Full Text Journal Articles by
Author Sudha Ram

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A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort.

Sung Hye Kong, Daehwan Ahn, Buomsoo Raymond Kim, Karthik Srinivasan, Sudha Ram, Hana Kim, A Ram Hong, Jung Hee Kim, Nam H Cho, Chan Soo Shin,

The prediction of fracture risk in osteoporotic patients has been a topic of interest for decades, and models have been developed for the accurate prediction of fracture, including the fracture risk assessment tool (FRAX). As machine-learning methodologies have recently emerged as a potential model for medical prediction tools, we aimed ... Read more >>

JBMR Plus (JBMR plus)
[2020, 4(3):e10337]

Cited: 0 times

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Giant Cervicodorsal Schwannoma.

Sudha Ram, V Vivek, Ravi Shekhar, Abhirama Chandra Gabbita, K Ganesh,

Objective:Schwannomas are benign slow growing tumors that arise from myelin producing Schwann cells. Schwannomas developing in cervical-dorsal region are rare benign neoplasms which are emerges leisurely remains asymptomatic some times and functionally inactive tumours. Giant Schwannomas extending over two or more vertebral levels have been documented and attempts have been ... Read more >>

J. Exp. Ther. Oncol. (Journal of experimental therapeutics & oncology)
[2019, 13(2):155-158]

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Pure intracranial, intraparenchymal presentation of a malignant peripheral nerve sheath tumour: A rare case.

Ganesh Arumugam, Sudha Ram, P Bhaskar Naidu, Selvakumar Kumaravelu,

Neurol India (Neurology India)
[2019, 67(3):900-903]

Cited: 0 times

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Effects of office workstation type on physical activity and stress.

Casey M Lindberg, Karthik Srinivasan, Brian Gilligan, Javad Razjouyan, Hyoki Lee, Bijan Najafi, Kelli J Canada, Matthias R Mehl, Faiz Currim, Sudha Ram, Melissa M Lunden, Judith H Heerwagen, Kevin Kampschroer, Esther M Sternberg,

OBJECTIVE:Office environments have been causally linked to workplace-related illnesses and stress, yet little is known about how office workstation type is linked to objective metrics of physical activity and stress. We aimed to explore these associations among office workers in US federal office buildings. METHODS:We conducted a wearable, sensor-based, observational ... Read more >>

Occup Environ Med (Occupational and environmental medicine)
[2018, 75(10):689-695]

Cited: 4 times

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Predicting High-Cost Patients at Point of Admission Using Network Science.

Karthik Srinivasan, Faiz Currim, Sudha Ram,

Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in the literature. This is due to the lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a ... Read more >>

IEEE J Biomed Health Inform (IEEE journal of biomedical and health informatics)
[2018, 22(6):1970-1977]

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Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

Ruben Amarasingham, Anne-Marie J Audet, David W Bates, I Glenn Cohen, Martin Entwistle, G J Escobar, Vincent Liu, Lynn Etheredge, Bernard Lo, Lucila Ohno-Machado, Sudha Ram, Suchi Saria, Lisa M Schilling, Anand Shahi, Walter F Stewart, Ewout W Steyerberg, Bin Xie,

CONTEXT:The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an ... Read more >>

EGEMS (Wash DC) (EGEMS (Washington, DC))
[2016, 4(1):1163]

Cited: 12 times

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Predicting asthma-related emergency department visits using big data.

Sudha Ram, Wenli Zhang, Max Williams, Yolande Pengetnze,

Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of ... Read more >>

IEEE J Biomed Health Inform (IEEE journal of biomedical and health informatics)
[2015, 19(4):1216-1223]

Cited: 15 times

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The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

Stephen A Goff, Matthew Vaughn, Sheldon McKay, Eric Lyons, Ann E Stapleton, Damian Gessler, Naim Matasci, Liya Wang, Matthew Hanlon, Andrew Lenards, Andy Muir, Nirav Merchant, Sonya Lowry, Stephen Mock, Matthew Helmke, Adam Kubach, Martha Narro, Nicole Hopkins, David Micklos, Uwe Hilgert, Michael Gonzales, Chris Jordan, Edwin Skidmore, Rion Dooley, John Cazes, Robert McLay, Zhenyuan Lu, Shiran Pasternak, Lars Koesterke, William H Piel, Ruth Grene, Christos Noutsos, Karla Gendler, Xin Feng, Chunlao Tang, Monica Lent, Seung-Jin Kim, Kristian Kvilekval, B S Manjunath, Val Tannen, Alexandros Stamatakis, Michael Sanderson, Stephen M Welch, Karen A Cranston, Pamela Soltis, Doug Soltis, Brian O'Meara, Cecile Ane, Tom Brutnell, Daniel J Kleibenstein, Jeffery W White, James Leebens-Mack, Michael J Donoghue, Edgar P Spalding, Todd J Vision, Christopher R Myers, David Lowenthal, Brian J Enquist, Brad Boyle, Ali Akoglu, Greg Andrews, Sudha Ram, Doreen Ware, Lincoln Stein, Dan Stanzione,

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address ... Read more >>

Front Plant Sci (Frontiers in plant science)
[2011, 2:34]

Cited: 149 times

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