Closing the Data Loop: An Integrated Open Access Analysis Platform for the MIMIC Database Mohammad Adibuzzaman1, Ken Musselman1, Alistair Johnson2, Paul Brown3, Zachary Pitluk3, Ananth Grama4 1
2
Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, USA Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA 3 Paradigm4, Waltham, USA 4 Department of Computer Science, Purdue University, West Lafayette, USA relatively rare events, the potential for drug-drug interactions, and these events may be dependent on the physiological status of the patient. “Cardiac safety concerns are a leading reason why pharmaceutical companies withdraw drug applications prior to approval and why approved drugs are removed from the market” [2]. The FDA is critically aware that advanced analytics are essential here, “We hope to identify patterns that will help us predict which patients are at an increased risk for cardiovascular side effects. This knowledge can guide the development of safer treatments” [2]. Moreover, the FDA understands that clinically relevant results will require large data sets, “In addition, very small increases in the QT interval appear to carry risk, so studies that assess cardiac drug effects require collection of many thousands of ECGs” [2]. Recent developments in models and methods for data sciences such as deep learning, coupled with massively parallel computing platforms are enabling significant advances in applications such as image processing, natural language processing, and computational biology. Analysis of large volumes of ECG and blood pressure data support data driven clinical decision making. However, improvements in computational throughput have not translated into increases in clinical understanding. The amount of translational research effectively utilizes only a small fraction of the terabytes of data currently being collected in clinical settings [3]. Primary obstacles to more effective utilization include poor lines of communication between data scientists and clinicians on disease prognoses, technical difficulties due to the heterogeneity and complexity of physiological data, and lack of regulatory guidelines. This latter consideration also includes the absence of research with data driven systems to assess the risk and benefit of using such systems in clinical settings. The importance of data driven systems in clinical decision making is at the core of the Precision Medicine initiative: collection of clinical data in the form of electronic health
Abstract We describe a new model for collaborative access, exploration, and analyses of the Medical Information Mart for Intensive Care - III (MIMIC III) database for translational clinical research. The proposed model addresses the significant disconnect between data collection at the point of care and translational clinical research. It addresses problems of data integration, preprocessing, normalization, analyses (along with associated compute back-end), and visualization. The proposed platform is general, and can be easily adapted to other databases. The pre-packaged analyses toolkit is easily extensible, and allows for multi-language support. The platform can be easily federated, mirrored at other locations, and supports a RESTful API for service composition and scaling.
1.
Introduction
The Precision Medicine Initiative was recently launched to develop a new model of patient-focused research, to “accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients” [1]. Precision Medicine can help develop best practices and to enhance safety by integrating multiple lines of evidence. The Intensive Care Unit (ICU) represents a unique data source for supporting precision medicine. ICU records are comprised of clinical treatment data, large ECG and blood pressure monitoring data sets, and associated outcomes. ICU data is often invaluable in understanding cardio physiology and the impact of medicines on heart physiology, as measured by blood pressure and ECG. Additionally, ICU records may provide enough data to support expansion of accepted endpoints and deeper insights into drug safety. One of the challenges of cardiac safety research is that there are
Computing in Cardiology 2016; 43:
137
TABLE 1. RESEARCH PROJECTS WITH MIMIC DATABASE. ‘C’ FOR CLINICAL AND ‘W’ FOR WAVEFORM OR NUMERIC DATABASE.
Citation [4]
[5]
[6]
[7]
[8]
Research Problem Mortality Prediction with acute kidney injury (AKI) Local customized mortality prediction, outcome is survival to hospital discharge Whether ‘similar’ dynamical patterns can be identified across a heterogeneous patient cohort Whether red cell distribution width (RDW) has the potential to improve prognostic performance Investigate discriminatory pattern in hemodynamic data
Methods Multivariable Regression
Cohort Selection Criteria /ϵс
2
Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, USA Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA 3 Paradigm4, Waltham, USA 4 Department of Computer Science, Purdue University, West Lafayette, USA relatively rare events, the potential for drug-drug interactions, and these events may be dependent on the physiological status of the patient. “Cardiac safety concerns are a leading reason why pharmaceutical companies withdraw drug applications prior to approval and why approved drugs are removed from the market” [2]. The FDA is critically aware that advanced analytics are essential here, “We hope to identify patterns that will help us predict which patients are at an increased risk for cardiovascular side effects. This knowledge can guide the development of safer treatments” [2]. Moreover, the FDA understands that clinically relevant results will require large data sets, “In addition, very small increases in the QT interval appear to carry risk, so studies that assess cardiac drug effects require collection of many thousands of ECGs” [2]. Recent developments in models and methods for data sciences such as deep learning, coupled with massively parallel computing platforms are enabling significant advances in applications such as image processing, natural language processing, and computational biology. Analysis of large volumes of ECG and blood pressure data support data driven clinical decision making. However, improvements in computational throughput have not translated into increases in clinical understanding. The amount of translational research effectively utilizes only a small fraction of the terabytes of data currently being collected in clinical settings [3]. Primary obstacles to more effective utilization include poor lines of communication between data scientists and clinicians on disease prognoses, technical difficulties due to the heterogeneity and complexity of physiological data, and lack of regulatory guidelines. This latter consideration also includes the absence of research with data driven systems to assess the risk and benefit of using such systems in clinical settings. The importance of data driven systems in clinical decision making is at the core of the Precision Medicine initiative: collection of clinical data in the form of electronic health
Abstract We describe a new model for collaborative access, exploration, and analyses of the Medical Information Mart for Intensive Care - III (MIMIC III) database for translational clinical research. The proposed model addresses the significant disconnect between data collection at the point of care and translational clinical research. It addresses problems of data integration, preprocessing, normalization, analyses (along with associated compute back-end), and visualization. The proposed platform is general, and can be easily adapted to other databases. The pre-packaged analyses toolkit is easily extensible, and allows for multi-language support. The platform can be easily federated, mirrored at other locations, and supports a RESTful API for service composition and scaling.
1.
Introduction
The Precision Medicine Initiative was recently launched to develop a new model of patient-focused research, to “accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients” [1]. Precision Medicine can help develop best practices and to enhance safety by integrating multiple lines of evidence. The Intensive Care Unit (ICU) represents a unique data source for supporting precision medicine. ICU records are comprised of clinical treatment data, large ECG and blood pressure monitoring data sets, and associated outcomes. ICU data is often invaluable in understanding cardio physiology and the impact of medicines on heart physiology, as measured by blood pressure and ECG. Additionally, ICU records may provide enough data to support expansion of accepted endpoints and deeper insights into drug safety. One of the challenges of cardiac safety research is that there are
Computing in Cardiology 2016; 43:
137
TABLE 1. RESEARCH PROJECTS WITH MIMIC DATABASE. ‘C’ FOR CLINICAL AND ‘W’ FOR WAVEFORM OR NUMERIC DATABASE.
Citation [4]
[5]
[6]
[7]
[8]
Research Problem Mortality Prediction with acute kidney injury (AKI) Local customized mortality prediction, outcome is survival to hospital discharge Whether ‘similar’ dynamical patterns can be identified across a heterogeneous patient cohort Whether red cell distribution width (RDW) has the potential to improve prognostic performance Investigate discriminatory pattern in hemodynamic data
Methods Multivariable Regression
Cohort Selection Criteria /ϵс
