CWDM: A Case-based Diabetes Management Web System

Managing diabetes using intelligent techniques is a recent priority for healthcare information systems and the medical domain. Diabetes is one of the most widespread diseases around the world including Australia. Numerous intelligent systems supporting diabetes management (DM) have been widely deployed, yet how to effectively develop a DM system integrating intelligent techniques remains a big issue. Case-based reasoning (CBR), as an intelligent technique, has been applied in various fields including customer services, medical diagnosis, and clinical treatment. This paper proposes a case-based lifecycle for DM consisting of case-based symptoms, case-based diagnosis, case-based prognosis, case-based treatment, and case-based care. The lifecycle is integrated with a web-based system in which CBR functions as an intelligent intermediary. The approach proposed in this research might facilitate research and development of diabetes management, healthcare information systems and intelligent systems

Artificial intelligence methodologies and their application to diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers?doctors and nurses?in this field

The Admissibility of Differential Diagnosis Testimony to Prove Causation in Toxic Tort Cases: The Interplay of Adjective and Substantive Law

This article uses the differential diagnosis opinions to explore a pair of interrelationships. The basic causal framework employed by most courts in toxic tort cases is presented. A key to understanding the developing case law in this area is to appreciate the degree to which the courts have adopted the interpretive conventions of science in assessing admissibility

Evaluating team-based, lecture-based, and hybrid learning methods for neurology clerkship in China: a method-comparison study

BACKGROUND: Neurology is complex, abstract, and difficult for students to learn. However, a good learning method for neurology clerkship training is required to help students quickly develop strong clinical thinking as well as problem-solving skills. Both the traditional lecture-based learning (LBL) and the relatively new team-based learning (TBL) methods have inherent strengths and weaknesses when applied to neurology clerkship education. However, the strengths of each method may complement the weaknesses of the other. Combining TBL with LBL may produce better learning outcomes than TBL or LBL alone. We propose a hybrid method (TBL + LBL) and designed an experiment to compare the learning outcomes with those of pure LBL and pure TBL. METHODS: One hundred twenty-seven fourth-year medical students attended a two-week neurology clerkship program organized by the Department of Neurology, Sun Yat-Sen Memorial Hospital. All of the students were from Grade 2007, Department of Clinical Medicine, Zhongshan School of Medicine, Sun Yat-Sen University. These students were assigned to one of three groups randomly: Group A (TBL + LBL, with 41 students), Group B (LBL, with 43 students), and Group C (TBL, with 43 students). The learning outcomes were evaluated by a questionnaire and two tests covering basic knowledge of neurology and clinical practice. RESULTS: The practice test scores of Group A were similar to those of Group B, but significantly higher than those of Group C. The theoretical test scores and the total scores of Group A were significantly higher than those of Groups B and C. In addition, 100% of the students in Group A were satisfied with the combination of TBL + LBL. CONCLUSIONS: Our results support our proposal that the combination of TBL + LBL is acceptable to students and produces better learning outcomes than either method alone in neurology clerkships. In addition, the proposed hybrid method may also be suited for other medical clerkships that require students to absorb a large amount of abstract and complex course materials in a short period, such as pediatrics and internal medicine clerkships

Role of the neurologist in hazard identification and risk assessment.

This review describes strategies used by a clinical neurologist in the investigation of neurotoxic disease. It emphasizes the need for a high level of suspicion that environmental substances are capable of producing impairments in neurologic and neurobehavioral functions. Because of the difficulties in differentiating neurotoxic from nonneurotoxic disease when presented with common neurological symptoms, it is necessary to rely upon corroborative evidence from past medical records, work and environmental histories, and exposure data, as well as detailed neurological examinations, to reach a conclusion about causation. Sensitive electrophysiologic and neuropsychologic test batteries are useful in identifying subclinical impairments and in providing objective confirmation of abnormalities in the central and peripheral nervous systems. Combining scientific and epidemiologic information with experience and clinical judgment, these sources of information are used in the formulation of a clinical diagnosis. When many patients among a group of people are exposed to neurotoxicants, the effects of the exposure may vary from one to another because of differences in susceptibility, duration of exposure and dosage of neurotoxicant, and other possible risk factors. Group statistics may obscure a significant effect for the larger group, despite clinically obvious effects in an individual. The neurologist applies clinical skills and refers to the accumulated neurotoxicologic literature as a frame of reference to make a diagnosis about an individual patient or a group of patients who have been exposed to particular neurotoxicants. The Boston University Environmental Neurology Assessment (BUENA) is a scheme that attempts to combine epidemiologic methodology and clinical approaches to detect effects of neurotoxic exposure. The advantages and limitations of such a strategy are discussed

Predictive Capability of an iPad-Based Medical Device (medx) for the Diagnosis of Vertigo and Dizziness

Background:Making the correct diagnosis of patients presenting with vertigo and dizziness in clinical practice is often challenging. Objective:In this study we analyzed the usage of the iPad based program medx in the prediction of different clinical vertigo and dizziness diagnoses . We examined the power of medx to distinguish between different vertigo diagnoses. Patients and methods:The data collection was done in the outpatient clinic of the German Center of Vertigo and Balance Disorders. The “gold standard diagnosis” was defined as the clinical diagnosis of the specialist during the visit of the patient standardized history and clinical examination. Another independent and blinded physician finalized each patient’s case in constellatory diagnostic of medx by entering all available clinical information in the system. The accuracy, sensitivity, specificity as well as positive and negative predictive values for the most common diagnoses were determined. Sixteen possible different vertigo and dizziness diagnoses could be provided by medx constellatory diagnostic system. These diagnoses were compared to the “gold standard” by retrospective review of the charts of the patients over the study period. Results:610 patients (mean age58.1±16.3 years, 51.2 female) were included. The accuracy for the most common diagnoses was between 82.1- 96.6 with a sensitivity from 40- 80.5 and a specificity of more than 80. When analyzing the quality of medx in a multiclass-problem for the six most common clinical diagnoses the sensitivity, specificity, positive and negative predictive value were as follows: Bilateral vestibulopathy (81.6, 97.1, 71.1, 97.5), Menière's disease (77.8, 97.6, 87., 95.3), benign paroxysmal positional vertigo (61.7, 98.3, 86.6, 93.4), downbeat nystagmus syndrome (69.6, 97.7, 71.1, 97.5), vestibular migraine (34.7, 97.8, 76.1, 88.3) and phobic postural vertigo (80.5, 82,5, 52.5, 94.6), Conclusions:This study demonstrates that medx is a new and easy approach to screen for different diagnoses. With the high specificity and high negative predictive value the system helps to rule out differential diagnoses and can therefore also lead to a cost reduction in health care system. However, the sensitivity was unexpectedly low, especially for vestibular migraine. All in all, this device can only be a complementary tool, in particular for non-experts in the field

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment

Cognitive Neuro-Fuzzy Expert System for Hypotension Control

Hypotension; also known as low blood sugar affect gender of all sort; hypotension is a relative term because the blood pressure normally varies greatly with activity, age, medications, and underlying medical conditions.  Low blood pressure can result from conditions of the nervous system, conditions that do not begin in the nervous system and drugs. Neurologic conditions (condition affecting the brain neurons) that can lead to low blood pressure include changing position from lying to more vertical (postural hypotension), stroke, shock, lightheadedness after urinating or defecating, Parkinson's disease, neuropathy and simply fright. Clinical symptoms of hypotension include low blood pressure, dizziness, Fainting, clammy skin, visual impairment and cold sweat. Neuro-Fuzzy Logic explores approximation techniques from neural networks to find the parameter of a fuzzy system. In this paper, the traditional procedure of the medical diagnosis of hypotension employed by physician is analyzed using neuro-fuzzy inference procedure. The proposed system which is self-learning and adaptive is able to handle the uncertainties often associated with the diagnosis and analysis of hypotension. Keywords: Neural Network, Fuzzy logic, Neuro Fuzzy System, Expert System, Hypotensio