Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Soteria Svmme Reverendo Ac Perillvstri Domino Bernhardo Pflvgio Ordinis Ioannaei Eqvite, Et Hevkewaldae Dominom Serenissimi Ac Potentissimi Regios Pologniae, Electoris Qve Saxoniae A Consiliis Intimis, Vt Et Cvriae Apvd Nos Provincialis Praeside E Gravisssimo Morbo Evadente In Academia Ienensi Pie Persolvta A. R. S. M DCC XI.

SOTERIA SVMME REVERENDO AC PERILLVSTRI DOMINO BERNHARDO PFLVGIO ORDINIS IOANNAEI EQVITE, ET HEVKEWALDAE DOMINOM SERENISSIMI AC POTENTISSIMI REGIOS POLOGNIAE, ELECTORIS QVE SAXONIAE A CONSILIIS INTIMIS, VT ET CVRIAE APVD NOS PROVINCIALIS PRAESIDE E GRAVISSSIMO MORBO EVADENTE IN ACADEMIA IENENSI PIE PERSOLVTA A. R. S. M DCC XI. Soteria Svmme Reverendo Ac Perillvstri Domino Bernhardo Pflvgio Ordinis Ioannaei Eqvite, Et Hevkewaldae Dominom Serenissimi Ac Potentissimi Regios Pologniae, Electoris Qve Saxoniae A Consiliis Intimis, Vt Et Cvriae Apvd Nos Provincialis Praeside E Gravisssimo Morbo Evadente In Academia Ienensi Pie Persolvta A. R. S. M DCC XI. ([1]rgef

Tractatio Synoptica De Iuramentis

Quam, Auxiliante Deo, Occasione Lib. XII. Digest. Tit. II. Praeside Wolfgang-Adamo Lauterbach ... Ad diem 5. & 12. April. ... Publico examini submittunt Bernhardus & Georgius Dietericus Pflugk/ Fratres, Equites MisniciNicht identisch mit VD17 12:148025H; dort letzte Zeile auf Bl. A: "§. 118." (Fingerprint

A transcriptomic approach to investigate the phylogeny of Dytiscoidea (Coleoptera: Adephaga)

info:eu-repo/semantics/publishe

Phylogenomic analyses clarify the pattern of evolution of Adephaga (Coleoptera) and highlight phylogenetic artefacts due to model misspecification and excessive data trimming

info:eu-repo/semantics/publishe

The ALFA (Activity Log Files Aggregation) Toolkit: A Method for Precise Observation of the Consultation

Background: There is a lack of tools to evaluate and compare Electronic patient record (EPR) systems to inform a rational choice or development agenda. Objective: To develop a tool kit to measure the impact of different EPR system features on the consultation. Methods: We first developed a specification to overcome the limitations of existing methods. We divided this into work packages: (1) developing a method to display multichannel video of the consultation; (2) code and measure activities, including computer use and verbal interactions; (3) automate the capture of nonverbal interactions; (4) aggregate multiple observations into a single navigable output; and (5) produce an output interpretable by software developers. We piloted this method by filming live consultations (n = 22) by 4 general practitioners (GPs) using different EPR systems. We compared the time taken and variations during coded data entry, prescribing, and blood pressure (BP) recording. We used nonparametric tests to make statistical comparisons. We contrasted methods of BP recording using Unified Modeling Language (UML) sequence diagrams. Results: We found that 4 channels of video were optimal. We identified an existing application for manual coding of video output. We developed in-house tools for capturing use of keyboard and mouse and to time stamp speech. The transcript is then typed within this time stamp. Although we managed to capture body language using pattern recognition software, we were unable to use this data quantitatively. We loaded these observational outputs into our aggregation tool, which allows simultaneous navigation and viewing of multiple files. This also creates a single exportable file in XML format, which we used to develop UML sequence diagrams. In our pilot, the GP using the EMIS LV (Egton Medical Information Systems Limited, Leeds, UK) system took the longest time to code data (mean 11.5 s, 95% CI 8.7-14.2). Nonparametric comparison of EMIS LV with the other systems showed a significant difference, with EMIS PCS (Egton Medical Information Systems Limited, Leeds, UK) (P = .007), iSoft Synergy (iSOFT, Banbury, UK) (P = .014), and INPS Vision (INPS, London, UK) (P = .006) facilitating faster coding. In contrast, prescribing was fastest with EMIS LV (mean 23.7 s, 95% CI 20.5-26.8), but nonparametric comparison showed no statistically significant difference. UML sequence diagrams showed that the simplest BP recording interface was not the easiest to use, as users spent longer navigating or looking up previous blood pressures separately. Complex interfaces with free-text boxes left clinicians unsure of what to add. Conclusions: The ALFA method allows the precise observation of the clinical consultation. It enables rigorous comparison of core elements of EPR systems. Pilot data suggests its capacity to demonstrate differences between systems. Its outputs could provide the evidence base for making more objective choices between systems. Keywords: Video recordings, process assessment, observation, attitude to computer, professional-patient relations, general practice, family practice, decision modeling, process assessment, medical informatics, computers, medical records systems, computerized, electronic patient record (EPR), electronic medical record (EMR), evaluation methodologies, usabilit

Automatic identification of variables in epidemiological datasets using logic regression

Background: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies