Proposal Title: Smart Analytic Health Plan Systems

Transcription

1 Proposal Title: Smart Analytic Health Plan Systems By Daniela Raicu, Associate Professor, School of Computing, DePaul University I. Proposal Goals and Motivation Someone is going to turn healthcare into a true system, designed with the patient's health in mind. Someone is going to put in place the key building blocks. Importantly, someone is going to institute standards, accomplish cross-segment connectivity and break down the silos. Someone is going to unleash and scale the expertise and creativity of America's healthcare professionals. And someone is going to build the capacity to identify the key patterns in all this data and knowledge. Samuel J. Palmisano, IBM Chairman, 2009 Medical Innovation Summit The goal of this proposal is to work together with IBM on developing an IT curriculum that will provide the next generation with the health care industry skills needed for a smarter planet. Given the data avalanche being created by the explosion of the medical imaging technologies, advanced analytical skills are needed to improve research, diagnosis, and treatment. Depending on the size of the institution, a radiology department can perform from hundreds to many thousands of examinations per day, generating a myriad of images, patient data, report text, findings, and recommendations [1]. New digital image management systems (e.g. Picture Archiving and Communication Systems - PACS) have been developed for image acquisition, storage, transmission, processing and display of images for their analysis and further diagnosis. IBM is taking the next step towards a better PACS (e.g. PACS++) through the creation of the new multi-modal Medical Analytics Platform. The platform is a new system that enables real-time collection-level text and image analytics for improved diagnosis and patient care. Medical record text analysis is applied to patient records to make them amenable for collection-level search and retrieval based on semantic concepts derived from RadLex taxonomy. Availability of digital data within PACS raises a possibility of health care and research enhancements associated with manipulation, processing and handling of data by computers, that is a basis for computer-assisted radiology development. In general, radiology data is well organized but poorly structured, and structuring this data prior to knowledge extraction is an essential first step in the successful mining of radiological data [1]. Compared to text, radiology images are enormous in size, highly variable over time, and quite difficult to mine. Therefore, image processing and data mining techniques are necessary for structuring, 1 P a g e

2 management, retrieval, and interpretation of image data. The curriculum material developed under this proposal will teach a set of advanced data analysis and data mining skills that will help identify key patterns in radiology imaging data. II. Description of the approach The course module will focus on teaching classification approaches for learning key patterns in medical data that will help with the creation of efficient and accurate computer-aided diagnosis systems. Two classification approaches, decision trees and linear discriminant analysis, will be taught in the context of lung nodules classification in Computed Tomography images of the chest. While the approach will be presented in the context of Computed Tomography (CT) images and lung nodules classification, it can be applied to any other medical imaging data (e.g. MRI, PET, etc) and other anatomical structures (e.g. liver, kidney, etc) besides CT images of the lung nodules. Furthermore, the same techniques can be experimented on newly available medical data such as the ones collected by IBM through its new philanthropic tool called City Forward. In the following three subsections, we describe the dataset, the two classification approaches, and the performance evaluation measures included in the course module. II.1. Lung Image Database Consortium (LIDC) Dataset The course module will be created using the NIH/NCI Lung Image Database Consortium (LIDC) dataset. The dataset [2] was created to serve as an international research resource for development, training, and evaluation of Computer-aided Diagnosis (CAD) algorithms for detecting lung nodules on CT scans. It is downloadable through the National Cancer Institute s Imaging Archive web site - and provides the image data, the radiologists nodule outlines, and the radiologists subjective ratings of nodule characteristics for this study. Any LIDC radiologist who identified a structure as a nodule > 3 mm also provided subjective ratings for 9 nodule characteristics: subtlety, internal structure, calcification, sphericity, margin, lobulation, spiculation, texture, and malignancy likelihood. For example, the texture characteristic provides meaningful information regarding nodule appearance ( Non-Solid, Part Solid/(Mixed), Solid ) while malignancy characteristic captures the likelihood of malignancy ( Highly Unlikely, Moderately Unlikely, Indeterminate, Moderately Suspicious, Highly Suspicious ) as perceived by the LIDC radiologists ([3]). The LIDC database currently contains complete thoracic CT scans for 208 patients acquired over different periods of time and with various scanner models resulting in a wide range of values of the 2 P a g e

