Interpretation Of Medical Data Research Articles

Spectrogram-based assessment of small SNR variations, with application to medical electrodes

In this paper, the problem of detection of small signal-to-noise ratio (SNR) variations in noisy signals is addressed in order to provide an efficient and fast method for detection of faulty electroencephalogram (EEG) electrodes which can improve the interpretation of medical data. The method for slight SNR variation assessment, based on the estimation of the longest useful information cluster, is proposed as an alternative to commonly used estimators such as signal energy spectral density, spectral peaks, and spectrogram entropy, which exhibited limited reliability for the considered task. The method proposed in this paper is validated on real signals, which are resistance fluctuations of the EEG Corkscrew electrode solder connection, in which failure is typically manifested as a lower signal-to-noise ratio in the output signal, when compared to the valid electrode. In order to obtain a reliable criterion for the distinction of signals with slight SNR variations, a time-frequency method that relies on observation of the longest useful information cluster of data preserved after the K-means-based denoising application has been introduced. Based on the measurement of the longest existing stationary component, an expert system has been developed, which provides reliable failure detection method with detection accuracy of up to 97.6%. Results on real and simulated data show that the proposed method can be adopted as a computer-aided decision system in a wide range of applications requiring high sensitivity to slight variations of SNRs.

Marginal Utility of Additional Clinical Shifts on Milestone-Based Competency in an Emergency Medicine Clerkship

Background: 
 Emergency medicine clerkships are a required element of medical school programs. The optimal rotation structure is unknown, and in particular the number of clinical shifts required to achieve basic competency is unknown. In this analysis of one year of evaluations at an academic center, we assess the marginal utility of clinical shifts on competency, as assessed by historical preceptor milestone-based competency evaluations. 
 The goal of the experiment is to observe the trend of passing rates throughout the course of an EM rotation using competencies including medical knowledge, data interpretation, and clinical judgement. The null hypothesis is that the percentage of students meeting the developmental milestones does not increase throughout the length of the clerkship. 
 Methods: 
 Clerkship evaluations of 200 students were retrospectively examined. A short form grading rubric was used to score students across eight developmental milestones. The average percentage of students meeting the developmental milestones were calculated and analyzed over the course of 14 shifts. A one-way ANOVA was used to compare the mean passing rates at different times of the clerkship. 
 Results: 
 The average percent of students achieving basic competency increased throughout the length of the clerkship, e.g. medical knowledge competency passing rates increased from 44.2% to 56.0%, first to last shifts respectively. Similar trends were observed in other competencies. The one-way ANOVA gave a p-value of less than 0.05; the null hypothesis was rejected. 
 Conclusion/Potential Impact: 
 The results can aid clerkship directors improve current grading rubrics to better assess student competency in their EM clerkships.

Risk knowledge of people with relapsing-remitting multiple sclerosis - Results of an international survey.

BackgroundAdequate disease and treatment-related risk knowledge of people with Multiple Sclerosis (pwMS) is a prerequisite for informed choices in medical encounters. Previous work showed that MS risk knowledge is low among pwMS and role preferences are different in Italy and Germany.ObjectiveWe investigated the level of risk knowledge and role preferences in 8 countries and assessed putative variables associated with risk knowledge.MethodsAn online-survey was performed based on the Risk knowledge questionnaire for people with relapsing-remitting MS (RIKNO 2.0), the electronic Control Preference Scale (eCPS), and other patient questionnaires. Inclusion criteria of participants were: (1) age ≥18 years, (2) a diagnosis of relapsing-remitting MS (RRMS), (3) being in a decision making process for a disease modifying drug.ResultsOf 1939 participants from Germany, Italy, the Netherlands, Serbia, Spain and Turkey, 986 (51%) (mean age 38.6 years [range 18–67], 77% women, 7.8 years of disease duration) completed the RIKNO 2.0, with a mean of 41% correct answers. There were less than 50 participants in the UK and Estonia and data were not analysed. Risk knowledge differed across countries (p < 0.001). Variables significantly associated with higher risk knowledge were higher education (p < 0.001), previous experience with disease modifying drugs (p = 0.001), correct answer to a medical data interpretation question (p < 0.001), while higher fear for wheelchair dependency was negatively associated to risk knowledge (p = 0.001).ConclusionMS risk knowledge was overall low and differed across participating countries. These data indicate that information is an unmet need of most pwMS.

Subjective and objective knowledge and decisional role preferences in cerebrovascular patients compared to controls.

BackgroundRisk knowledge and active role preferences are important for patient involvement in treatment decision-making and adherence. Although knowledge about stroke warning signs and risk factors has received considerable attention, objective knowledge on secondary prevention and further self-esteem subjective knowledge have rarely been studied. The aim of our study was to investigate knowledge and treatment decisional role preferences in cerebrovascular patients compared to controls.MethodsWe performed a survey on subjective and objective stroke risk knowledge and autonomy preferences in cerebrovascular patients from our stroke outpatient clinic (n=262) and from pedestrians on the street taken as controls during a “World Stroke Day” (n=274). The questionnaire includes measures for knowledge and decisional role preferences from previously published questionnaires and newly developed measures, for example, subjective knowledge, revealed on a visual analog scale.ResultsThe overall stroke knowledge was low to moderate, with no differences between patients and controls. Knowledge about secondary prevention was particularly low. Only 10%–15% of participants correctly estimated the stroke absolute risk reduction potential of aspirin. The medical data interpretation competence was moderate in both groups. Age and basic mathematical and statistical understanding (numeracy) were the only independent predictors of objective stroke knowledge, whereas previous stroke had no impact on stroke knowledge. However, patients were thought to be better informed than controls. Approximately 60% of both patients and controls claimed to prefer a shared decision-making approach in treatment decisions.ConclusionThe level of stroke risk knowledge in patients with cerebrovascular diseases was as low as in randomly selected pedestrians, although patients felt better informed. Both groups preferred involvement in treatment decision-making. We conclude that educational concepts for increasing awareness of knowledge gaps as well as for stroke risk and for prevention strategies are needed.

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop.

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

Medical data preprocessing for increased selectivity of diagnosis

Abstract In this review, we present a framework that will enable us to obtain increased accuracy of computer diagnosis in medical patient checkups. To some extent, a new proposition for medical data analysis has been built based on medical data preprocessing. The result of such preprocessing is transformation of medical data from descriptive, semantic form into parameterized math form. A proper model for digging of hidden medical data properties is presented as well. Exploration of hidden data properties achieved by means of preprocessing creates new possibilities for medical data interpretation. Diagnosis selectivity has been increased by means of parameterized illnesses patterns in medical databases.

Application of fuzzy logic in medical data interpretation

This paper serves the purpose of presentation of a general view of the current applications of fuzzy logic in medicine and bioinformatics. Using fuzzy logic, we particularly review medical aspects. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). DOI: http://dx.doi.org/10.3329/bjsir.v49i3.22127 Bangladesh J. Sci. Ind. Res. 49(3), 137-146, 2014

Quilt Plots: A Simple Tool for the Visualisation of Large Epidemiological Data

BackgroundGraphical representation of data is one of the most easily comprehended forms of explanation. The current study describes a simple visualization tool which may allow greater understanding of medical and epidemiological data.MethodWe propose a simple tool for visualization of data, known as a “quilt plot”, that provides an alternative to presenting large volumes of data as frequency tables. Data from the Australian Needle and Syringe Program survey are used to illustrate “quilt plots”.ConclusionVisualization of large volumes of data using “quilt plots” enhances interpretation of medical and epidemiological data. Such intuitive presentations are particularly useful for the rapid assessment of problems in the data which cannot be readily identified by manual review. We recommend that, where possible, “quilt plots” be used along with traditional quantitative assessments of the data as an explanatory data analysis tool.

PP1.5 – 1899 Management and interpretation of medical data related to cerebral palsy: the ICT4 Rehab project

PP1.5 – 1899 Management and interpretation of medical data related to cerebral palsy: the ICT4 Rehab project

The Role of Numeracy and Intelligence in Health‐Risk Estimation and Medical Data Interpretation

ABSTRACTDespite ample evidence that numeracy is an important influence on patient understanding and use of health‐related information, there is a dearth of studies examining the concept's relationship to other individual differences measures that may underlie complex judgments in the health domain. In this study, we compared the relative contributions of selected extant numeracy measures and general intelligence and other measures to varied judgment and decision‐making outcomes. Two hundred participants completed numeracy items, subscales of the Wechsler Adult Intelligence Scales, the need for cognition scale, and four relevant outcome measures including risk estimation and medical data interpretation. A numeracy scale constructed using item response and confirmatory factor analyses was consistently the strongest predictor across all outcome measures and accounted for unique variance over and above general intelligence. The results support the concept of numeracy as an independent construct that merits consideration in patient communication. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Interpretation of Medical Imaging Data with a Mobile Application: A Mobile Digital Imaging Processing Environment

Digital Imaging Processing (DIP) requires data extraction and output from a visualization tool to be consistent. Data handling and transmission between the server and a user is a systematic process in service interpretation. The use of integrated medical services for management and viewing of imaging data in combination with a mobile visualization tool can be greatly facilitated by data analysis and interpretation. This paper presents an integrated mobile application and DIP service, called M-DIP. The objective of the system is to (1) automate the direct data tiling, conversion, pre-tiling of brain images from Medical Imaging NetCDF (MINC), Neuroimaging Informatics Technology Initiative (NIFTI) to RAW formats; (2) speed up querying of imaging measurement; and (3) display high-level of images with three dimensions in real world coordinates. In addition, M-DIP provides the ability to work on a mobile or tablet device without any software installation using web-based protocols. M-DIP implements three levels of architecture with a relational middle-layer database, a stand-alone DIP server, and a mobile application logic middle level realizing user interpretation for direct querying and communication. This imaging software has the ability to display biological imaging data at multiple zoom levels and to increase its quality to meet users’ expectations. Interpretation of bioimaging data is facilitated by an interface analogous to online mapping services using real world coordinate browsing. This allows mobile devices to display multiple datasets simultaneously from a remote site. M-DIP can be used as a measurement repository that can be accessed by any network environment, such as a portable mobile or tablet device. In addition, this system and combination with mobile applications are establishing a virtualization tool in the neuroinformatics field to speed interpretation services.

The Numeracy Understanding in Medicine Instrument

Health numeracy can be defined as the ability to understand and apply information conveyed with numbers, tables and graphs, probabilities, and statistics to effectively communicate with health care providers, take care of one's health, and participate in medical decisions. To develop the Numeracy Understanding in Medicine Instrument (NUMi) using item response theory scaling methods. A 20-item test was formed drawing from an item bank of numeracy questions. Items were calibrated using responses from 1000 participants and a 2-parameter item response theory model. Construct validity was assessed by comparing scores on the NUMi to established measures of print and numeric health literacy, mathematic achievement, and cognitive aptitude. Community and clinical populations in the Milwaukee and Chicago metropolitan areas. Twenty-nine percent of the 1000 respondents were Hispanic, 24% were non-Hispanic white, and 42% were non-Hispanic black. Forty-one percent had no more than a high school education. The mean score on the NUMi was 13.2 (s = 4.6) with a Cronbach α of 0.86. Difficulty and discrimination item response theory parameters of the 20 items ranged from -1.70 to 1.45 and 0.39 to 1.98, respectively. Performance on the NUMi was strongly correlated with the Wide Range Achievement Test-Arithmetic (0.73, P < 0.001), the Lipkus Expanded Numeracy Scale (0.69, P < 0.001), the Medical Data Interpretation Test (0.75, P < 0.001), and the Wonderlic Cognitive Ability Test (0.82, P < 0.001). Performance was moderately correlated to the Short Test of Functional Health Literacy (0.43, P < 0.001). The NUMi was found to be most discriminating among respondents with a lower-than-average level of health numeracy. The NUMi can be applied in research and clinical settings as a robust measure of the health numeracy construct.

Statistical interpretation of medical data of patients with alcohol abuse

Abstract An attempt is made to assess a set of biochemical, kinetic and anthropometric data for patients suffering from alcohol abuse (alcoholics) and healthy patients (non-alcoholics). The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. Multivariate statistical methods (cluster analysis and principal components analysis) were used to assess the data collection. Several significant patterns of related parameters were found to be representative of the role of the liver function, kinetic and anthropometric indicators (conditionally named “liver function factor”, “ethanol metabolism factor”, “body weight factor”, and “acetaldehyde metabolic factor”). An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel.

Intelligent Signal and Image Processing in eHealth

Intelligent Signal and Image Processing in eHealth

Framework for eliciting knowledge for a medical laboratory diagnostic expert system

Medical laboratory diagnostic report system in developing countries like Nigeria is awash with data that the medical expert is expected to correctly classify or interpret visually in consultation with medical literature. The advent of more system components (diseases, tests, et cetera) and growing pressure caused mostly by shortage of medical experts render a process that is time consuming, costly and unreliable. With proper knowledge elicitation, an expert system can automate the interpretation of medical diagnostic data based on knowledge elicited from human expert classifiers. There are numerous methods for eliciting expert knowledge, all aimed at ensuring that the data collected is valid and reliable. This paper proposes a framework for implementing an elicitation process aimed at extracting valuable knowledge (in production rules) required to build a medical laboratory diagnostic expert system expected to interpret medical laboratory test reports on areas of limited domains in medicine. We have implemented a prototype expert system that operationalizes this framework for tests on urine and blood samples. Preliminary results indicate that the framework is able to facilitate effective elicitation of parameters needed for a medical laboratory diagnostic experts system.

Quantitative Literacy Assessments: An Introduction to Testing Tests

This paper describes how professional evaluators construct assessment instruments that work properly to measure the right thing. Constructing an assessment tool begins with getting feedback from relevant experts on the content of questions. The tool is developed and refined through comparison with existing instruments, focus groups and cognitive interviews. The final instrument is formally tested for content validity, usability, reliability and construct validity through a variety of statistical measures. This process of construction is illustrated by two examples relevant to quantitative literacy: the Medical Data Interpretation Test and the Math Attitudes Survey.

Evaluating existing measures of health numeracy using item response theory

Objective To evaluate existing measures of health numeracy using item response theory (IRT). Methods A cross-sectional study was conducted. Participants completed assessments of health numeracy measures including the Lipkus expanded health numeracy scale (Lipkus), and the Medical Data Interpretation Test (MDIT). The Lipkus and MDIT were scaled with IRT utilizing the two-parameter logistic model. Results Three-hundred and fifty-nine (359) participants were surveyed. Classical test theory parameters and IRT scaling parameters of the numeracy measures found most items to be at least moderately discriminating. Modified versions of the Lipkus and MDIT were scaled after eliminating items with low discrimination, high difficulty parameters, and poor model fit. The modified versions demonstrated a good range of discrimination and difficulty as indicated by the test information functions. Conclusion An IRT analysis of the Lipkus and MDIT indicate that both health numeracy scales discriminate well across a range of ability. Practice implications Health numeracy skills are needed in order for patients to successfully participate in their medical care. The accurate assessment of health numeracy may help health care providers to tailor patient education interventions to the patient's level of understanding and ability. Item response theory scaling methods can be used to evaluate the discrimination and difficulty of individual items as well as the overall assessment.

Soft computing in medicine

Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL–NN), NN and GA (NN–GA) and FL and GA (FL–GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems. The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68% of FL–NN, 27% of NN–GA and 5% of FL–GA. So far, FL–NN methodology was significantly used in medicine. The rates of using FL–NN in clinical science, diagnostic science and basic science were found as %83, %71 and %48, respectively. On the other hand NN–GA and FL–GA methodologies were mostly preferred by basic science of medicine. Another message emerging from this survey is that the number of papers which used NN–GA methodology has continuously risen until today. Also search results put the case clearly that FL–GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.

Quantitative Literacy on the Web of Science, 2 - Mining the Health Numeracy Literature for Assessment Items

A topic search of the Web of Science (WoS) database using the term “numeracy” produced a bibliography of 293 articles, reviews and editorial commentaries (Oct 2008). The citation graph of the bibliography clearly identifies five benchmark papers (1995-2001), four of which developed numeracy assessment instruments. Starting with the 80 papers that cite these benchmarks, we identified a set of 25 papers (1995-2008) in which the medical research community reports the development and/or application of health-numeracy assessments. In all we found 10 assessment instruments from which we have compiled a total of 48 assessment items. There are both general and context-specific tests, with the wide range in the latter illustrated by names such as the Diabetes Numeracy Test and the Asthma Numeracy Questionnaire. There is also a Medical Data Interpretation Test and a Subjective Numeracy Scale. Much of this literature discusses the validity and reliability of the test, and many papers include item-by-item results of the tests from when they were applied in the research reported in the papers. The research that used the tests was directed at exploring such subjects as the patients’ ability to evaluate risks and benefits in order to make informed decisions; to understand and carry out instructions in order to self-manage their medical conditions; and, in research settings, to understand what the researchers were asking in their assessments (e.g., quantified quality of life) that require comparison of numerical information. We present the collection of items as a potential resource for educators interested in numeracy assessments in context.

Validating the Medical Data Interpretation Test in a Dutch population

Objective To validate the Dutch translation of the Medical Data Interpretation Test. Methods A test–retest design with a 2-week interval was used. Results The intraclass correlation coefficient (ICC = .82), the limits-of-agreement interval (LOA = −8.96 to 2.48) and the test–retest reliability (Pearson's r = 86) suggest that the Dutch translation has good reproducibility. Construct validity was tested by two hypotheses, both of which were confirmed. University participants had higher test scores than non-university participants ( p = .02), and males did not score differently than females ( p = .61). Conclusion The results suggest that the Dutch version of the Medical Data Interpretation Test is an adequate scale to assess ability to interpret medical data. Practice implications Assessing patients’ numeracy skills before a counseling session will enable the counselor to adjust subsequent communication accordingly and, as such, improve the session's effectiveness.