Creating Evidence from Real World Patient Digital Data

2021 ◽  
Keyword(s):  
Real World ◽  
Digital Data

Symbols Without Context

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Data Base ◽  
Machine Translation ◽  
Real World ◽  
Language Translation ◽  
Digital Data ◽  
Meaningful Difference ◽  
World Knowledge ◽  
Similarities And Differences ◽  
Words And Images ◽  
The Way

Humans have invaluable real-world knowledge because we have accumulated a lifetime of experiences that help us recognize, understand, and anticipate. Computers do not have real-world experiences to guide them, so they must rely on statistical patterns in their digital data base—which may be helpful, but is certainly fallible. We use emotions as well as logic to construct concepts that help us understand what we see and hear. When we see a dog, we may visualize other dogs, think about the similarities and differences between dogs and cats, or expect the dog to chase after a cat we see nearby. We may remember a childhood pet or recall past encounters with dogs. Remembering that dogs are friendly and loyal, we might smile and want to pet the dog or throw a stick for the dog to fetch. Remembering once being scared by an aggressive dog, we might pull back to a safe distance. A computer does none of this. For a computer, there is no meaningful difference between dog, tiger, and XyB3c, other than the fact that they use different symbols. A computer can count the number of times the word dog is used in a story and retrieve facts about dogs (such as how many legs they have), but computers do not understand words the way humans do, and will not respond to the word dog the way humans do. The lack of real world knowledge is often revealed in software that attempts to interpret words and images. Language translation software programs are designed to convert sentences written or spoken in one language into equivalent sentences in another language. In the 1950s, a Georgetown–IBM team demonstrated the machine translation of 60 sentences from Russian to English using a 250-word vocabulary and six grammatical rules. The lead scientist predicted that, with a larger vocabulary and more rules, translation programs would be perfected in three to five years. Little did he know! He had far too much faith in computers. It has now been more than 60 years and, while translation software is impressive, it is far from perfect. The stumbling blocks are instructive. Humans translate passages by thinking about the content—what the author means—and then expressing that content in another language.

Developing a Virtual Model of a Second Order System to Simulation Real Laboratory Measurement Problems

2004 ◽  
Author(s):  
Peter Avitabile ◽  
Tracy Van Zandt
Keyword(s):  
Simulation Model ◽  
Real World ◽  
Measurement Errors ◽  
Second Order ◽  
Digital Data ◽  
Quantization Noise ◽  
Order System ◽  
Laboratory Exercise ◽  
Problems And Solutions ◽  
Real World Problems

Most of the student’s educational exposure is to well behaved, deterministic problems with known results. Most courses expose students to material in compartmentized modules (chapters of a book) with exercises/problems (at the end of the chapter) where the majority of the material is readily found in the compartmentized module. Unfortunately, real world problems never fit this simple mold. Laboratory is the perfect place for students to become exposed to real world problems and solutions to those problems. Laboratory is the perfect place to put all the student’s knowledge of basic STEM material to the test. However, many times the real world measurement is much more complicated than the textbook problems and students often struggle with methods and procedures to solve a given problem (with no answer at the back of the book). This is true for a mechanical measurement of a simple second order mass, spring, dashpot system which is measured with displacement and acceleration instruments in an existing mechanical engineering laboratory exercise. The measurement is plagued with measurement errors, drift, bias, digital data acquisition amplitude/quantization errors, etc. In order to understand the basic underlying measurement and associated “problems” with the measurement, a simple simulation model was developed. The simulation model allows the students to define a basic second order system and then add different types of “problems” (drift, bias, quantization, noise, etc) to the measurement to see their effects. The simulation module further allows the student to “cleanse” the distorted data using common measurement tools such as coupling, filtering, smoothing, etc. to understand the effects of processing the data. The simulation model is built using Simulink/MATLAB and allows a simple GUI to modify the model, the “problems” added to the data and the “cleansing” of the data, to obtain a better understanding of the problem and tools to process the data. The simulation model is presented and discussed in the paper. Several data sets are presented to illustrate the simulation module.

scholarly journals Editorial: Creating Evidence From Real World Patient Digital Data

2021 ◽  
Vol 2 ◽  
Author(s):  
Jane Nikles ◽  
Eric J. Daza ◽  
Suzanne McDonald ◽  
Eric Hekler ◽  
Nicholas J. Schork
Keyword(s):  
Real World ◽  
Digital Data

scholarly journals Survey data and human computation for improved flu tracking

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefan Wojcik ◽  
Avleen S. Bijral ◽  
Richard Johnston ◽  
Juan M. Lavista Ferres ◽  
Gary King ◽  
...  
Keyword(s):  
Real Time ◽  
Survey Data ◽  
Human Behavior ◽  
Real World ◽  
Data Streams ◽  
Behavioral Model ◽  
Human Computation ◽  
Digital Data ◽  
Search Data ◽  
Tracking Model

AbstractWhile digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users’ online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.

scholarly journals Statistical Outlier Curation Kernel Software (SOCKS): A Modern, Efficient Outlier Detection and Curation Suite

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Remko Van Lutterveld ◽  
Nancy Quirós ◽  
Veronique Taylor ◽  
Judson Brewer
Keyword(s):  
Data Processing ◽  
Outlier Detection ◽  
Real World ◽  
Software Tool ◽  
Ground Truth ◽  
Data Sources ◽  
Digital Data ◽  
Signal Acquisition ◽  
Real World Data ◽  
True Nature

Real world signal acquisition through sensors, is at the heart of modern digital revolution. However, almost every signal acquisition systems are contaminated with noise and outliers. Precise detec- tion, and curation of data is an essential step to reveal the true-nature of the uncorrupted observations. With the exploding volumes of digital data sources, there is a critical need for a robust but easy-to-operate, low-latency, generic yet highly customizable, outlier- detection and curation tool, easily accessible, adaptable to diverse types of data sources. Existing methods often boil down to data smoothing that inherently cause valuable information loss. We have developed a C++ based, software tool to decontaminate time- series and matrix like data sources, with the goal of recovering the ground-truth. The SOCKS tool would be made available as an open-source software for broader adoption in the scientific community. Our work calls for a philosophical shift in the design pipelines of real- world data processing. We propose, raw data should be decontaminated first, through conditional flagging of outliers, curation of flagged points, followed by iterative, parametrically tuned, asymptotic converge to the ground-truth as accurately as possible, before performing traditional data processing tasks.

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

scholarly journals Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper (Preprint)

2019 ◽  
Author(s):  
Guy Fagherazzi
Keyword(s):  
Disease Management ◽  
Real World ◽  
Large Scale ◽  
Digital Data ◽  
Health It ◽  
Patient Centered ◽  
Digital Phenotyping ◽  
Digital Twins ◽  
Centered Care ◽  
Precision Health

UNSTRUCTURED This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone’s digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention.

Role of real-world digital data for orthopedic implant automated surveillance: a systematic review

2021 ◽  
Author(s):  
Thibault Dhalluin ◽  
Sara Fakhiri ◽  
Guillaume Bouzillé ◽  
Julien Herbert ◽  
Philippe Rosset ◽  
...  
Keyword(s):  
Systematic Review ◽  
Real World ◽  
Digital Data ◽  
Orthopedic Implant ◽  
Automated Surveillance

scholarly journals Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper

10.2196/16770 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e16770 ◽  
Author(s):  
Guy Fagherazzi
Keyword(s):  
Disease Management ◽  
Real World ◽  
Large Scale ◽  
Digital Data ◽  
Health It ◽  
Patient Centered ◽  
Digital Phenotyping ◽  
Digital Twins ◽  
Centered Care ◽  
Precision Health

This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone’s digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention.

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