A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations

Analyzing the Performance of the Fuzzy Inference System in Decision Making

Inference systems that are fuzzy, It is common practise to make use of models such as the Mamdani and Sugeno models in order to take into consideration the presence of uncertainty and imprecision in the decision-making process. MATLAB is a well-known programming environment that provides persons who are interested in developing and implementing fuzzy inference systems with the necessary tools and strategies to accomplish their goals. In order to evaluate Diabetes Mellitus (DM), the Mamdani and Sugeno fuzzy inference systems have been developed in MATLAB. This abstract provides a brief summary of how the evaluation was carried out.The Mamdani model provides a description of uncertain data through the utilisation of fuzzy sets and is founded on language standards. Through the utilisation of the Fuzzy Logic Toolbox, users of MATLAB are able to rapidly construct and simulate Mamdani fuzzy systems. Membership functions, fuzzy rule sets, simulations, and Mamdani system optimisations can all be defined and created by users without any restrictions that are placed on them. The visualisation options that are available in MATLAB, such as the surface plot and the rules plot, help to make the behaviour of the system more understandable.For the purpose of producing inferences and predictions, the Sugeno model, also known as the Takagi-Sugeno-Kang (TSK) model, combines fuzzy principles with linear calculations. It is possible to implement Sugeno fuzzy systems by utilising the Fuzzy Logic Toolbox that is included in MATLAB. The user is able to create the linear functions that are associated to each rule after the information regarding the input-output relationships has been specified through the utilisation of linguistic variables and membership functions. Evaluation, simulation, and visualisation of the rule surfaces and output response curves of Sugeno fuzzy systems can be accomplished in MATLAB in a short amount of time. To summarise, the Mamdani and Sugeno fuzzy inference systems are capable of being constructed in an efficient manner by utilising MATLAB. There is software available for rapid system modelling, simulation, and analysis applications. The techniques of fuzzy logic that are available in MATLAB can be utilised by both professionals and academics in order to address the issue of uncertainty and imprecision in decision-making processes

PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease

Intelligent decision support systems for optimised diabetes

Computers now pervade the field of medicine extensively; one recent innovation is the development of intelligent decision support systems for inexperienced or non-specialist pbysicians, or in some cases for use by patients. In this thesis a critical review of computer systems in medicine, with special reference to decision support systems, is followed by a detailed description of the development and evaluation of two new, interacting, intelligent decision support systems in the domain of diabetes. Since the discovery of insulin in 1922, insulin replacement therapy for the treatment of diabetes mellitus bas evolved into a complex process; there are many different formulations of insulin and much more information about the factors which affect patient management (e.g. diet, exercise and progression of complications) are recognised. Physicians have to decide on the most appropriate anti-diabetic therapy to prescribe to their patients. Insulin-treated patients also have to monitor their blood glucose and decide how much insulin to inject and when to inject it. In order to help patients determine the most appropriate dose of insulin to take, a simple-to-use, hand-held decision support system has been developed. Algorithms for insulin adjustment have been elicited and combined with general rules of therapy to offer advice for every dose. The utility of the system has been evaluated by clinical trials and simulation studies. In order to aid physician management, a clinic-based decision support system has also been developed. The system provides wide-ranging advice on all aspects of diabetes care and advises an appropriate therapy regimen according to individual patient circumstances. Decisions advised by the pbysician-related system have been evaluated by a panel of expert physicians and the system has undergone informal primary evaluation within the clinic setting. An interesting aspect of both systems is their ability to provide advice even in cases where information is lacking or uncertain

Coupling computer-interpretable guidelines with a drug-database through a web-based system – The PRESGUID project

BACKGROUND: Clinical Practice Guidelines (CPGs) available today are not extensively used due to lack of proper integration into clinical settings, knowledge-related information resources, and lack of decision support at the point of care in a particular clinical context. OBJECTIVE: The PRESGUID project (PREScription and GUIDelines) aims to improve the assistance provided by guidelines. The project proposes an online service enabling physicians to consult computerized CPGs linked to drug databases for easier integration into the healthcare process. METHODS: Computable CPGs are structured as decision trees and coded in XML format. Recommendations related to drug classes are tagged with ATC codes. We use a mapping module to enhance computerized guidelines coupling with a drug database, which contains detailed information about each usable specific medication. In this way, therapeutic recommendations are backed up with current and up-to-date information from the database. RESULTS: Two authoritative CPGs, originally diffused as static textual documents, have been implemented to validate the computerization process and to illustrate the usefulness of the resulting automated CPGs and their coupling with a drug database. We discuss the advantages of this approach for practitioners and the implications for both guideline developers and drug database providers. Other CPGs will be implemented and evaluated in real conditions by clinicians working in different health institutions

