12 datasets for Computer Methods and Programs in Biomedicine
Data for: Integrating Machine Learning Techniques and Physiology Based Heart Rate Features for Antepartum Fetal Monitoring
Contributors: Maria G. Signorini, Marta Campanile, Giovanni Magenes
... The article contains a set of 12 linear and nonlinear indices extracted from Fetal Heart Rate (FHR) recordings by means of CTG monitors on two groups of fetuses: 60 normals and 60 Intra Uterine Growth Restricted (IUGR) fetuses. The two populations were selected by clinicians after birth on the basis of clinical standards for detecting growth restricted newborns (Apgar scores, percentile weight, …). The indices were computed on FHR recordings, each one lasting more than 30 minutes, by means of algorithms already published in the scientific literature.
Data for: Using The Feature Selection with Genetic Algorithm to Abbreviate Indonesia’s Health Literacy Survey Questionnaire (HLS-EU) and Comparing the Accurate Classification among the Datasets from Existing Short Version of HLS-EU
Contributors: Nurjanah Nurjanah, Enny Rachmani, Arif Kurniadi, Guruh fajar Shidik, Peter Wushou Chang, Edi Noersasongko
... Data Health Literacy Survey in Semarang, Indonesia using HLS-EU-Q47
Contributors: Francisco Luna-Perejon, Daniel Cascado-Caballero, Panagiotis Bamidis, Javier Civit, Shwetambara Malwade, Yu-Chuan Li, Evdokimos Konstantinidis, Charis Styliadis, Anton Civit, Syed-Abdul Shabbir
... Research data file contains the appendices referenced in the article. Spreadshets contain the most relevant anonymized information of the participants during the study period.
Contributors: Nicholas Widmann, Newton Buchanan, Matthew Maltese, Dana Niles, Robert Sutton, Vinay Nadkarni, Godfrey Nazareth
... Source code for our CPR manikin that simulates blood pressure and end tidal CO2 waveforms for the purpose of training the titration of CPR mechanics to physiology, as recommended by the American Heart Association.
Data for: Quantitative characterization of rodent feto-placental vasculature morphology in micro-computed tomography images
Contributors: Yutthapong Tongpob, Shushan Xia, Caitlin Wyrwoll, Andrew Mehnert
... Here are Supplementary Materials for "Quantitative characterization of rodent feto-placental vasculature morphology in micro-computed tomography images": Supplement 1: Procedure for sample preparation and casting of the vasculature, Supplement 2: Micro-CT scanning and image reconstruction, Supplement 3: Matlab code, Supplement 4a: RAT_summary_data.xlsx, Supplement 4b: RAT_detailed_data.xlsx, Supplement 4c: Mouse_summary_data.xlsx, Supplement 4d: Mouse_detailed_data.xlsx.
Data for: Detection of respiratory rate using a classifier of waves in the signal from a FBG-based vital signs sensor
Contributors: Mariusz Krej, Łukasz Dziuda, Paulina Baran
Contributors: Junrong Zheng, Liang Deng
... The codes include the non-cartesian reconstruction for PROPELLER sequences, and the fundamental process for raw optical signals.
Contributors: Kong-Fa Hu, Pei-pei Fang, Jia-Dong Xie, Guo-Zheng Li, 胡 晨骏, Yu-Qing Mao, Ju He
... S1 to Results of the Prince Algorithm S2 to The full KEGG pathways S3 to The full Reactome pathways
Data for: MobilityAnalyser: A novel approach for automatic quantification of cell mobility on periodic patterned substrates using brightfield microscopy images
Contributors: Angela Carvalho, Fernando Monteiro, Tiago Esteves, Pedro Quelhas
... Supplement file consisting on a user guide for instalation and use of the developed software, MobilityAnalyser.
Supplementary Materials for: Supervised signal detection for adverse drug reactions in medication dispensing data
Contributors: Tao Hoang, Jiuyong Li, Jixue Liu, Nicole Pratt, Elizabeth Roughead
... This file compares potential signals of adverse drug reactions (ADRs) detected by sequence symmetry analysis (SSA) and supervised gradient boosting classifier. ADR signals of higher confidence are assigned higher adjusted sequence ratios (rightward) by SSA and higher probabilities (upward) by gradient boosting classifier. Blue circles represent known ADRs while red squares indicate unknown potential ADR signals. A signal is picked up by SSA if the 95% confidence interval lower limit of its adjusted sequence ratio exceeds 1 and picked up by gradient boosting classifier if its probability is greater than 0.5. ADR signals of higher confidence are assigned higher adjusted sequence ratios (rightward) by SSA and higher probabilities (upward) by gradient boosting classifier.