Data mining approach to estimate the duration of drug therapy from longitudinal electronic medical records

Background: Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research. Objectives: To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality. Methods: Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists. Results: At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level. Conclusion: The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.Publisher PDFPeer reviewe

Performance evaluation of binary classifier using Relative Cost Curve

Maģistra darbs ir veltīts klasifikācijas algoritmu precizitātes novērtēšanas metodēm gadījumā, kad disklasifikācijas sekas atšķiras starp klasēm. Aprakstītas izmaksu līkņu un Brajera līkņu metodes. Galvenie rezultāti izstrādāti relatīvo izmaksu līkņu pieejai. Bināra klasifikatora precizitātes skalārai novertēšanai piedāvāts apskatīt laukumu virs līknes. Ir aprakstīts kā paplašināt metodi vairāku klašu problēmai, ar pieņēmumu, ka sagaidāmās izmaksas ir atkarīgas tikai no īstās klases disklasifikācijas. Atslēgas vārdi: klasifikācija, ROC līkne, izmaksu līkne, Brajera līkne, relatīvo izmaksu līkne.Master Thesis is devoted to methods of performance evaluation of classifier under unequal misclassification costs. Cost Curves and Brier Curves methods are described. Main results are developed for Relative Cost Curves technique. The concept of Area Above the Curve is introduced as scalar measure of classifier performance. Relative Cost Curves technique is extended to multicategory problems when misclassification costs depend only on the true class. Keywords: classification, ROC curve, Cost Curve, Brier Curve, Relative Costs Curve

An agregation approach for solving bilevel linear programming problems

Diplomdarbs ir veltīts divu līmeņu lineārās programmēšanas uzdevumam gadījumā, kad augšējā līmenī ir viena mērķa funkcija un apakšējā līmenī - vairākas. Par darba pamatu ir ņemts nestriktas matemātikas algoritms optimāla plāna noteikšanai, meklējot kompromisu starp augšējā un apakšējā līmeņa mērķa funkciju optimālām vērtībām. Uzdevuma risinājuma rezultāts ir atkarīgs no algoritmā izvēlētajiem parametriem. Diplomdarbā ir izstrādāta algoritma parametru analīzes metode, kura izmanto speciāli šim mērķim uzdotu faktoragregācijas operatoru. Darba praktiskā daļa satur optimālās izvietošanas problēmu, kuras risināšanas shēma ir ilustrēta ar skaitlisko piemēru. Atslēgas vārdi: Lineārā programmēšana, vairāku mērķa funkciju lineārās programmēšanas uzdevums, divu līmeņu lineārās programmēšanas uzdevums, agregācijas operators, visparinātais agregācijas operators.The thesis is devoted to bi-level linear programming problem with a single objective function on the upper level and multiple objective functions on the lower level. As initial point was taken fuzzy algorithm for finding optimal solutions by choosing compromises between upper and lower level functions' optimal values. The solution of the problem depends on the algorithm's parameters. Specially constructed quotient aggregation is applied in algorithm's parameters analysis method, which has been developed in the thesis. Practical part of the thesis contains optimal location problem, of which a solution scheme is illustrated by numerical example. Keywords: Linear programming, multi-objective linear programming problem, bilevel linear programming problem, aggregation operator, general aggregation operator

Kochen mit Tim Mälzer : ein Vergleich zwischen öffentlich - rechtlichem und privatem Sender

Diese Bachelorarbeit befasst sich mit der Fragestellung, inwieweit sich öffentlich-rechtliche und private Fernsehsendungen gleicher Thematik in Bezug auf Anspruch und Konzeption unterscheiden. Es soll zudem verdeutlicht werden, warum sich der jeweilige Sender für dieses Format entschieden hat. Am Beispiel von „Schmeckt nicht, gibt’s nicht!“ (VOX) und „Tim Mälzer kocht!“ (Das Erste) werden diese Punkte praktisch untersucht

Weight gain in insulin-treated patients by body mass index category at treatment initiation: new evidence from real-world data in patients with type 2 diabetes

AimsTo evaluate, in patients with type 2 diabetes (T2DM) treated with insulin, the extent of weight gain over 2 years of insulin treatment, and the dynamics of weight gain in relation to glycaemic achievements over time according to adiposity levels at insulin initiation.Materials and methodsPatients with T2DM (n = 155 917), who commenced insulin therapy and continued it for at least 6 months, were selected from a large database of electronic medical records in the USA. Longitudinal changes in body weight and glycated haemoglobin (HbA1c) according to body mass index (BMI) category were estimated.ResultsPatients had a mean age of 59 years, a mean HbA1c level of 9.5%, and a mean BMI of 35 kg/m2 at insulin initiation. The HbA1c levels at insulin initiation were significantly lower (9.2-9.4%) in the obese patients than in patients with normal body weight (10.0%); however, the proportions of patients with HbA1c >7.5% or >8.0% were similar across the BMI categories. The adjusted weight gain fell progressively with increasing baseline BMI category over 6, 12 and 24 months (p 2 to a 0.32 kg loss in those with a BMI > 40 kg/m2.ConclusionsDuring 24 months of insulin treatment, obese patients gained significantly less body weight than normal-weight and overweight patients, while achieving clinically similar glycaemic benefits. These data provide reassurance with regard to the use of insulin in obese patients

Data mining approach to estimate the duration of drug therapy from longitudinal electronic medical records

Background: Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research.Objectives: To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality. Methods: Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists.Results: At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level.Conclusion: The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended