--------------------------------------------------------------------- Utah Workshop on the Applications of Intelligent and Adaptive Systems --------------------------------------------------------------------- May 10th, 1993 - Salt Lake City, UT, USA The University of Utah Cognitive Science Group's Industrial Advisory Board presents the first Utah Workshop on the "Applications of Intelligent and Adaptive Systems." This will be held at the Salt Lake City VA Medical Center's multipurpose room, Building 8, on May 10th. This event is free and open to the public. A listing of speakers, titles, abstracts, e-mail addresses, phone numbers, parking information, etc.., is provided at the end of this notice. For listings of relevant publications, biographical sketches of the speakers, or a hard copy of a map of the VA campus and appropriate parking, contact Jerome Soller (soller@asylum.cs.utah.edu,582-1565, ext. 2469). Hopefully, we will have a videotape of this event available public domain at a later point. This workshop complements the yearly Psychology Department's William F. Prokasy Lecture by Dr. Irving Biederman, Head of the Cognitive and Behavioral Neuroscience Program at the University of Southern California. Dr. Biederman will speak on Tuesday, May 11, at 5:00 p.m. in BEH SCI 110 at the University of Utah. The title is "Shape Recognition in Mind and Brain." The official notice from Kim Poulson is included after the workshop description. Summary (abstracts and contact information are provided at the end of this notice): Talk 1: 10:00-10:45 The Utah State University Space Dynamics Laboratory, An Introduction Presented by J Steven Hansen, Ph.D. Director, Instrument and Data Evaluation Center Space Dynamics Laboratory Associate Research Professor, Department of Electrical and Computer Engineering Department of Physics Utah State University Talk 2: 10:45-11:30 Active Noise and Vibration Control: Structures, Algorithms, and Applications Scott C. Douglas, Ph.D. Department of Electrical Engineering University of Utah Talk 3: 11:30 - 12:15 A COMPUTERIZED DECISION SUPPORT SYSTEM FOR CRITICAL CARE: MANAGEMENT OF MECHANICAL VENTILATION IN PATIENTS WITH ARDS Thomas D. East, Ph.D.: Director of informatics research in the pulmonary division at LDS Hospital(Intermountain Health Care). Associate professor of anesthesiology, bioengineering and medical informatics at the University of Utah. Lunch break: 12:15-1:45 Talk 4: 1:45-2:30 "Neural Networks for Classification, Signal Processing, and Control in Patient Monitoring" Dwayne Westenskow, Ph.D. Acting Director of Institute of Life Support in Space and Professor, U. of Utah Department of Anesthesiology (additional appointments in Bioengineering, Surgery, and Medical Informatics). Talk 5: 2:30-3:15 Towards a General Self-Organizing Learning Model Tony Martinez, Ph.D., Director of the Neural Networks and Machine Intelligence Research Group, Brigham Young University Assistant Professor, Department of Computer Science and Department of Electrical and Computer Engineering, Brigham Young University. Talk 6: 3:15-4:00 Neil Cotter, Ph.D. Consulting Engineer, Geneva Steel Research Assistant Professor, Electrical Engineering, University of Utah "Neural Networks, Fuzzy Logic, and Triangulation in Process Control" (801-227-9865) Sincerely, Dick Burgess, U. of Utah Department of Physiology and Program Director, U. of Utah Cog. Sci. Group (801-581-4072) Dale Sanders, Chairman of Industrial Advisory Board to U. of Utah Cognitive Science Group and Senior Technical Member, TRW Corporation (dsanders@bmd.trw.com, 801-625-8343) Robert L. Angell, Principal, Applied Information and Management Systems and Small Business Representative to U. of Utah Cognitive Science Group (bangell@cs.utah.edu, 801-583-8544) Jerome B. Soller, VA GRECC and U. of Utah Department of Computer Science (soller@asylum.cs.utah.edu, 801-582-1565, ext. 2469) Abstracts: ---------------------------------------------------------------------- Active Noise and Vibration Control: Structures, Algorithms, and Applications Scott C. Douglas, Ph.D. Department of Electrical Engineering University of Utah Active noise and vibration control is a method for cancelling unwanted sound or vibration by generating and introducing an equal-but-opposite acoustic signal into the noise environment. Current research and application developments in this field include active mufflers for automobiles, active quieting of machine noise on factory floors, active vibration mounts for airplane engines, active noise suppressors for air ducts, and active suppression of wall vibration for apartment dwellings. In this talk, I will provide an overview of the technology needed for active noise control systems. In particular, I will discuss the digital signal processing hardware and multichannel adaptive control algorithms required for noise cancellation across large acoustic regions. Results from an active noise suppression system for quieting an air conditioner compressor will be presented. ------------------------------------------------------------------------ Neil Cotter, Ph.D. Consulting Engineer, Geneva Steel Research Assistant Professor, E.E. Department, University of Utah "Neural Networks, Fuzzy Logic, and Triangulation in Process Control" (801-227-9865) Many process control problems in industry require the approximation of nonlinear functions of several variables. This presentation will include a tutorial showing that the following approximation methods are structurally similar to Fourier series: linear regression, neural networks, radial basis functions, and fuzzy logic. The speaker will contrast these methods with a practical interpolation method called triangulation. -------------------------------------------------------------------------- "Neural Networks for Classification, Signal Processing, and Control in Patient Monitoring" Dwayne R. Westenskow, Ph.D. Department of Anesthesiology (801-581-6393, drw@cc.utah.edu) Artificial neural networks learn from examples to construct a function which maps input variables to a desired output. The mapping provides closed-loop control where the inputs are the variables to be controlled, and the output is the drive signal for an actuator. Neural network control will be illustrated with the truck backing up example, introducing the concept of supervised learning. The neural network mapping similarity provides for the classification of data, as will be illustrated with an intelligent anasthesia alaram system. Because the mapping function extrapolates between individual data points, the neural network provides signal processing, i.e., filtering and noise and artifact rejection. This will be illustrated by showing the neural network processing of oscillometric blood pressure curves and cardiac output by thermal dilution. In both of these examples, the neural network provides a nonlinear mapping which compensates for the overestimation at low values and underestimation at high values, which occurs with traditional linear algorithms. Success in processing Doppler signals for the detection of air embolism illustrates how the mapping function is constructed using training data rather than expert knowledge. --------------------------------------------------------------------- Dr. Tony Martinez, Director of the Neural Networks and Machine Intelligence Research Group, Brigham Young University Assistant Professor, Department of Computer Science and Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT Towards a General Self-Organizing Learning Model (martinez@cs.byu.edu, 801-378-6464) A new class of connectionist architectures is presented called ASOCS (Adaptive Self-Organizing Concurrent Systems). ASOCS models support efficient computation through self-organized learning and parallel execution. Learning is done through the incremental presentation of rules and/or examples. Data types include Boolean and multi-state variables; recent models support analog variables. The model incorporates rules into an adaptive logic network in a parallel and self organizing fashion. The system itself resolves inconsistencies and generalizes as the rules are presented. After an introduction to the ASOCS paradigm, the talk introduces current research thrusts which significantly increase the power and applicability of ASOCS models. Current application targets include adaptive network routing, speech recognition, automated document updating, and general classification problems. ------------------------------------------------------------------------ Author Information: Thomas D. East, Ph.D., Pulmonary Division, LDS Hospital, 8th Ave and C St., Salt Lake City, UT, 84143, 801-321-3503 phone, 801-321-1671 fax, teast@fenta.med.utah.edu Audio Visual Media used: 35 mm slide projector and VHS video projector. A COMPUTERIZED DECISION SUPPORT SYSTEM FOR CRITICAL CARE: MANAGEMENT OF MECHANICAL VENTILATION IN PATIENTS WITH ARDS T.D. East, A.H. Morris, C.J. Wallace, A. T. Kinder, W.D. Littman*, J.S. Gochberg* Pulmonary Division, LDS Hospital,Salt Lake City, Utah 84143 * ACT/PC, 6501 Watts Road, Suite 115, Madison, WI 53719 Thomas D. East,Ph.D.: Associate professor of anesthesiology, bioengineering and medical informatics at the University of Utah. Director of informatics research in the pulmonary division at LDS Hospital. MEBE and Ph.D. in bioengineering from the U of U. My research interests are in the applications of computers to critical care. In particular much of my work has been in the area of rule based decision support systems and knowledge engineering. The care of critically ill patients is increasingly complex and clinicians frequently suffer from information overload. It is difficult, if not impossible to assess all this information and generate a systematic and reasonable therapy plan. Computerized decision support systems can assist the clinician with many of the tasks such as the iterative management of mechanical ventilation. This decision support not only standardizes care but may improve the quality of care by reducing mistakes. This standardization of care also makes it possible to thoroughly characterize the current treatment process in order to compare it to a proposed new therapy as part of an ongoing continuous quality improvement (CQI) program. A computerized decision support system for the management of mechanical ventilation (respiratory evaluation, oxygenation, ventilation, weaning and extubation) in patients with adult respiratory distress syndrome has already been developed and clinically validated at the LDS Hospital (1, 2) . The protocol logic was developed using our existing consensus generating physician group and was implemented on the HELP system (3) . The computerized decision support system was used for over 35,000 hours in 111 Adult Respiratory Distress Syndrome patients and has controlled decision making 95% of the 24 hour day. The survival rate was 67%, higher than the expected 31-33% from historical data (4, 5) , p < 0.05. These results have demonstrated that computerized decision support for critical care is feasible. We are in the process of conducting a prospective randomized clinical trial to test efficacy of computerized protocols in 400 patients with ARDS at two different clinical sites; KDMC a county hospital in the Watts district of Los Angeles, CA and Hermann Hospital, a private hospital in Houston affiliated with University of Texas Medical School (H0: There is no difference in efficacy between protocol and non-protocol controlled critical care). The knowledge base (set of protocol logic rules) was transferred from the HELP system at LDS Hospital to a PC based ICU computer system known as ARGUS Windows (ACT/PC, Madison , WI). ARGUS Windows runs under QNX V4.1 and QNX windows V2.03. The rules were implemented using a rule based decision support engine designed by ACT/PC. The engine is a finite state automata written in C for QNX and QNX Windows. The ARGUS Windows system has been installed at all 12 beds of the surgical ICU at KDMC. This system is now in routine use for respiratory care charting and the decision support system has been used to successfully care for 10 ARDS patients in a pilot study of feasibility. In the randomized trial, will define efficacy using a hierarchical four level approach; Efficacy F a)Survival, b) Length of ICU Stay, c) Morbidity, d) Incidence and severity of barotrauma. Generalizablity of the computerized decision support system will be determined by examining; 1) Percent of total time in the trial during which protocols controlled patient care. 2) Number of protocol instructions which were not followed. 3) Number of objections to protocol logic which, based on medical evidence, forced a change in the logic. To our knowledge this is the first prospective randomized clinical trial designed to test the impact of computerized critical care decision support on patient outcome. References: 1. East T, et al. Int J Clin Monit Comput 1992;8:263-269. 2. Morris AH, et al. Am Rev Respir Dis 1992;145(4):A184. 3. Pryor TA, et al. The HELP system development tools. In: Implementing health care information systems. New York: Springer-Verlag, 1989: 365-383. 4. Zapol WM, et al. The adult respiratory distress syndrome at Massachusetts General Hospital, Etiology progession and survival rates,1978-1988. In: Zapol WM, Lemaire F, ed. Adult Respiratory Distress Syndrome. New York: Marcel Dekker, Inc, 1991: 367-380. 5. Artigas A, et al. Clinical presentation, prognostic factors, and outcome of ARDS in the European Collaborative Study (1985-1987).In:Same book as ref 4, 1991: 37-63. Acknowledgements: This work was supported by NHLBI grant #HL36787, AHCPR grant HS06594, Siemens Ventilators, ACT/PC, the Respiratory Distress Syndrome Foundation and the Deseret Foundation (LDS Hospital). My biographical sketch: Thomas D. East,Ph.D.: Associate professor of anesthesiology, bioengineering and medical informatics at the University of Utah. Director of informatics research in the pulmonary division at LDS Hospital. MEBE and Ph.D. in bioengineering from the U of U. My research interests are in the applications of computers to critical care. In