3 imaging acquisition parameters. The Medical Informatics (MedIX) lab of School of Computing at DePaul ( has already processed the LIDC data and, for each one of the 914 nodules (all larger than 3mm), a set of 64 image features were calculated to quantify the texture, size, intensity, and shape of the nodules [4]. This feature data set will be made available to exemplify the classification approaches for computer-aided diagnostic and characterization as proposed for the course module. While the LIDC dataset has been used by different research groups for nodule detection, characterization, and diagnosis, through this course module the LIDC will be used for first time for imaging analytics education and training purposes. II.2. Classification Approaches for Computer-aided Diagnosis (CADx) Given that medical imaging technology allows generating higher-resolution images and many more slices per single study, the efficiency and accuracy of the radiologists interpreting these images may be affected. New methodologies and systems are required to assist the radiologists in coping with this trend and improving their performance. Classification approaches have been used in imaging analytics to build computer-aided diagnosis systems that act as second readers in the diagnostic decision making process. Linear discriminant analysis (LDA) and decision trees are two of the classification approaches that have been used for computer-aided diagnosis for breast cancer and lung nodules. The course module will present the theory behind these two approaches and will illustrate them through examples of classification of lung nodules based on the LIDC dataset. II.3. Evaluation Measures for Computer-Aided Diagnosis The critical requirement of a classifier is its ability to generalize well. Namely, it should correctly classify new datasets. Therefore, ways for successful validation of the classification approaches to achieve a balance between performing well on the training dataset while generalizing well on new cases will be presented. III. Course Module Milestones The course module will be developed using IBM/SPSS technology and will contain the following items as outlined in Table 1: A. Instructor guide on how to use the materials inside the module 3 P a g e

4 B. PowerPoint lecture notes for the instructor C. Practice exercises hands on work with the LIDC publically available medical dataset D. Video demonstrations on how to use the appropriate IBM/SPSS software The development of the course module will be followed by the experimentation part within the Medical Informatics (MedIX) laboratory; the MedIX lab is affiliated with the DePaul Data Mining and Predictive Analytics (DaMPA) Center ( whose first industry partner is IBM. The course module will be also taught in two DePaul courses: IS567: Knowledge Discovery Technologies and CSC424: Multivariate Statistics. Table 1: Proposed materials for the course module Milestones for the Description Course Module Instructor guide Readme file on how to use the materials from the module Chapter 1: What is classification and what its applications are Lecture notes Chapter 2: Decision trees PowerPoint Chapter 3: Linear discriminant analysis presentations Chapter 4: Evaluation and interpretation of classification results All the steps from the lecture notes will be illustrated through case studies from Labs/exercises the medical domain. One case study will be performed on the Lung Imaging Database Consortium (LIDC) dataset and another possible one on a dataset identified by IBM medical experts. All steps of applying the two classification approaches presented in the PowerPoint lecture notes will be demonstrated and recorded using Camtasia Video demonstrations which allows us to integrate audio recording while screen-capturing is in progress, so the presenter can narrate the demonstration as it is carried out. The video output will be exported to common video formats such as MPEG-2 or MPEG-4. IV. Benefits of the curriculum and societal impact The developed curriculum materials will be organized such that they can be used in whole or part to teach university students at both the undergraduate and graduate levels. Courses such as medical image processing and analysis, computer vision, data analysis, and data mining taught in computer sciences, information systems, medical physics, and biomedical engineering can use the proposed course module. Students developing the taught skills should be able to work further in computerassisted radiology jobs or any other related imaging analytics jobs. 4 P a g e

5 In the long run, preparing the necessary technical workforce to assist radiologists, surgeons, and oncologists in their daily workflow will allow better decision making in the diagnosis process and further expand the number of opportunites for computer-aided medical research and education. V. Current funding and interested collaborators The faculty, Daniela Stan Raicu, has teaching and research experience in imaging analytics for over 10 years. She is the co-director of the Medical Informatics and the Intelligent Multimedia Processing Laboratories, and the Director of the Data Mining and Predictive Analytics Center at DePaul. Her research interests include medical imaging, multimedia retrieval, pattern recognition and data mining. She is the recipient of the DePaul Excellence in Teaching Award 2008 and the DePaul Spirit of Inquiry Award in Daniela's medical imaging projects have been funded by the National Science Foundation (NSF) and the Department of Energy (DOE). She is collaborating with Dr. Sam Armato of University Chicago and Dr. David Channin of Northwestern University on her medical imaging research. Her work has also similarities with the work performed in the IBM medical imaging analytics research group, in particular with the work lead by Ebadollahi [5]. VI. References [1] Dreyer KJ The Alchemy of Data Mining, Imaging Economics, [2] Armato SG, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR et al. Lung Image Database Consortium: Developing a resource for the medical imaging research community, Radiology, 232(3): , [3] McNitt-Gray M.F., Armato S.G. III, Meyer C.R., Reeves A.P., McLennan G. et al. The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Academic Radiology , [4] Zinovev D, Raicu DS, Furst JD, Armato III, SG. Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers, Algorithms 2009, 2, [5] S Ebadollahi, J Cooper, D Kaufman, A Levas, A Laine, et al. "Concept-Oriented Access to Longitudinal Multimedia Medical Records: A Case Study in Brain Tumor Patient Management." Proceedings of Cross-Media Information Analysis, Extraction and Management Workshop (SAMT 2009). 5 P a g e