Semantic web system for differential diagnosis recommendations

There is a growing realization that healthcare is a knowledge-intensive field. The ability to capture and leverage semantics via inference or query processing is crucial for supporting the various required processes in both primary (e.g. disease diagnosis) and long term care (e.g. predictive and preventive diagnosis). Given the wide canvas and the relatively frequent knowledge changes that occur in this area, we need to take advantage of the new trends in Semantic Web technologies. In particular, the power of ontologies allows us to share medical research and provide suitable support to physician's practices. There is also a need to integrate these technologies within the currently used healthcare practices. In particular the use of semantic web technologies is highly demanded within the clinicians' differential diagnosis process and the clinical pathways disease management procedures as well as to aid the predictive/preventative measures used by healthcare professionals

Variation in diabetes care by age: opportunities for customization of care

BACKGROUND: The quality of diabetes care provided to older adults has usually been judged to be poor, but few data provide direct comparison to other age groups. In this study, we hypothesized that adults age 65 and over receive lower quality diabetes care than adults age 45–64 years old. METHODS: We conducted a cohort study of members of a health plan cared for by multiple medical groups in Minnesota. Study subjects were a random sample of 1109 adults age 45 and over with an established diagnosis of diabetes using a diabetes identification method with estimated sensitivity 0.91 and positive predictive value 0.94. Survey data (response rate 86.2%) and administrative databases were used to assess diabetes severity, glycemic control, quality of life, microvascular and macrovascular risks and complications, preventive care, utilization, and perceptions of diabetes. RESULTS: Compared to those aged 45–64 years (N = 627), those 65 and older (N = 482) had better glycemic control, better health-related behaviors, and perceived less adverse impacts of diabetes on their quality of life despite longer duration of diabetes and a prevalence of cardiovascular disease twice that of younger patients. Older patients did not ascribe heart disease to their diabetes. Younger adults often had explanatory models of diabetes that interfere with effective and aggressive care, and accessed care less frequently. Overall, only 37% of patients were simultaneously up-to-date on eye exams, foot exams, and glycated hemoglobin (A1c) tests within one year. CONCLUSION: These data demonstrate the need for further improvement in diabetes care for all patients, and suggest that customisation of care based on age and explanatory models of diabetes may be an improvement strategy that merits further evaluation

Focal Spot, Spring 2006

https://digitalcommons.wustl.edu/focal_spot_archives/1102/thumbnail.jp

Analytics of the Cost Effectiveness and Real-Time Health Risk Assessments for Pharmacy-Based Preventive Health Services

More patient-centered care and participation in managing chronic illnesses are features of the modern pharmacist\u27s task, which has evolved substantially in recent years. Still, the issue of whether or not pharmacists are cost-effective in diabetes care persists. Cost-effectiveness analyses (CEA) have emerged as a crucial tool when making informed decisions about healthcare delivery. Patients\u27 present health status, past medical history (personal and family), and lifestyle variables influencing their health are all part of a health risk assessment (HRA). Hence, this study proposed a Cost-Effectiveness Analysis of Pharmacy-based Preventive Health Services (CEA-PBPHS) model for real-time health risk assessment. Nowadays, medical professionals place a greater emphasis on preventative health care. This research aims to examine the potential impact of risk assessment tools on males past due for a physical examination and evaluate the return on investment (ROI) for community pharmacists who provide this service for free. More cost-efficient or cost-saving than conventional treatment is pharmacist engagement in diabetes management due to better glucose control, higher patient compliances, and lower risks of medication-related issues. The experimental outcomes demonstrate that the suggested CEA-PBPHS model increases the health risk assessment ratio by 98.9%, the personalized health service ratio by 97.5% and the cost-effectiveness analysis ratio by 98.3% compared to other existing models