scholarly journals Monitoring Pneumatic Actuators’ Behavior Using Real-World Data Set

2020 ◽  
Vol 1 (4) ◽  
Author(s):  
Tibor Kovacs ◽  
Andrea Ko
Keyword(s):  
Real World ◽  
Real World Data ◽  
Data Set ◽  
Pneumatic Actuators ◽  
World Data

scholarly journals Consensus Development of a Modern Ontology of Emergency Department Presenting Problems—The Hierarchical Presenting Problem Ontology (HaPPy)

2019 ◽  
Vol 10 (03) ◽  
pp. 409-420 ◽  
Author(s):  
Steven Horng ◽  
Nathaniel R. Greenbaum ◽  
Larry A. Nathanson ◽  
James C. McClay ◽  
Foster R. Goss ◽  
...  
Keyword(s):  
Emergency Department ◽  
Real World ◽  
Emergency Department Patient ◽  
Snomed Ct ◽  
Presenting Problems ◽  
Real World Data ◽  
Validation Data ◽  
Data Set ◽  
World Data ◽  
Academic Level

Objective Numerous attempts have been made to create a standardized “presenting problem” or “chief complaint” list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges. Materials and Methods We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT). Results Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set. Discussion and Conclusion We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

A retrospective analysis of treatment patterns in metastatic castration-resistant prostate cancer patients treated with radium-223.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 180-180 ◽  
Author(s):  
A. Oliver Sartor ◽  
Sreevalsa Appukkuttan ◽  
Ronald E. Aubert ◽  
Jeffrey Weiss ◽  
Joy Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Pilot Trial ◽  
Clinical Trial Data ◽  
Castration Resistant Prostate Cancer ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
Castration Resistant ◽  
Radium 223

180 Background: Radium-223 (RA-223) is the first FDA approved targeted alpha therapy that significantly improves overall survival (OS) in patients (pts) with metastatic castration resistant prostate cancer (mCRPC) with symptomatic bone metastases. There is limited real world data describing RA-223 current use. Methods: A retrospective patient chart review was done of men who received at least 1 cycle of Ra-223 for mCRPC in 10 centers throughout the US (4 academic, 6 private practices). All pts had a minimum follow-up of 4 months, or placed in hospice or death. Descriptive analyses for clinical characteristics and treatment outcomes were performed. Results: Among the 200 pts (mean age-73.6 years, mean Charlson comorbidity index-6.9) RA-223 was initiated on average 1.6 years from mCRPC diagnosis (first line use (1L)=38.5%, 2L=31.5% and ≥3L=30%). 78% completed 5-6 cycles of RA-223 with mean therapy duration of 4.2 months. Among all pts, 43% received RA-223 as monotherapy (no overlap with other mCRPC therapies) while 57% had combination therapy with either abiraterone or enzalutamide. Median OS following RA-223 initiation was 21.2 months (95% CI 19.6- 29.2). Table provides the RA-223 utilization by type of clinical practice. Conclusions: Utilization of RA-223 in this real world data set was distinct from clinical trial data. Most patients received RA-223 in combination with abiraterone or enzalutamide, therapies that were unavailable when the pilot trial was conducted. Median survival was 21.2 months. Real world use of RA-223 has evolved as newer agents have become FDA approved in bone-metastatic CRPC. Academic and community patterns of practice were more similar than distinct. [Table: see text]

Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18725-e18725
Author(s):  
Ravit Geva ◽  
Barliz Waissengrin ◽  
Dan Mirelman ◽  
Felix Bokstein ◽  
Deborah T. Blumenthal ◽  
...  
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Clinical Decision Making ◽  
Homomorphic Encryption ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Raw Data ◽  
Encrypted Data ◽  
World Data

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

Empirically Testing a Disaggregate Choice Model for Segments

1985 ◽  
Vol 22 (4) ◽  
pp. 462-467 ◽  
Author(s):  
Dennis H. Gensch
Keyword(s):  
Real World ◽  
Choice Model ◽  
Simple Procedure ◽  
Choice Models ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
Homogeneity Assumption ◽  
Multiattribute Choice

All disaggregate multiattribute choice models contain the assumption that the population is reasonably homogeneous with respect to the aggregate parameters estimated by the model. The author points out that one particular choice model, logit, has a structure that makes it particularly suited to test a data set for possible segments. A real-world data set is used to illustrate a simple procedure for testing the homogeneity assumption. The analysis provides a warning that managers may easily derive suboptimal or counterproductive strategies if they fail to test this assumption.

scholarly journals Performance of Machine Learning Algorithms and Diversity in Data

2018 ◽  
Vol 210 ◽  
pp. 04019 ◽  
Author(s):  
Hyontai SUG
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Real World Data ◽  
Random Data ◽  
Data Set ◽  
World Data

Recent world events in go games between human and artificial intelligence called AlphaGo showed the big advancement in machine learning technologies. While AlphaGo was trained using real world data, AlphaGo Zero was trained using massive random data, and the fact that AlphaGo Zero won AlphaGo completely revealed that diversity and size in training data is important for better performance for the machine learning algorithms, especially in deep learning algorithms of neural networks. On the other hand, artificial neural networks and decision trees are widely accepted machine learning algorithms because of their robustness in errors and comprehensibility respectively. In this paper in order to prove that diversity and size in data are important factors for better performance of machine learning algorithms empirically, the two representative algorithms are used for experiment. A real world data set called breast tissue was chosen, because the data set consists of real numbers that is very good property for artificial random data generation. The result of the experiment proved the fact that the diversity and size of data are very important factors for better performance.

Sign in / Sign up

Export Citation Format

Share Document