Advantages of large medical record database for outcomes research: Insights into post‐menopausal hormone therapy

Approximately 25 years ago, our team initiated studies to determine whether outcome results from a large medical record database would yield valid results. We utilized the data in the United Kingdom (UK) General Practice Research Database (GPRD) to replicate the randomized controlled trial (RCT) study result and compared them to confirm the database results. The initial studies compared favorably, but some subsequent studies did not. This prompted development of a new strategy to determine and correct for unrecognized confounding in the database. This strategy divided outcome rates prior to initiation of therapy in the database study (which should include both identified and unidentified confounders) into the outcome rates during the treatment interval. When they differed from Cox‐adjusted results, it reflected unrecognized confounding. We called this strategy Prior Event Rate Ratio (PERR)–adjusted outcome.One of our previously published observational studies replicated the Women’s Health Initiative (WHI) RCT study of hormone therapy in post‐menopausal women. Our study results replicated the WHI RCT results except it did not exhibit an increase in heart attack in contrast to the RCT. Furthermore, we could not evaluate death reliably since our analytic approach to overcome unrecognized confounding does not work for this outcome. In Volume 1, Issue 1 of the Learning Health Systems open access journal, we published a new study (titled “A new method to address unmeasured confounding of mortality in observational studies”) that reported a novel death method, based on our prior methodology, that could analyze unrecognized confounding of the death outcome. This new methodology, termed Post Treatment Event Rate Ratio (PTERR), permitted a reliable examination of mortality in post‐menopausal women undergoing hormone therapy. These results are reported in this manuscript. The study used the data from our previous observational study. It demonstrates that estrogen therapy markedly reduced death in post‐menopausal women.This work also illuminates principles of database construction and correspondingly demonstrates the use of novel methodologies for obtaining valid results, which can be applied to enable learning from such databases. Work to advance such methodologies is essential to advancing the scientific integrity Core Value underpinning learning health systems (LHSs). Indeed, in the absence of such efforts to develop and refine methodologies for learning trustworthy lessons from real‐world data, we risk inadvertently creating mis‐learning systems.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150513/1/lrh210193.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150513/2/lrh210193_am.pd

Rail-freight crew scheduling with a genetic algorithm

peer reviewedThis article presents a novel genetic algorithm designed for the solution of the Crew Scheduling Problem (CSP) in the rail-freight industry. CSP is the task of assigning drivers to a sequence of train trips while ensuring that no driver’s schedule exceeds the permitted working hours, that each driver starts and finishes their day’s work at the same location, and that no train routes are left without a driver. Real-life CSPs are extremely complex due to the large number of trips, opportunities to use other means of transportation, and numerous government regulations and trade union agreements. CSP is usually modelled as a set-covering problem and solved with linear programming methods. However, the sheer volume of data makes the application of conventional techniques computationally expensive, while existing genetic algorithms often struggle to handle the large number of constraints. A genetic algorithm is presented that overcomes these challenges by using an indirect chromosome representation and decoding procedure. Experiments using real schedules on the UK national rail network show that the algorithm provides an effective solution within a faster timeframe than alternative approaches

Amoxicillin-Clavulanate-Induced Liver Injury

Background and Aims Amoxicillin–clavulanate (AC) is the most frequent cause of idiosyncratic drug-induced injury (DILI) in the US DILI Network (DILIN) registry. Here, we examined a large cohort of AC-DILI cases and compared features of AC-DILI to those of other drugs. Methods Subjects with suspected DILI were enrolled prospectively, and cases were adjudicated as previously described. Clinical variables and outcomes of patients with AC-DILI were compared to the overall DILIN cohort and to DILI caused by other antimicrobials. Results One hundred and seventeen subjects with AC-DILI were identified from the cohort (n = 1038) representing 11 % of all cases and 24 % of those due to antimicrobial agents (n = 479). Those with AC-DILI were older (60 vs. 48 years, P < 0.001). AC-DILI was more frequent in men than women (62 vs. 39 %) compared to the overall cohort (40 vs. 60 %, P < 0.001). The mean time to symptom onset was 31 days. The Tb, ALT, and ALP were 7 mg/dL, 478, and 325 U/L at onset. Nearly all liver biopsies showed prominent cholestatic features. Resolution of AC-DILI, defined by return of Tb to <2.5 mg/dL, occurred on average 55 days after the peak value. Three female subjects required liver transplantation, and none died due to DILI. Conclusion AC-DILI causes a moderately severe, mixed hepatocellular–cholestatic injury, particularly in older men, unlike DILI in general, which predominates in women. Although often protracted, eventual apparent recovery is typical, particularly for men and usually in women, but three women required liver transplantation

Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem

AbstractThis article presents a fuzzy-logic controlled genetic algorithm designed for the solution of the crew-scheduling problem in the rail-freight industry. This problem refers to the assignment of train drivers to a number of train trips in accordance with complex industrial and governmental regulations. In practice, it is a challenging task due to the massive quantity of train trips, large geographical span and significant number of restrictions. While genetic algorithms are capable of handling large data sets, they are prone to stalled evolution and premature convergence on a local optimum, thereby obstructing further search. In order to tackle these problems, the proposed genetic algorithm contains an embedded fuzzy-logic controller that adjusts the mutation and crossover probabilities in accordance with the genetic algorithm’s performance. The computational results demonstrate a 10% reduction in the cost of the schedule generated by this hybrid technique when compared with a genetic algorithm with fixed crossover and mutation rates

Laser vision : lidar as a transformative tool to advance critical zone science

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hydrology and Earth System Sciences 19 (2015): 2881-2897, doi:10.5194/hess-19-2881-2015.Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demonstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.The workshop forming the impetus for this paper was funded by the National Science Foundation (EAR 1406031). Additional funding for the workshop and planning was provided to S. W. Lyon by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT grant no. 2013-5261). A. A. Harpold was supported by an NSF fellowship (EAR 1144894)