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Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

Organizing and accessing biomedical big data will require quite different business models, say Philip E. Bourne, Jon R. Lorsch and Eric D. Green.

Background Cochrane systematic reviews collate and summarise studies of the effects of healthcare interventions. The characteristics of these reviews and the meta-analyses and individual studies they contain provide insights into the nature of healthcare research and important context for the development of relevant statistical and other methods. Methods We classified every meta-analysis with at least two studies in every review in the January 2008 issue of the Cochrane Database of Systematic Reviews ( CDSR ) according to the medical specialty, the types of interventions being compared and the type of outcome. We provide descriptive statistics for numbers of meta-analyses, numbers of component studies and sample sizes of component studies, broken down by these categories. Results We included 2321 reviews containing 22,453 meta-analyses, which themselves consist of data from 112,600 individual studies (which may appear in more than one meta-analysis). Meta-analyses in the areas of gynaecology, pregnancy and childbirth (21%), mental health (13%) and respiratory diseases (13%) are well represented in the CDSR . Most meta-analyses address drugs, either with a control or placebo group (37%) or in a comparison with another drug (25%). The median number of meta-analyses per review is six (inter-quartile range 3 to 12). The median number of studies included in the meta-analyses with at least two studies is three (inter-quartile range 2 to 6). Sample sizes of individual studies range from 2 to 1,242,071, with a median of 91 participants. Discussion It is clear that the numbers of studies eligible for meta-analyses are typically very small for all medical areas, outcomes and interventions covered by Cochrane reviews. This highlights the particular importance of suitable methods for the meta-analysis of small data sets. There was little variation in number of studies per meta-analysis across medical areas, across outcome data types or across types of interventions being compared.

Treatment decisions for hypertension using sphygmomanometer based measurements and the current clinical practice paradigm do not account for the timing of blood pressure (BP) measurement. This study aimed to evaluate the clinical implications of discordance between measured and true BP, to quantify BP misclassification rate at a typical clinical visit in current clinical practice, and to propose a BP calibration system to decrease the impact of timing of BP measurement. A clinical trial simulation case study was performed using an in silico Monte Carlo Simulation approach. The time-courses of BPs with and without an antihypertensive treatment effect were simulated from a baseline BP model combined with an antihypertensive PK/PD model. Virtual subject characteristics were sampled from the FDA internal database. The baseline BP model was qualified using observed 24 h ambulatory BP monitoring (ABPM) data from 225 subjects by a visual predictive check as well as a global sensitivity analysis. First of all, our results showed that the measured cuff BP in current typical clinical practice deviated from the true values. (1) Cuff BP deviated from the true values by more than 5 mmHg in 57 % (95 % CI: 54–61 %) of patients and more than 10 mmHg in 26 % (95 % CI: 22–32 %) of patients respectively. (2) These discordances were reduced to 28 % (deviation ≥5 mmHg, 95 % CI: 18–40 %) and 9 % (deviation ≥10 mmHg, 95 % CI: 4–18%) of patients assuming perfect sphygmomanometer measurement and thus represent the contribution of ignoring the daily circadian rhythm of BP. Secondly, our results showed 23–32 % of patients were misclassified to an incorrect BP category for a casual clinical visit based on JNC 7 guideline. In addition, the accuracy of the measured cuff BP varied by time of clinic visit. Specifically, 11:00 AM to 3:00 PM was identified to be the better time frame, while times before 9:00 AM were the worst time frame. Therefore, clinic visit time may need to be adjusted accordingly. Finally, we proposed an easy BP calibration method for clinic use to adjust for time of day differences due to circadian variability in case that the desirable clinic visit time cannot be tailored for practical reasons.

Study designMapping of the National Spinal Cord Injury Model System (SCIMS) Database (NSCID) to the International Classification of Functioning, Disability and Health (ICF).ObjectivesTo link the content of the latest two versions of the NSCID to the ICF; more specifically (1) to compare the content of the current NSCID 2016–2021 version to its predecessor (NSCID 2011–2016) using the ICF as a neutral reference framework, and (2) to compare the content contained in the NSCID 2016–2021 version with relevant ICF Sets.SettingThe forms of the NSCID 2016–2021 and 2011–2016 versions were linked to the ICF and contrasted. Comparability of the current version of the NSCID with the ICF Core Set for Spinal Cord Injury (SCI) in the post-acute and long-term context and the two generic ICF sets— ICF Generic-7 and ICF Generic-30 was then examined.MethodsICF Linking Rules and descriptive statistics.ResultsThe current NSCID 2016–2021 version covers functioning as classified in the ICF with 8 ICF categories more comprehensively than its predecessor does. More than 50% of ICF categories contained in the two ICF Generic Sets were covered. The coverage of the brief ICF Core Sets for SCI by the NSCID 2016–2021 was more than 50%, but the coverage of the comprehensive core sets was low. Results showed the best coverage in the ICF component Activities and Participation.ConclusionsThis study emphasizes how the ICF and its Sets can serve as a reference framework to foster comparability of existing data sets from both clinical practice and research.

Background The National Hospital Discharge Survey (NHDS) and the Nationwide Inpatient Sample (NIS) collect sample data and publish annual estimates of inpatient care in the United States, and both are commonly used in orthopaedic research. However, there are important differences between the databases, and because of these differences, asking these two databases the same question may result in different answers. The degree to which this is true for arthroplasty-related research has, to our knowledge, not been characterized. Question/purposes We tested the following null hypotheses: (1) there are no differences between the NHDS and NIS in patient characteristics, comorbidities, and adverse events in patients with hip osteoarthritis treated with THA, and (2) there are no differences between databases in factors associated with inpatient mortality, adverse events, and length of hospital stay after THA. Methods The NHDS and NIS databases use different methods of data collection and weighting to provide data representative of all nonfederal hospital discharges in the United States. In 2006 the NHDS database contained 203,149 patients with hip arthritis treated with hip arthroplasty, and the NIS database included 193,879 patients. Multivariable analyses for factors associated with inpatient mortality, adverse events, and days of care were constructed for each database. Results We found that 26 of 42 of the factors in demographics, comorbidities, and adverse events after THA in the NIS and NHDS databases differed more than 10%. Age and days of care were associated with inpatient mortality with the NHDS and the NIS although the effect rates differ more than 10%. The NIS identified seven other factors not identified by the NHDS: wound complications, congestive heart failure, new mental disorder, chronic pulmonary disease, dementia, geographic region Northeast, acute postoperative anemia, and sex, that were associated with inpatient mortality even after controlling for potentially confounding variables. For inpatient adverse events, atrial fibrillation, osteoporosis, and female sex were associated with the NHDS and the NIS although the effect rates differ more than 10%. There were different directions for sources of payment, dementia, congestive heart failure, and geographic region. For longer length of stay, common factors differing more than 10% in effect rate included chronic pulmonary disease, atrial fibrillation, complication not elsewhere classified, congestive heart failure, transfusion, discharge nonroutine compared with routine, acute postoperative anemia, hypertension, wound adverse events, and diabetes mellitus, whereas discrepant factors included geographic region, payment method, dementia, sex, and iatrogenic hypotension. Conclusions Studies that use large databases intended to be representative of the entire United States population can produce different results, likely related to differences in the databases, such as the number of comorbidities and procedures that can be entered in the database. In other words, analyses of large databases can have limited reliability and should be interpreted with caution. Level of Evidence Level II, prognostic study. See the Instructions for Authors for a complete description of levels of evidence.

Thanks to the confluence of genome sequencing and bioinformatics, the number of metabolic databases has expanded from a handful in the mid-1990s to several thousand today. These databases lie within distinct families that have common ancestry and common attributes. The main families are the MetaCyc, KEGG, Reactome, Model SEED, and BiGG families. We survey these database families, as well as important individual metabolic databases, including multiple human metabolic databases. The MetaCyc family is described in particular detail. It contains well over 1,000 databases, including highly curated databases for Escherichia coli , Saccharomyces cerevisiae , Mus musculus , and Arabidopsis thaliana . These databases are available through a number of web sites that offer a range of software tools for querying and visualizing metabolic networks. These web sites also provide multiple tools for analysis of gene expression and metabolomics data, including visualization of those datasets on metabolic network diagrams and over-representation analysis of gene sets and metabolite sets.

