Data wars over data stores: challenges in medical data linkage

A primary concern of the medical e-research community is the availability of suitable data sets for their analysis requirements. The quantity and dubious quality of data present significant barriers to the application of many automated analysis technologies, including data mining, to the medical and health domain. Publicly available data is frequently poorly coded, incomplete, out-of-date or simply not applicable to the analysis or algorithm being applied. Work has been done to overcome these issues through the application of data linking processes but further complications have been encountered resulting in slow progress. The use of locally held medical data is difficult enough due to its structural complexity and non-standardised language, however linking data from disparate electronic sources adds the challenges of privacy, security, semantic compatibility, provenance, and governance, each with its own inherent issues. A focal requirement is a mechanism for the sharing of medical and health data across multiple sites which incorporates careful management of the semantics and limitations of the data sets whilst maintaining functional relevance for the end user. Our paper addresses this requirement by exploring recent conceptual modeling and data evaluation methodologies that facilitate effective data linking whilst ensuring the semantics of the data are maintained and the individual needs of the end user are met

Discovering itemset interactions

Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering item-set interactions, a task which cannot be undertaken by current association mining techniques

CATS II long-term anthropometric and metabolic effects of maternal sub-optimal thyroid function in offspring and mothers

Context and Objectives The Controlled Antenatal Thyroid Screening Study I (CATS-I) was a randomized controlled trial investigating the effects of levothyroxine therapy for suboptimal gestational thyroid function (SGTF), comparing outcomes in children of treated (SGTF-T) with untreated (SGTF-U) women during pregnancy. This follow-up study, CATS-II, reports the long-term effects on anthropometric, bone, and cardiometabolic outcomes in mothers and offspring and includes a group with normal gestational thyroid function (NGTF). Design & Participants 332 mothers (197 NGTF, 56 SGTF-U, 79 SGTF-T) aged 41.2±5.3 years (mean±SD) and 326 paired children assessed 9.3±1.0 years after birth for (i) body mass index (BMI); (ii) lean, fat, and bone mass by dual-energy X-ray absorptiometry; (iii) blood pressure, augmentation index, and aortic pulse-wave-velocity; and (iv) thyroid function, lipids, insulin, and adiponectin. The difference between group means was compared using linear regression. Results Offspring’s measurements were similar between groups. Although maternal BMI was similar between groups at CATS-I, after 9 years (at CATS-II) SGTF-U mothers showed higher BMI (median [interquartile ratio] 28.3 [24.6-32.6] kg/m2) compared with NGTF (25.8 [22.9-30.0] kg/m2; P = 0.029), driven by fat mass increase. At CATS-II SGTF-U mothers also had higher thyroid-stimulating hormone (TSH) values (2.45 [1.43-3.50] mU/L) than NGTF (1.54 [1.12-2.07] mU/L; P = 0.015), since 64% had never received levothyroxine. At CATS-II, SGTF-T mothers had BMI (25.8 [23.1-29.8] kg/m2, P = 0.672) and TSH (1.68 [0.89-2.96] mU/L; P = 0.474) values similar to NGTF mothers. Conclusions Levothyroxine supplementation of women with SGTF did not affect long-term offspring anthropometric, bone, and cardiometabolic measurements. However, absence of treatment was associated with sustained long-term increase in BMI and fat mass in women with SGTF

Reconceptualising interestingness metrics for medical data mining

Data mining researchers have long been concerned with the application of tools to automate data analysis and the improvement of analysis systems for use on large data sets. A more recent and larger challenge is to make current data mining and knowledge discovery systems applicable to a wider range of domains, among them medicine. Much of the early work was performed over transactional, retail based data sets, but the attraction of finding previously unknown knowledge from data collected and held in other domains, including medicine, is an area of growing interest and specialisation. The problem is finding a solution which is suitably flexible to allow for cross domain application whilst being specific enough to provide functionality which caters for the nuances of each domain. This paper discusses the progress to date, identifies some of the significant issues and outlines our research in one of these issues- the determination of what constitutes an interesting discovery in an area as complex as medicine.

Optimizing Operational-Level Forest Biomass Logistic Costs for Storage, Chipping and Transportation through Roadside Drying

Forest biomass (FB) could supply more of Australia’s energy needs, but delivered costs must be reduced for it to be a viable energy source. Operational planning is critical to reducing delivered costs as it determines actual activities, though few operational FB supply chain (FBSC) planning tools have been published. This paper presents a “proof-of-concept” operational FBSC decision support system (DSS) to schedule FB deliveries for eight weeks from roadside storage for the least cost, taking in account moisture content changes. Four mathematical models are compared, solving a linear formulation of the FB delivery problem in terms of solution speed and delivered cost, and the practicality of implementing the solutions. The best performing model was a Greedy algorithm as it produced solutions not significantly different from those of the tested linear programming solver and was readily modified to significantly improve solution implementation through the addition of a non-linear element. FBSC planning tools typically assume accurate knowledge of stored FB quantities and that little or no rainfall occurs during storage. In practice, stored FB quantity estimates can be inaccurate due to variation in the bulk density of the piles. Improving these estimates is a critical area for future research. This study found that simulated rainfall with <20 mm during the first week of the scheduled period did not significantly effect delivered costs

Discovering Itemset Interactions

Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering itemset interactions, a task which cannot be undertaken by current association mining techniques