ARTMAP-IC and Medical Diagnosis: Instance Counting and Inconsistent Cases

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking algorithm (MT+), the new algorithm (MT-) better approximates the real-time network differential equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pima Indian diabetes, breast cancer, heart disease, and gall bladder removal. ARTMAP-IC results arc equal to or better than those of logistic regression, K nearest neighbor (KNN), the ADAP perceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP dynamics are fast, stable, and scalable. A voting strategy improves prediction by training the system several times on different orderings of an input set. Voting, instance counting, and distributed representations combine to form confidence estimates for competing predictions.National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-95-J-0409, N00014-95-0657

PC based storage and processing of electrocardiogram tracings recorded with a HP4745A pagewriter II cardiograph

ThesisCurrently the Department of Cardiology, Universitas Hospital, keeps paper copies of ECGs filed in large filing cabinets. Access to these files is tedious during office hours, and impossible after hours, when the filing room is locked and no filing personnel are available. Commercially available systems for computerised storage of ECG data are available from a number of vendors. Some drawbacks of these systems include: • Extremely expensive. • Only a portion of the functions offered by these systems are really needed at the Department of Cardiology, Universitas Hospital. These systems are thus not economically justifiable by the Department of Cardiology, Universitas Hospital. • Some require new/different ECG machines to be used. • Some require an expensive computer system to be installed. • Additional space is needed for additional equipment. • Staff needs to be extensively trained to use the new equipment. This dissertation describes the development of a dynamic link library (DLL) which is used to acquire and decode data from a Hewlet Packard HP4745A Cardiograph II Page Writer electrocardiograph. Furthermore, the database application using the HP4745A DLL can also be expanded to accept data from other ECG machines. The acquisition and decoding DLL must be developed to produce a decoded data file conforming to the format described in this dissertation. By storing these decoded data in a database such as Hearts 32, the data can be reprocessed (drawing of ECG traces on screen or on printer). Selected leads from different ECGs can also be plotted on the same screen. Fast access to previous ECGs will help the cardiologists at the Universitas Hospital in Bloemfontein to improve patient care. The cardiac patients of the Free State community as well as the staff at the Department of Cardiology, Universitas Hospital, Bloemfontein can benefit from the results of this research

Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention