Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

The Dutch Individualised Care Scale for patients and nurses : a psychometric validation study

Aims and objectives: Translating and psychometrically assessing the Individualised Care Scale (ICS) for patients and nurses for the Flemish and Dutch healthcare context. Background: Individualised care interventions have positive effects on health outcomes. However, there are no valid and reliable instruments for evaluating individualised care for the Flemish and Dutch healthcare context. Design: Psychometric validation study. Setting and participants: In Flemish hospitals, data were collected between February and June 2016, and in Dutch hospitals, data were collected between December 2014 and May 2015. Nurses with direct patient contact and a working experience of minimum 6 months on the wards could participate. Patient inclusion criteria were being an adult, being mentally competent, having an expected hospital stay of minimum 1 day, and being able to speak and read the Dutch language. In total, 845 patients and 569 nurses were included. Methods: The ICS was translated into Dutch using a forward–backward translation process. Minimal linguistic adaptations to the Dutch ICS were made to use the scale as a Flemish equivalent. Omega, Cronbach’s Alpha, mean inter-item correlations and standardised subscale correlations established the reliability and confirmatory factor analysis the construct validity of the ICS. Results: Internal consistency using Omega (Cronbach’s Alpha) ranged from 0.83 to 0.96 (0.82–0.95) for the ICSNurse and from 0.88 to 0.96 (0.87–0.96) for the ICSPatient. Fit indices of the confirmatory factor analysis indicated a good model fit, except for the root mean square error of approximation, which indicated only moderate model fit. Conclusion: The Dutch version of the ICS showed acceptable psychometric performance, supporting its use for the Dutch and Flemish healthcare context. Relevance to clinical practice: Knowledge of nurses’ and patients’ perceptions on individualised care will aid to target areas in the Dutch and Flemish healthcare context in which work needs to be undertaken to provide individualised nursing care

Laparoscopy Pneumoperitoneum Fuzzy Modeling

Abstract: Gas volume to intra-peritoneal pressure fuzzy modeling for evaluating pneumoperitoneum in videolaparoscopic surgery is proposed in this paper. The proposed approach innovates in using fuzzy logic and fuzzy set theory for evaluating the accuracy of the prognosis value in order to minimize or avoid iatrogenic injuries due to the blind needle puncture. In so doing, it demonstrates the feasibility of fuzzy analysis to contribute to medicine and health care. Fuzzy systems is employed here in synergy with artificial neural network based on backpropaga tion, multilayer perceptron architecture for building up numerical functions. Experimental data employed for analysis were collected in the accomplishment of the pneumoperitoneum in a random population of patients submitted to videolaparoscopic surgeries. Numerical results indicate that the proposed fuzzy mapping for describing the relation from the intra peritoneal pressure measures as function injected gas volumes succeeded in determinining a fuzzy model for this nonlinear system when compared to the statistical model

Medical analysis and diagnosis by neural networks

In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system. Keywords: Statistical Classification, Adaptive Prediction, Neural Networks, Neurofuzzy, Medical System

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770