A Novel Approach to Explore How Nursing Care Affects Intracranial Pressure

2017 ◽  
Vol 26 (2) ◽  
pp. 136-139 ◽  
Author(s):  
DaiWai M. Olson ◽  
Camille Parcon ◽  
Aljean Santos ◽  
Guilla Santos ◽  
Ryan Delabar ◽  
...  
Keyword(s):  
Intracranial Pressure ◽  
Video Recording ◽  
Nursing Intervention ◽  
Pressure Data ◽  
Patient Level ◽  
Novel Approach ◽  
Level Data ◽  
Self Reports ◽  
Chart Abstraction ◽  
Nursing Behaviors

Background Intracranial pressure is measured continuously, and nursing behaviors have been associated with variations in the measurements. Methods A prospective pilot observational study was done to develop a comprehensive list of nursing behaviors that affect patients’ intracranial pressure. Data on nurses were obtained by self-reports and video recording. Patient-level data were collected via chart abstraction, video recording, and patients’ monitors. Results Data on 9 patients and 32 nurses were analyzed. A total of 6244 minutes of data were video recorded. Intracranial pressure was changed because of a nursing intervention during 3394 observations. Compared with baseline levels, intracranial pressure was significantly higher if a nursing intervention was performed (odds ratio, 1.96; 95% CI, 1.71–2.24; P < .001). Conclusion Studying nursing behaviors is feasible. Synchronizing and analyzing mutually exclusive and exhaustive behaviors indicated that nursing behaviors have an effect on patients’ intracranial pressure.

scholarly journals CLEP: A Hybrid Data- and Knowledge-Driven Framework for Generating Patient Representations

2020 ◽  
Author(s):  
Vinay Srinivas Bharadhwaj ◽  
Mehdi Ali ◽  
Colin Birkenbihl ◽  
Sarah Mubeen ◽  
Jens Lehmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prior Knowledge ◽  
Knowledge Graph ◽  
Biomedical Data ◽  
Biological Interactions ◽  
Patient Level ◽  
Novel Approach ◽  
Level Data ◽  
Transcriptomics Data ◽  
Characteristic Features

AbstractAs machine learning and artificial intelligence become more useful in the interpretation of biomedical data, their utility depends on the data used to train them. Due to the complexity and high dimensionality of biomedical data, there is a need for approaches that combine prior knowledge around known biological interactions with patient data. Here, we present CLEP, a novel approach that generates new patient representations by leveraging both prior knowledge and patient-level data. First, given a patient-level dataset and a knowledge graph containing relations across features that can be mapped to the dataset, CLEP incorporates patients into the knowledge graph as new nodes connected to their most characteristic features. Next, CLEP employs knowledge graph embedding models to generate new patient representations that can ultimately be used for a variety of downstream tasks, ranging from clustering to classification. We demonstrate how using new patient representations generated by CLEP significantly improves performance in classifying between patients and healthy controls for a variety of machine learning models, as compared to the use of the original transcriptomics data. Furthermore, we also show how incorporating patients into a knowledge graph can foster the interpretation and identification of biological features characteristic of a specific disease or patient subgroup. Finally, we released CLEP as an open source Python package together with examples and documentation.

scholarly journals Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

2021 ◽  
Vol 09 (02) ◽  
pp. E233-E238
Author(s):  
Rajesh N. Keswani ◽  
Daniel Byrd ◽  
Florencia Garcia Vicente ◽  
J. Alex Heller ◽  
Matthew Klug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Medical Center ◽  
Academic Medical Center ◽  
Procedure Time ◽  
Image Capture ◽  
Common Procedure ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Abstract Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

Want to reduce the bullwhip? Measure it. Here’s how

2017 ◽  
Vol 22 (4) ◽  
pp. 297-304 ◽  
Author(s):  
Ming Jin ◽  
Nicole DeHoratius ◽  
Glen Schmidt
Keyword(s):  
Small Perturbation ◽  
Design Methodology ◽  
Bullwhip Effect ◽  
End Users ◽  
Social Implications ◽  
Content Type ◽  
Novel Approach ◽  
Level Data ◽  
Beer Game ◽  
Practical Implications

Purpose The popular “beer game” illustrates the bullwhip effect where a small perturbation in downstream demand can create wild swings in upstream product flows. The purpose of this paper is to present a methodical framework to measure the bullwhip effect and evaluate its impact. Design/methodology/approach This paper illustrates a framework using SKU-level data from an industry-leading manufacturer, its distributors, end-users and suppliers. Findings Firms benefit from tracking multiple intra-firm bullwhips and from tracking bullwhips pertinent to specific products, specific suppliers and specific customers. The framework presented in this paper enables managers to pinpoint bullwhip sources and mitigate bullwhip effects. Research limitations/implications This paper presents a framework for methodically measuring and tracking intra-firm and inter-firm bullwhips. Practical implications A disconnect exists between what is known and taught regarding the bullwhip effect and how it is actually tracked and managed in practice. This paper aims to reduce this gap. For the various products analyzed herein, the authors show how using this framework has the potential to reduce delivered product cost by 2 to 15 per cent. Social implications Properly managing the bullwhip leads to lower inventories and potentially lower product prices while simultaneously increasing firm profits. Originality/value This paper presents a novel approach to systematically tracking intra-firm bullwhips along with bullwhips specific to a given supplier or customer.

scholarly journals Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Chuan Hong ◽  
Everett Rush ◽  
Molei Liu ◽  
Doudou Zhou ◽  
Jiehuan Sun ◽  
...  
Keyword(s):  
Feature Selection ◽  
Electronic Health Record ◽  
Large Scale ◽  
Knowledge Extraction ◽  
Knowledge Map ◽  
Health Record ◽  
Clinical Knowledge ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

AbstractThe increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

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