matthew E. Levine
- Department of Computing and Mathematical Sciences, California Institute of Technology
| | - Yishen Miao
- Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara
| | | | - Pooja M. Desai
- Department of Biomedical Informatics, Columbia University
| | | | | | | | - David J. Albers
- University of Colorado, Anschutz Medical Campus, Section of Informatics and Data Science, Departments of Pediatrics, Biomedical Engineering, and Biostatistics and Informatics, and Department of Biomedical Informatics, Columbia University
| | | | - Lena Mamykina
- Department of Biomedical Informatics, Columbia University
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13
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Stroh JN, Bennett TD, Kheyfets V, Albers D. Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales. JMIR Med Inform 2021; 9:e23215. [PMID: 33749613 PMCID: PMC8077603 DOI: 10.2196/23215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained. OBJECTIVE This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data. METHODS We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP. RESULTS The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation. CONCLUSIONS Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.
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Affiliation(s)
- J N Stroh
- Department of Bioengineering, University of Colorado Denver
- Anschutz Medical Campus, Aurora, CO, United States
| | - Tellen D Bennett
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Vitaly Kheyfets
- Department of Bioengineering, University of Colorado Denver
- Anschutz Medical Campus, Aurora, CO, United States
| | - David Albers
- Department of Bioengineering, University of Colorado Denver
- Anschutz Medical Campus, Aurora, CO, United States.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States
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14
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Diprose WK, Buist N, Hua N, Thurier Q, Shand G, Robinson R. Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator. J Am Med Inform Assoc 2021; 27:592-600. [PMID: 32106285 DOI: 10.1093/jamia/ocz229] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 12/14/2019] [Accepted: 12/31/2019] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outputs will need to be understood and trusted by physicians, and then explained to patients. We therefore investigated the association between physician understanding of ML outputs, their ability to explain these to patients, and their willingness to trust the ML outputs, using various ML explainability methods. MATERIALS AND METHODS We designed a survey for physicians with a diagnostic dilemma that could be resolved by an ML risk calculator. Physicians were asked to rate their understanding, explainability, and trust in response to 3 different ML outputs. One ML output had no explanation of its logic (the control) and 2 ML outputs used different model-agnostic explainability methods. The relationships among understanding, explainability, and trust were assessed using Cochran-Mantel-Haenszel tests of association. RESULTS The survey was sent to 1315 physicians, and 170 (13%) provided completed surveys. There were significant associations between physician understanding and explainability (P < .001), between physician understanding and trust (P < .001), and between explainability and trust (P < .001). ML outputs that used model-agnostic explainability methods were preferred by 88% of physicians when compared with the control condition; however, no particular ML explainability method had a greater influence on intended physician behavior. CONCLUSIONS Physician understanding, explainability, and trust in ML risk calculators are related. Physicians preferred ML outputs accompanied by model-agnostic explanations but the explainability method did not alter intended physician behavior.
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Affiliation(s)
- William K Diprose
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Nicholas Buist
- Department of Emergency Medicine, Whangarei Hospital, Whangarei, New Zealand
| | - Ning Hua
- Orion Health, Auckland, New Zealand
| | | | - George Shand
- Clinical Education and Training Unit, Waitematā District Health Board, Auckland, New Zealand
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15
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Stroh JN, Albers DJ, Bennett TD. Personalization and Pragmatism: Pediatric Intracranial Pressure and Cerebral Perfusion Pressure Treatment Thresholds. Pediatr Crit Care Med 2021; 22:213-216. [PMID: 33528196 PMCID: PMC7861119 DOI: 10.1097/pcc.0000000000002637] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- J N Stroh
- Department of Bioengineering, College of Engineering, Design, and Computing, Aurora, CO
| | - David J Albers
- Department of Bioengineering, College of Engineering, Design, and Computing, Aurora, CO
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
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16
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Karamched B, Hripcsak G, Albers D, Ott W. Delay-induced uncertainty for a paradigmatic glucose-insulin model. CHAOS (WOODBURY, N.Y.) 2021; 31:023142. [PMID: 33653035 PMCID: PMC7910007 DOI: 10.1063/5.0027682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/02/2021] [Indexed: 06/12/2023]
Abstract
Medical practice in the intensive care unit is based on the assumption that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that delay within the glucose-insulin system can induce sustained temporal chaos, rendering the system unpredictable. Specifically, we exhibit such chaos for the ultradian glucose-insulin model. This well-validated, finite-dimensional model represents feedback delay as a three-stage filter. Using the theory of rank one maps from smooth dynamical systems, we precisely explain the nature of the resulting delay-induced uncertainty (DIU). We develop a framework one may use to diagnose DIU in a general oscillatory dynamical system. For infinite-dimensional delay systems, no analog of the theory of rank one maps exists. Nevertheless, we show that the geometric principles encoded in our DIU framework apply to such systems by exhibiting sustained temporal chaos for a linear shear flow. Our results are potentially broadly applicable because delay is ubiquitous throughout mathematical physiology.
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Affiliation(s)
- Bhargav Karamched
- Department of Mathematics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA
| | | | - William Ott
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA
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17
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Mitchell EG, Tabak EG, Levine ME, Mamykina L, Albers DJ. Enabling personalized decision support with patient-generated data and attributable components. J Biomed Inform 2020; 113:103639. [PMID: 33316422 DOI: 10.1016/j.jbi.2020.103639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 08/03/2020] [Accepted: 11/26/2020] [Indexed: 10/22/2022]
Abstract
Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems.
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Affiliation(s)
- Elliot G Mitchell
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
| | - Esteban G Tabak
- Courant Institute of Mathematical Sciences, New York, NY, USA.
| | | | - Lena Mamykina
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
| | - David J Albers
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Pediatrics, Division of Informatics, University of Colorado, Aurora, CO, USA.
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18
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Maier C, Hartung N, de Wiljes J, Kloft C, Huisinga W. Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:153-164. [PMID: 31905420 PMCID: PMC7080550 DOI: 10.1002/psp4.12492] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 12/09/2019] [Indexed: 02/03/2023]
Abstract
An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP‐based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP‐based approaches and show how probabilistic statements about key markers related to chemotherapy‐induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
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Affiliation(s)
- Corinna Maier
- Institute of Mathematics, University of Potsdam, Potsdam, Germany.,Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Freie Universität Berlin and University of Potsdam, Berlin, Potsdam, Germany
| | - Niklas Hartung
- Institute of Mathematics, University of Potsdam, Potsdam, Germany
| | - Jana de Wiljes
- Institute of Mathematics, University of Potsdam, Potsdam, Germany.,Department of Mathematics and Statistics, University of Reading, Whiteknights, UK
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
| | - Wilhelm Huisinga
- Institute of Mathematics, University of Potsdam, Potsdam, Germany
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19
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Albers DJ, Levine ME, Mamykina L, Hripcsak G. The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems. Math Biosci 2019; 316:108242. [PMID: 31454628 PMCID: PMC6759390 DOI: 10.1016/j.mbs.2019.108242] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 08/20/2019] [Accepted: 08/22/2019] [Indexed: 12/21/2022]
Abstract
One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, instead of a set of functions, such as neural networks. Such regressions have an advantage of being useful with particularly sparse, non-stationary clinical data. However, physiological models are often nonlinear and can have many parameters, leading to potential problems with parameter identifiability, or the ability to find a unique set of parameters that minimize forecasting error. The identifiability problems can be minimized or eliminated by reducing the number of parameters estimated, but reducing the number of estimated parameters also reduces the flexibility of the model and hence increases forecasting error. We propose a method, the parameter Houlihan, that combines traditional machine learning techniques with data assimilation, to select the right set of model parameters to minimize forecasting error while reducing identifiability problems. The method worked well: the data assimilation-based glucose forecasts and estimates for our cohort using the Houlihan-selected parameter sets generally also minimize forecasting errors compared to other parameter selection methods such as by-hand parameter selection. Nevertheless, the forecast with the lowest forecast error does not always accurately represent physiology, but further advancements of the algorithm provide a path for improving physiologic fidelity as well. Our hope is that this methodology represents a first step toward combining machine learning with data assimilation and provides a lower-threshold entry point for using data assimilation with clinical data by helping select the right parameters to estimate.
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Affiliation(s)
- David J Albers
- Department of Biomedical Informatics, Columbia University, 622 West 168th Street, PH-20, New York, NY, USA; Department of Pediatrics, Division of Informatics, University of Colorado Medicine, Mail: F443, 13199 E. Montview Blvd. Ste: 210-12 | Aurora, CO 80045 USA.
| | - Matthew E Levine
- Department of Computational and Mathematical sciences, California Institute of Technology, 1200 E California Blvd M/C 305-16 Pasadena, CA 91125 USA
| | - Lena Mamykina
- Department of Biomedical Informatics, Columbia University, 622 West 168th Street, PH-20, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, 622 West 168th Street, PH-20, New York, NY, USA
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20
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Turchioe MR, Heitkemper EM, Lor M, Burgermaster M, Mamykina L. Designing for engagement with self-monitoring: A user-centered approach with low-income, Latino adults with Type 2 Diabetes. Int J Med Inform 2019; 130:103941. [PMID: 31437618 PMCID: PMC6746233 DOI: 10.1016/j.ijmedinf.2019.08.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/12/2019] [Accepted: 08/01/2019] [Indexed: 01/09/2023]
Abstract
BACKGROUND AND SIGNIFICANCE Data-driven interventions for health can help to personalize self-management of Type 2 Diabetes (T2D), but lack of sustained engagement with self-monitoring among disadvantaged populations may widen existing health disparities. Prior work developing approaches to increase motivation and engagement with self-monitoring holds promise, but little is known about applicability of these approaches to underserved populations. OBJECTIVE To explore how low-income, Latino adults with T2D respond to different design concepts for data-driven solutions in health that require self-monitoring, and what features resonate with them the most. MATERIAL AND METHODS We developed a set of mockups that incorporated different design features for promoting engagement with self-monitoring in T2D. We conducted focus groups to examine individuals' perceptions and attitudes towards mockups. Multiple interdisciplinary researchers analyzed data using directed content analysis. RESULTS We conducted 14 focus groups with 25 English- and Spanish-speaking adults with T2D. All participants reacted positively to external incentives. Social connectedness and healthcare expert feedback were also well liked because they enhanced current social practices and presented opportunities for learning. However, attitudes were more mixed towards goal setting, and very few participants responded positively to self-discovery and personalized decision support features. Instead, participants wished for personalized recommendations for meals and other health behaviors based on their personal health data. CONCLUSION This study suggests connections between individuals' degree of internal motivation and motivation for self-monitoring in health and their attitude towards designs of self-monitoring apps. We relate our findings to the self-determination continuum in self-determination theory (SDT) and propose it as a blueprint for aligning incentives for self-monitoring to different levels of motivation.
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Affiliation(s)
- Meghan Reading Turchioe
- Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, United States.
| | | | - Maichou Lor
- School of Nursing, Columbia University, New York, NY, United States
| | - Marissa Burgermaster
- Department of Nutritional Sciences, College of Natural Sciences & Department of Population Health, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
| | - Lena Mamykina
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
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21
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Jackson G, Hu J. Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications. Yearb Med Inform 2019; 28:52-54. [PMID: 31419815 PMCID: PMC6697508 DOI: 10.1055/s-0039-1677925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Objective
: To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018.
Methods
: Ovid MEDLINE
®
and Web of Science
®
databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH
®
) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook.
Results
: Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies.
Conclusions
: Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.
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Affiliation(s)
- Gretchen Jackson
- IBM Watson Health, Cambridge, Massachusetts, USA.,Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, USA
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