Entity

Time filter

Source Type

Allenstown Elementary School, NH, United States

Davis M.A.,Dartmouth College | Higgins J.,Collaboratory for Healthcare and Biomedical Informatics | Li Z.,Childrens Environmental Health and Disease Prevention Research Center at Dartmouth | Gilbert-Diamond D.,Childrens Environmental Health and Disease Prevention Research Center at Dartmouth | And 3 more authors.
Environmental Health: A Global Access Science Source | Year: 2015

Background: Early life exposure to arsenic is associated with decreased birth weight in highly exposed populations but little is known about effects of low-level arsenic exposure on growth in utero. Methods: Using a sample of 272 pregnancies from New Hampshire we obtained biometric measurements directly from fetal ultrasound reports commonly found in electronic medical records. We used information extraction methods to develop and validate an automated approach for mining biometric measurements from the text of clinical reports. As a preliminary analysis, we examined associations between in utero low-level arsenic exposure (as measured by maternal urinary arsenic concentration) and fetal growth measures (converted to Z-scores based on reference populations for estimated fetal weight, head, and other body measures) at approximately 18 weeks of gestation. Results: In a preliminary cross-sectional analysis of 223 out of 272 pregnancies, maternal urinary arsenic concentration (excluding arsenobetaine) was associated with a reduction in head circumference Z-score (Spearman correlation coefficient, rs = -0.08, p-value = 0.21) and a stronger association was observed among female fetuses at approximately 18 weeks of gestation (rs = - 0.21, p-value < 0.05). Although, associations were attenuated in adjusted analyses - among female fetuses a 1 μg/L increase in maternal urinary arsenic concentration was associated with a decrease of 0.047 (95% CI: -0.115, 0.021) in head circumference and 0.072 (95% CI: -0.151, 0.007) decrease in biparietal head diameter Z-score. Conclusions: Our study demonstrates that useful data can be extracted directly from electronic medical records for epidemiologic research. We also found evidence that exposure to low-level arsenic may be associated with reduced head circumference in a sex dependent manner that warrants further investigation. © 2015 Davis et al.; licensee BioMed Central. Source


Davis M.A.,Childrens Environmental Health and Disease Prevention Research Center at Dartmouth | Davis M.A.,Dartmouth College | Davis M.A.,University of Michigan | Higgins J.,Collaboratory for Healthcare and Biomedical Informatics | And 8 more authors.
Environmental Health: A Global Access Science Source | Year: 2015

Background: Early life exposure to arsenic is associated with decreased birth weight in highly exposed populations but little is known about effects of low-level arsenic exposure on growth in utero. Methods: Using a sample of 272 pregnancies from New Hampshire we obtained biometric measurements directly from fetal ultrasound reports commonly found in electronic medical records. We used information extraction methods to develop and validate an automated approach for mining biometric measurements from the text of clinical reports. As a preliminary analysis, we examined associations between in utero low-level arsenic exposure (as measured by maternal urinary arsenic concentration) and fetal growth measures (converted to Z-scores based on reference populations for estimated fetal weight, head, and other body measures) at approximately 18 weeks of gestation. Results: In a preliminary cross-sectional analysis of 223 out of 272 pregnancies, maternal urinary arsenic concentration (excluding arsenobetaine) was associated with a reduction in head circumference Z-score (Spearman correlation coefficient, rs∈=∈-0.08, p-value∈=∈0.21) and a stronger association was observed among female fetuses at approximately 18 weeks of gestation (rs∈=∈- 0.21, p-value∈<∈0.05). Although, associations were attenuated in adjusted analyses - among female fetuses a 1 μg/L increase in maternal urinary arsenic concentration was associated with a decrease of 0.047 (95% CI: -0.115, 0.021) in head circumference and 0.072 (95% CI: -0.151, 0.007) decrease in biparietal head diameter Z-score. Conclusions: Our study demonstrates that useful data can be extracted directly from electronic medical records for epidemiologic research. We also found evidence that exposure to low-level arsenic may be associated with reduced head circumference in a sex dependent manner that warrants further investigation. © 2015 Davis et al.; licensee BioMed Central. Source


Das A.K.,Collaboratory for Healthcare and Biomedical Informatics | Thompson S.,Collaboratory for Healthcare and Biomedical Informatics | Levin B.G.,Collaboratory for Healthcare and Biomedical Informatics | Andrews S.B.,Collaboratory for Healthcare and Biomedical Informatics
Proceedings - 2014 IEEE International Conference on Healthcare Informatics, ICHI 2014 | Year: 2014

There is growing need for research enterprises, such as academic medical centers, to measure, monitor, and evaluate the use and impact of institutional resources and services on research activity. To address this need, we have developed a web-based, mobile friendly, open-source system called In SPIRE (Information Sharing Platform for an Integrated Research Environment). We have designed In SPIRE based on activity theory, an established framework for analyzing work practices in terms of technical and social factors. We employed a spiral software development cycle to elicit system requirements based on activity theory, to define use cases, to implement an integrated, flexible architecture, and to validate the ability to capture data on specific research activities. In this work, we present the outcome of our novel design approach, namely an integrated multi-objective system that allows investigators to find and engage a range of research resources (such as cores, funding, and training) and that permits research administrators to track, measure, and assess the use of those resources in real time based on context-based abstraction of the data that is collected. © 2014 IEEE. Source

Discover hidden collaborations