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Diaz-Garelli F, Strowd R, Lawson VL, Mayorga ME, Wells BJ, Lycan TW, Topaloglu U. Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel. JCO Clin Cancer Inform 2020; 4:529-538. [PMID: 32543899 PMCID: PMC7331128 DOI: 10.1200/cci.19.00114] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Diagnosis (DX) information is key to clinical data reuse, yet accessible structured DX data often lack accuracy. Previous research hints at workflow differences in cancer DX entry, but their link to clinical data quality is unclear. We hypothesized that there is a statistically significant relationship between workflow-describing variables and DX data quality. METHODS We extracted DX data from encounter and order tables within our electronic health records (EHRs) for a cohort of patients with confirmed brain neoplasms. We built and optimized logistic regressions to predict the odds of fully accurate (ie, correct neoplasm type and anatomic site), inaccurate, and suboptimal (ie, vague) DX entry across clinical workflows. We selected our variables based on correlation strength of each outcome variable. RESULTS Both workflow and personnel variables were predictive of DX data quality. For example, a DX entered in departments other than oncology had up to 2.89 times higher odds of being accurate (P < .0001) compared with an oncology department; an outpatient care location had up to 98% fewer odds of being inaccurate (P < .0001), but had 458 times higher odds of being suboptimal (P < .0001) compared with main campus, including the cancer center; and a DX recoded by a physician assistant had 85% fewer odds of being suboptimal (P = .005) compared with those entered by physicians. CONCLUSION These results suggest that differences across clinical workflows and the clinical personnel producing EHR data affect clinical data quality. They also suggest that the need for specific structured DX data recording varies across clinical workflows and may be dependent on clinical information needs. Clinicians and researchers reusing oncologic data should consider such heterogeneity when conducting secondary analyses of EHR data.
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Affiliation(s)
- Franck Diaz-Garelli
- University of North Carolina at Charlotte, Charlotte, NC
- Wake Forest School of Medicine, Winston Salem, NC
| | - Roy Strowd
- Wake Forest School of Medicine, Winston Salem, NC
| | - Virginia L. Lawson
- University of North Carolina at Charlotte, Charlotte, NC
- Wake Forest School of Medicine, Winston Salem, NC
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2
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Mate S, Bürkle T, Kapsner LA, Toddenroth D, Kampf MO, Sedlmayr M, Castellanos I, Prokosch HU, Kraus S. A method for the graphical modeling of relative temporal constraints. J Biomed Inform 2019; 100:103314. [PMID: 31629921 DOI: 10.1016/j.jbi.2019.103314] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 08/13/2019] [Accepted: 10/14/2019] [Indexed: 02/06/2023]
Abstract
Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen's time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries.
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Affiliation(s)
- Sebastian Mate
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
| | - Thomas Bürkle
- Bern University of Applied Sciences, Biel, Switzerland
| | - Lorenz A Kapsner
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Dennis Toddenroth
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Marvin O Kampf
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ixchel Castellanos
- Department of Anesthesiology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany; Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan Kraus
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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3
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Diaz-Garelli JF, Strowd R, Ahmed T, Wells BJ, Merrill R, Laurini J, Pasche B, Topaloglu U. A tale of three subspecialties: Diagnosis recording patterns are internally consistent but Specialty-Dependent. JAMIA Open 2019; 2:369-377. [PMID: 31984369 PMCID: PMC6951969 DOI: 10.1093/jamiaopen/ooz020] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 04/22/2019] [Accepted: 05/27/2019] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Structured diagnosis (DX) are crucial for secondary use of electronic health record (EHR) data. However, they are often suboptimally recorded. Our previous work showed initial evidence of variable DX recording patterns in oncology charts even after biopsy records are available. OBJECTIVE We verified this finding's internal and external validity. We hypothesized that this recording pattern would be preserved in a larger cohort of patients for the same disease. We also hypothesized that this effect would vary across subspecialties. METHODS We extracted DX data from EHRs of patients treated for brain, lung, and pancreatic neoplasms, identified through clinician-led chart reviews. We used statistical methods (i.e., binomial and mixed model regressions) to test our hypotheses. RESULTS We found variable recording patterns in brain neoplasm DX (i.e., larger number of distinct DX-OR = 2.2, P < 0.0001, higher descriptive specificity scores-OR = 1.4, P < 0.0001-and much higher entropy after the BX-OR = 3.8 P = 0.004 and OR = 8.0, P < 0.0001), confirming our initial findings. We also found strikingly different patterns for lung and pancreas DX. Although both seemed to have much lower DX sequence entropy after the BX-OR = 0.198, P = 0.015 and OR = 0.099, P = 0.015, respectively compared to OR = 3.8 P = 0.004). We also found statistically significant differences between the brain dataset and both the lung (P < 0.0001) and pancreas (0.009 CONCLUSION Our results suggest that disease-specific DX entry patterns exist and are established differently by clinical subspecialty. These differences should be accounted for during clinical data reuse and data quality assessments but also during EHR entry system design to maximize accurate, precise and consistent data entry likelihood.
