Mining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework

(1) Background: We aimed to develop a transparent machine-learning (ML) framework to automatically identify patients with a condition from electronic health records (EHRs) via a parsimonious set of features. (2) Methods: We linked multiple sources of EHRs, including 917,496,869 primary care records and 40,656,805 secondary care records and 694,954 records from specialist surgeries between 2002 and 2012, to generate a unique dataset. Then, we treated patient identification as a problem of text classification and proposed a transparent disease-phenotyping framework. This framework comprises a generation of patient representation, feature selection, and optimal phenotyping algorithm development to tackle the imbalanced nature of the data. This framework was extensively evaluated by identifying rheumatoid arthritis (RA) and ankylosing spondylitis (AS). (3) Results: Being applied to the linked dataset of 9657 patients with 1484 cases of rheumatoid arthritis (RA) and 204 cases of ankylosing spondylitis (AS), this framework achieved accuracy and positive predictive values of 86.19% and 88.46%, respectively, for RA and 99.23% and 97.75% for AS, comparable with expert knowledge-driven methods. (4) Conclusions: This framework could potentially be used as an efficient tool for identifying patients with a condition of interest from EHRs, helping clinicians in clinical decision-support process

Fuzzy Set Theory in Medicine

Fuzzy set theory has a number of properties that make it suitable for formalizing the uncertain information upon which medical diagnosis and treatment is usually based. Firstly, it allows us to define inexact medical entities as fuzzy sets. Secondly, it provides a linguistic approach with an excellent approximation to texts. Finally, fuzzy logic offers powerful reasoning methods capable of drawing approximate inferences. These facts suggest that fuzzy set theory might be a suitable basis for the development of a computerized diagnosis and treatment-recommendation system. This is borne out by trials performed with the medical expert system CADIAG-2, which uses fuzzy set theory to formalize medical relationships

Demonstration of Semantic Web-based Medical Ontologies and Clinical Decision Support Systems

Master's thesis in Information- and communication technology IKT590 - University of Agder 2016Konfidensiell til / confidential until 01.01.202

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Health Risks of Ionizing Radiation: An Overview of Epidemiological Studies

A Report by the Community-Based Hazard Management Program, George Perkins Marsh Institute, Clark University. The health risks of exposure to low levels of ionizing radiation are disputed within the scientific community. Risks associated with exposure to high levels of radiation are widely accepted and well documented based primarily on the studies of the atomic bomb survivors in Nagasaki and Hiroshima. Some feel that the best way to estimate risk for low- level exposures is to extrapolate from higher doses, although there is some clear evidence of low-dose risk. In this overview we have attempted to give an unbiased summary of the available research with an emphasis on the lower doses. The strengths and weaknesses of the studies are explained in order to help assess the variety of sometimes conflicting evidence. This research was completed money allocated during Round 6 of the Citizens’ Monitoring and Technical Assessment Fund (MTA Fund). Clark University was named conservator of these works. If you have any questions or concerns please contact us at [email protected]://commons.clarku.edu/clark_mtafund/1004/thumbnail.jp

Etiological pattern, clinical course, complications and visual prognosis of paediatric uveitis in South India

INTRODUCTION: Uveitis is an intraocular inflammatory disease caused by disorders of various etiologies which include both infectious and immune-mediated disorders. The causes of uveitis vary in different populations depending upon the ecological, racial and socio-economic variation of the population studied. Tropical countries are unique in their climate, prevailing pathogens and in the existing diseases which further influence the epidemiological and geographic distribution of specific entities. The incidence of uveitis is 20 per 100,000 in the population per year, resulting in a prevalence of about 200 per 100,000 in the population 4,5. A recent study of the prevalence of uveitis in an urban population in Hyderabad, South India, found evidence of either past or active uveitis in 1 of 140 people in the population6 suggesting that the prevalence of uveitis may be at an order of magnitude higher in developing than in developed nations. AIMS OF THE STUDY: To study the 1. Etiological pattern of paediatric uveitis, 2. Clinical course of paediatric uveitis, 3. Complications and Visual outcome of paediatric uveitis. MATERIALS AND METHODS: All uveitic patients presenting to the uvea department Aravind Eye Hospital Madurai of < 17 yrs of age between February 2011 – January 2012 will be included in our study. Inclusion criteria: Age _ 16 yrs at the time of diagnosis. Patients in whom follow-up is possible. Exclusion criteria: Age more than 16 years of age. Patients in whom follow-up is not possible. (Our exclusion criteria is broad because most of the systemic conditions will itself be an unique entity in uveitis ) Setting: University affiliated teaching center attached to a community based eye hospital offering primary to tertiary care. Centre: Aravind Eye Hospital, Madurai. Department: Uvea clinic and Uveitis services, Aravind Eye Hospital, Madurai. Methods: Demographic data were gathered including age, gender, occupation, history of exposure to contaminated environment and place of residence. Uveitis history included the ocular symptoms, details on disease severity, laterality, chronicity, course of illness, response to therapy, associated systemic conditions, precipitating events and number of episodes or recurrences. A complete ocular and systemic history were obtained from these patients. The usual systemic history covers joint problem, skin disease, respiratory disease, neurological disease, gastrointestinal disease, mouth and genital ulcers. History was followed by complete ocular and systemic examination. CONCLUSION: Delayed diagnosis, extended burden of disease and blindness over a lifetime, limited treatment options, complicated examinations, and the risk of amblyopia are special challenges unique to paediatric uveitis. Even though the incidence and prevalence of uveitis in children is low they are more prone to develop sight threatening complications of uveitis than adults. In addition, children are more vulnerable to various sides effects such as corticosteroid induced growth retardation in prepubescent children and an increased tendency for corticosteroids to induce ocular hypertension and cataracts. In our population, analysis of 105 eyes of 78 patients showed that anterior uveitis is the most common, followed by intermediate, panuveitis and posterior uveitis. The most common cause of anterior uveitis was tubercular etiology, that of intermediate uveitis was idiopathic. DUSN was common cause of posterior uveitis, while endogenous endophthalmitis is the most common cause of pan uveitis. River water granuloma, a cause of anterior uveitis has not been reported in other studies and seems to be endemic to this area. When a patient presents with an anterior chamber granuloma and gives history of contact with river or pond water, we have to suspect river water granuloma. It usually has a good prognosis

Sociotechnical Safeguards for Genomic Data Privacy

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information