In the drive to increase quality and decrease health care costs for elderly populations, providers are collecting data on outcomes of medical procedures to guide decision making, identify opportunities for process improvements, and demonstrate the value of interventions. The key barrier to using this collected information is the lack of powerful, user-friendly programs to facilitate data analysis. Conventional statistical analysis applications have proven inadequate to identify important patterns that may be hidden in the recorded demographic procedural, and health outcomes data elements. Also, the application of conventional statistical techniques is difficult because of the enormous combination of recorded variables. Mandala Sciences (MSI) proposes to apply proprietary neural networks (NN) techniques to develop user-friendly data analysis systems which will facilitate exploration of complex datasets for outcome studies. The novel MSI approach is based on transformation of a decision tree into a layered NN. After training, this special NN can also be used to generate a fixed set of expert system compatible rules suitable for utilization in guiding clinical decision making and employment in public health population studies. To demonstrate concept feasibility, MSI will focus on hip replacement data that has been collected by the Henry Ford Health System.
| Schwartz, M H; Ward, R E; Macwilliam, C et al. (1997) Using neural networks to identify patients unlikely to achieve a reduction in bodily pain after total hip replacement surgery. Med Care 35:1020-30 |