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Affiliation(s)
| | - Roy Strowd
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
| | - Tamjeed Ahmed
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
| | - Brian J Wells
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
| | - Rebecca Merrill
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
| | - Javier Laurini
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
| | - Boris Pasche
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
| | - Umit Topaloglu
- Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
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4
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Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med 2018; 95:27-37. [PMID: 30213670 DOI: 10.1016/j.artmed.2018.08.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 08/10/2018] [Accepted: 08/20/2018] [Indexed: 12/25/2022]
Abstract
BACKGROUND Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmission prediction models are often built based on physiological variables. Intuitively, snapshot measurements, especially the last measurements, are effective predictors that are widely used by researchers. However, methods that only use snapshot measurements neglect predictive information contained in the trends of physiological and medication variables. Mean, maximum or minimum values take multiple time points into account and capture their summary statistics, however, these statistics are not able to catch the detailed picture of temporal trends. In this study, we find strong predictors with ability of capturing detailed temporal trends of variables for 30-day readmission risk and build prediction models with high accuracy. METHODS We study physiological measurements and medications from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) clinical dataset. Time series of each variable are converted into trend graphs with nodes being discretized measurements of each variable. Then we extract important temporal trends by applying frequent subgraph mining on the trend graphs. The frequency of a subgraph is a good cue to find important temporal trends since similar patients often share similar trends regarding their pathophysiological evolution under medical interventions. Important temporal trends are then grouped automatically by non-negative matrix factorization. The grouped trends could be considered as an approximate representation of patients' pathophysiological states and medication profiles. We train a logistic regression model to predict 30-day ICU readmission risk based on snapshot measurements, grouped physiological trends and medication trends. RESULTS Our dataset consists of 1170 patients who are alive 30 days after discharge from ICU and have at least 12 h of data. In the dataset, 860 patients were not readmitted and 310 were readmitted, within 30 days after discharge. Our model outperforms all comparison models, and shows an improvement in the area under the receiver operating characteristic curve (AUC) of almost 4% from the best comparison model. CONCLUSIONS Grouped physiological and medication trends carry predictive information for ICU readmission risk. In order to build predictive models with higher accuracy, we should add grouped physiological and medication trends as complementary features to snapshot measurements.
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Affiliation(s)
- Ye Xue
- Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA.
| | - Diego Klabjan
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA.
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
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Park S, Megjhani M, Frey HP, Grave E, Wiggins C, Terilli KL, Roh DJ, Velazquez A, Agarwal S, Connolly ES, Schmidt JM, Claassen J, Elhadad N. Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data. J Clin Monit Comput 2018; 33:95-105. [PMID: 29556884 DOI: 10.1007/s10877-018-0132-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 03/15/2018] [Indexed: 11/26/2022]
Abstract
To develop and validate a prediction model for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) using a temporal unsupervised feature engineering approach, demonstrating improved precision over standard features. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Baseline information and standard grading scales were evaluated: age, sex, Hunt Hess grade, modified Fisher Scale (mFS), and Glasgow Coma Scale (GCS). An unsupervised approach applying random kernels was used to extract features from physiological time series (systolic and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (Partial Least Squares, linear and kernel Support Vector Machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.58. Combined demographics and grading scales: AUC 0.60. Random kernel derived physiologic features: AUC 0.74. Combined baseline and physiologic features with redundant feature reduction: AUC 0.77. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that our models achieve higher classification accuracy.
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Affiliation(s)
- Soojin Park
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA.
| | - Murad Megjhani
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - Hans-Peter Frey
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - Edouard Grave
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Chris Wiggins
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
| | - Kalijah L Terilli
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - David J Roh
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - Angela Velazquez
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - Sachin Agarwal
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | | | - J Michael Schmidt
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - Jan Claassen
- Department of Neurology, Columbia University, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA
| | - Noemie Elhadad
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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6
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Megjhani M, Terilli K, Frey HP, Velazquez AG, Doyle KW, Connolly ES, Roh DJ, Agarwal S, Claassen J, Elhadad N, Park S. Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods. Front Neurol 2018; 9:122. [PMID: 29563892 PMCID: PMC5845900 DOI: 10.3389/fneur.2018.00122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 02/19/2018] [Indexed: 11/13/2022] Open
Abstract
Purpose Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. Methods 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. Results The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Conclusion Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.
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Affiliation(s)
- Murad Megjhani
- Department of Neurology, Columbia University, New York, NY, United States
| | - Kalijah Terilli
- Department of Neurology, Columbia University, New York, NY, United States
| | - Hans-Peter Frey
- Department of Neurology, Columbia University, New York, NY, United States
| | - Angela G Velazquez
- Department of Neurology, Columbia University, New York, NY, United States
| | | | | | - David Jinou Roh
- Department of Neurology, Columbia University, New York, NY, United States
| | - Sachin Agarwal
- Department of Neurology, Columbia University, New York, NY, United States
| | - Jan Claassen
- Department of Neurology, Columbia University, New York, NY, United States
| | - Noemie Elhadad
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Soojin Park
- Department of Neurology, Columbia University, New York, NY, United States
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7
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Temporal electronic phenotyping by mining careflows of breast cancer patients. J Biomed Inform 2017; 66:136-147. [PMID: 28057564 DOI: 10.1016/j.jbi.2016.12.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 11/21/2016] [Accepted: 12/31/2016] [Indexed: 11/22/2022]
Abstract
In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.
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8
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Boland MR, Jacunski A, Lorberbaum T, Romano JD, Moskovitch R, Tatonetti NP. Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 8:104-22. [PMID: 26559926 DOI: 10.1002/wsbm.1323] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 09/30/2015] [Accepted: 10/01/2015] [Indexed: 01/06/2023]
Abstract
Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.
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Affiliation(s)
- Mary Regina Boland
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Observational Health Data Science and Informatics (OHDSI), New York, NY, USA
| | - Alexandra Jacunski
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University, New York, NY, USA
| | - Tal Lorberbaum
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA
| | - Joseph D Romano
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA
| | - Robert Moskovitch
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Observational Health Data Science and Informatics (OHDSI), New York, NY, USA
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Abstract
The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.
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Affiliation(s)
- Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy IRCCS Fondazione S. Maugeri, Pavia, Italy
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Lucia Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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