Rationale Little is known about the longitudinal changes associated with using the 2013 update of the multidimensional GOLD strategy for chronic obstructive pulmonary disease (COPD). Objective To determine the COPD patient distribution of the new GOLD proposal and evaluate how this classification changes over one year compared with the previous GOLD staging based on spirometry only. Methods We analyzed data from the CHAIN study, a multicenter observational Spanish cohort of COPD patients who are monitored annually. Categories were defined according to the proposed GOLD: FEV 1 %, mMRC dyspnea, COPD Assessment Test (CAT), Clinical COPD Questionnaire (CCQ), and exacerbations-hospitalizations. One-year follow-up information was available for all variables except CCQ data. Results At baseline, 828 stable COPD patients were evaluated. On the basis of mMRC dyspnea versus CAT, the patients were distributed as follows: 38.2% vs. 27.2% in group A, 17.6% vs. 28.3% in group B, 15.8% vs. 12.9% in group C, and 28.4% vs. 31.6% in group D. Information was available for 526 patients at one year: 64.2% of patients remained in the same group but groups C and D show different degrees of variability. The annual progression by group was mainly associated with one-year changes in CAT scores (RR, 1.138; 95%CI: 1.074-1.206) and BODE index values (RR, 2.012; 95%CI: 1.487-2.722). Conclusions In the new GOLD grading classification, the type of tool used to determine the level of symptoms can substantially alter the group assignment. A change in category after one year was associated with longitudinal changes in the CAT and BODE index.

Background Use of administrative claims from multiple sources for research purposes is challenged by the lack of consistency in the structure of the underlying data and definition of data across claims data providers. This paper evaluates the impact of applying a standardized revenue code-based logic for defining inpatient encounters across two different claims databases. Methods We selected members who had complete enrollment in 2012 from the Truven MarketScan Commercial Claims and Encounters (CCAE) and the Optum Clinformatics (Optum) databases. The overall prevalence of inpatient conditions in the raw data was compared to that in the common data model (CDM) with the standardized visit definition applied. Results In CCAE, 87.18% of claims from 2012 that were classified as part of inpatient visits in the raw data were also classified as part of inpatient visits after the data were standardized to CDM, and this overlap was consistent from 2006 to 2011. In contrast, Optum had 83.18% concordance in classification of 2012 claims from inpatient encounters before and after standardization, but the consistency varied over time. The re-classification of inpatient encounters substantially impacted the observed prevalence of medical conditions occurring in the inpatient setting and the consistency in prevalence estimates between the databases. On average, before standardization, each condition in Optum was 12% more prevalent than that same condition in CCAE; after standardization, the prevalence of conditions had a mean difference of only 1% between databases. Amongst 7,039 conditions reviewed, the difference in the prevalence of 67% of conditions in these two databases was reduced after standardization. Conclusions In an effort to improve consistency in research results across database one should review sources of database heterogeneity, such as the way data holders process raw claims data. Our study showed that applying the Observational Medical Outcomes Partnership (OMOP) CDM with a standardized approach for defining inpatient visits during the extract, transfer, and load process can decrease the heterogeneity observed in disease prevalence estimates across two different claims data sources.

Considerable attention is being paid to hospital readmission as a marker of poor postdischarge care coordination. However, little is known about another potential marker: emergency department (ED) use. We examined ED visits for Medicare patients within thirty days of discharge for six common inpatient surgeries. We found that these visits were widespread and showed extensive variation across facilities. For example, 17.3 percent of these patients experienced at least one ED visit within the postdischarge period, and 4.4 percent of patients had multiple ED visits. Among those patients who were readmitted, 56.5 percent were readmitted from the ED. There was substantial variation-as much as fourfold-in hospital-level ED use for these patients across all six procedures. The variation might signify a failure in upstream coordination of care and therefore might represent a novel hospital quality indicator. In addition, the postdischarge ED visit is an opportunity to ensure that care is properly coordinated and is the last best chance to avoid preventable readmissions. [PUBLICATION ABSTRACT]