Artificial diagnosis of sensory taints due to brettanomyces spp. contamination in Valpolicella wines

Diagnosis and intervention to avoid Brett taints in the product can be a time-consuming task for the enologist in large production facilities and an instrumental and automated detection systems assisting the local expert technician would be desirable. This paper investigates whether electronic noses, which have been tested in other wine making and classification tasks, can be of use in detecting Brett taints in Valpolicella wines

Clinical Decision Diagnosis Support System for Complementary and Alternative Medicine Practitioners in Lifestyle-related Diseases Management

Chronic diseases accounted for 60% of all deaths – corresponding to a projected 36.65 million deaths worldwide in 2007. 2.8% of the world population suffers from diabetes mellitus and it may cross 5.4% by the year 2025. Hypertension is a major burden on health care. Prevalence of lifestyle-related diseases increases. Low accessibility to and non-affordability of orthodox medicine by rural dwellers and their need to keep healthy to be economically productive have led to their dependence on medicinal plants to remedy afflictions. Complementary and Alternative Medicine (CAM) attracts patronage due to patients’ dissatisfaction with conventional health care, a desire for treatment and care that work, good relationship with practitioner, provision of information, a desire for greater control over one’s health, and a desire for cultural and philosophical congruence with personal beliefs about health and illness. Medicinal plants’ threatened sustainability makes adulteration and species’ substitutions reduce their efficacy, quality and safety. It was found that CAM practitioners who participated in this study relied heavily upon knowledge that had 'stood the test of time' (traditional theory and practice) and 'that which worked' (experientially based knowledge) as the basis for clinical decision-making. The safe, effective and efficient delivery of client care is informed primarily by sound clinical decision making. Body mass index (BMI) plays a significant role in the process. Strategies that guide practitioners through the process of decision making may not only foster professional excellence in CAM practice, but also help to improve the quality of client care. Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. CAM practitioners of modern time need currency and timeliness on computations of patients’ body mass index, waist circumference and body shape combination; product/therapy data on therapeutic efficacy; product quality and safety; adverse reactions and herb-drug interactions. This paper presents a clinical decision diagnosis system supporting CAM practitioners to effectively treat emerging lifestyle-related diseases with medicinal plants. Keywords: body mass index, complementary and alternative medicine, lifestyle-related diseases, medicinal plants, clinical decision support syste

Warranted Diagnosis

A diagnostic process is an investigative process that takes a clinical picture as input and outputs a diagnosis. We propose a method for distinguishing diagnoses that are warranted from those that are not, based on the cognitive processes of which they are the outputs. Processes designed and vetted to reliably produce correct diagnoses will output what we shall call ‘warranted diagnoses’. The latter are diagnoses that should be trusted even if they later turn out to have been wrong. Our work is based on the recently developed Cognitive Process Ontology and further develops the Ontology of General Medical Science. It also has applications in fields such as intelligence, forensics, and predictive maintenance, all of which rely on vetted processes designed to secure the reliability of their outputs

Learning approximate diagnosis

Model-based diagnosis (MBD) provides several advantages over experiential rule-based systems. A principal shortcoming of MBD is that MBD learns nothing from any given example. An MBD system facing the same task a second time will incur the same computational effort as that incurred the first time. Our earlier work on incorporating explanation-based learning (EBL) in MBD [4] suggested a diagnostic architecture integrating EBL and MBD components. In this architecture, EBL was used to learn diagnostic rules. But the diagnoses proposed by the rules could be erroneous. So constraint suspension testing was used to check all proposed diagnoses. Insisting on perfect accuracy causes the performance of this scheme for "learning while doing" to deteriorate rapidly with the size of the device to be diagnosed. In this paper, we describe a method for trading off accuracy for efficiency. In this approach, most diagnosis problems are handled by the associational rules learned from previous problems. Model-based reasoning and learning are activated only when performance drops below a given threshold. We present empirical results on circuits of increasing number of components illustrating how this approach scales up

Diagnosis of South Asia Specific Diseases

Asian StudiesHindi Urdu FlagshipSouth Asia Institut

Robust Fault Diagnosis by Optimal Input Design for Self-sensing Systems

This paper presents a methodology for model based robust fault diagnosis and a methodology for input design to obtain optimal diagnosis of faults. The proposed algorithm is suitable for real time implementation. Issues of robustness are addressed for the input design and fault diagnosis methodologies. The proposed technique allows robust fault diagnosis under suitable conditions on the system uncertainty. The designed input and fault diagnosis techniques are illustrated by numerical simulation.Comment: Accepted in IFAC World Congress 201