particular much of my work has been in the are of rule based decision support systems and knowledge engineering. ------------------------------------------------------------------------ The Utah State University Space Dynamics Laboratory, An Introduction Presented by Dr. J Steven Hansen (801-750-4850, jsh@sdl.usu.edu) Director, Instrument and Data Evaluation Center Space Dynamics Laboratory Associate Research Professor, Department of Electrical and Computer Engineering Department of Physics Utah State University, Logan, Utah Abstract This talk will provide description of the Space Dynamics Laboratory and its capabilities, particularly in the areas of data, signal, and image processing. The presentation will include a brief history of SDL over the past 30 years, an overview of the past and current projects at SDL and the Instrument and Data Evaluation Center (IDEC), a look at the current computing and image/signal processing capabilities IDEC. A look at the future and particular area of possible interest to the community will be addressed. ------------------------------------------------------------------------ Lunch: A group of us will go to lunch at a our own expense. If you wish to join that group, contact Jerome Soller to have a spot reserved. Directions to the VA Hospital: For directions, call 582-1565, and ask for learning resources. The address is 500 Foothill Boulevard Parking: The VA has limited parking for visitors behind building 8. If you come in the main entrance, make a right past building 1 and 14, continue through one of the staff parking lots. Make a right before reaching the staff parking lot between building 5 and 8. You should pass between buildings 8 and 9, and park behind building eight (not nine). Rough Map: Dash lines represent a desired path. Main Entrance:-- 1 | | 14 | 5 Don't Park | Don't Park Here Here | ----------| |---|Don't | Park | Here | 9 | 8 6 |___ Don't Park Don't Park Park Here Here Here It is advisable to have a map of the VA campus mailed to you, and that map and postage will be free of charge if Jerome Soller is notified within the next two weeks. If parking is tight, you may need to park nearby in Research Park (leave ample time for that). If you are at the University of Utah, you can catch a VA shuttle from the medical school or research park directly to the VA hospital. University shuttles run to university parking, which is across from the VA hospital, and to the LDS Insitute, which is also across the VA hospital. Registration: No registration is required. However, it would be greatly appreciated if you would rsvp by the end of April so we can estimate the attendance and set up the room accordingly. The room can comfortably seat 80 people with good workspace. Afterwards: We hope that speakers will provide some form of handouts. We hope to have a video tape record of this workshop, which will be public domain. *************************************************************************** >From POULSON@freud.sbs.utah.edu Fri Apr 16 10:50:57 1993 From: KIMBERLY POULSON MEMORANDUM TO: Faculty, Graduate Students, Auxiliary Faculty and other interested parties FROM: William Johnston/Charlie Shimp TOPIC: William F. Prokasy Lecture This year's William F. Prokasy Lecturer will be Dr. Irving Biederman. Dr. Biederman will speak on Tuesday, May 11, at 5:00 p.m. in BEH SCI 110. The title is "Shape Recognition in Mind and Brain." Dr. Irving Biederman is the William M. Keck Professor of Cognitive Neuroscience at the University of Southern California, where he is a member of the Departments of Psychology, Computer Science, and Neuroscience and Head of the Cognitive and Behavioral Neuroscience Program. Professor Biederman has proposed a theory of real-time human object recognition that posits that objects and scenes are represented as an arrangement of simple volumetric primitives, termed geons. This theory has undergone extensive assessment in psychophysical experiments. Recently, he has employed neural network models to provide a more biologically based version of the geon-assemblage theory which is currently undergoing tests through single unit recording experiments in monkeys and the study of the impairment of object recognition in patients with a variety of neurological symptoms. Prior to his recent appointment at USC, Dr. Biederman was the Fesler-Lampert Professor of Artificial Intelligence and Cognitive Science at the University of Minnesota. He has been a member of panels for the National Science Foundation, National Research Council, and the Air Force Office of Scientific Research, where he served as the first Program Manager (consulting) for the Cognitive Science Program. Please put these dates on your calendars.