Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing

Fu, Sunyang; Wyles, Cody C.; Osmon, Douglas R.; Carvour, Martha L.; Sagheb, Elham; Ramazanian, Taghi; Kremers, Walter K.; Lewallen, David G.; Berry, Daniel J.; Sohn, Sunghwan; Kremers, Hilal Maradit

doi:10.1016/j.arth.2020.07.076

Cited by 35 publications

(22 citation statements)

References 13 publications

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“…While patient-generated health data (PGHD) [ 14 ] are becoming commonplace, the sheer enormity of captured data points for a single patient [ 15 – 17 ] yield almost more information than can perceivably be comprehended and managed by the human processing capacity. Even ‘off-the-shelf’ wearable sensors [ 15 , 16 ] may capture several million discrete data points [ 14 ] for small cohorts, or single patients tracked for extended time periods. This mass of stored digital information is collectively known as ‘big data’ [ 8 , 10 , 12 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Kurmis

Ianunzio

2022

Arthroplasty

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Technological advances continue to evolve at a breath-taking pace. Computer-navigation, robot-assistance and three-dimensional digital planning have become commonplace in many parts of the world. With near exponential advances in computer processing capacity, and the advent, progressive understanding and refinement of software algorithms, medicine and orthopaedic surgery have begun to delve into artificial intelligence (AI) systems. While for some, such applications still seem in the realm of science fiction, these technologies are already in selective clinical use and are likely to soon see wider uptake. The purpose of this structured review was to provide an understandable summary to non-academic orthopaedic surgeons, exploring key definitions and basic development principles of AI technology as it currently stands. To ensure content validity and representativeness, a structured, systematic review was performed following the accepted PRISMA principles. The paper concludes with a forward-look into heralded and potential applications of AI technology in orthopedic surgery.While not intended to be a detailed technical description of the complex processing that underpins AI applications, this work will take a small step forward in demystifying some of the commonly-held misconceptions regarding AI and its potential benefits to patients and surgeons. With evidence-supported broader awareness, we aim to foster an open-mindedness among clinicians toward such technologies in the future.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Kurmis

Ianunzio

2022

Arthroplasty

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“…Common types of unstructured text include clinical notes (e.g., progress, consultation, admission/discharge summary), radiology reports, pathology reports, and microbiology reports. Since the HITECH Act of 2008, there have been an increasing number of studies that use EHR text to enrich patient information in the areas of incidental findings ( 1 ), diseases and conditions with multi-factorial causes ( 2 , 3 ), diseases and conditions with no singular and conclusive diagnostic tests ( 4 ), surgical information( 4 , 5 ), and social determinants of health ( 6 ). These examples strongly suggest that text information can drastically improve the discovery and detection of conditions that are not routinely coded and/or are underdiagnosed in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Quality assessment of functional status documentation in EHRs across different healthcare institutions

Vassilaki²,

Ibrahim³

et al. 2022

Front. Digit. Health

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The secondary use of electronic health records (EHRs) faces challenges in the form of varying data quality-related issues. To address that, we retrospectively assessed the quality of functional status documentation in EHRs of persons participating in Mayo Clinic Study of Aging (MCSA). We used a convergent parallel design to collect quantitative and qualitative data and independently analyzed the findings. We discovered a heterogeneous documentation process, where the care practice teams, institutions, and EHR systems all play an important role in how text data is documented and organized. Four prevalent instrument-assisted documentation (iDoc) expressions were identified based on three distinct instruments: Epic smart form, questionnaire, and occupational therapy and physical therapy templates. We found strong differences in the usage, information quality (intrinsic and contextual), and naturality of language among different type of iDoc expressions. These variations can be caused by different source instruments, information providers, practice settings, care events and institutions. In addition, iDoc expressions are context specific and thus shall not be viewed and processed uniformly. We recommend conducting data quality assessment of unstructured EHR text prior to using the information.

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“…Zhang et al propose a model to detect potentially fraudulent financial activities through textual analysis of financial reports of some Chinese companies [22]. Fu et al use an NLP algorithm to analyse a dataset based on periprosthetic joint disease [23].…”

Section: Introductionmentioning

confidence: 99%

A novel data processing approach to detect fraudulent insurance claims for physical damage to cars

YÜCEL

2022

Journal of New Results in Science

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Some automobile insurance companies use computerized auto-detection systems to expedite claims payment decisions for insured vehicles. Claims suspected of fraud are evaluated using empirical data from previously investigated claims. The main objective of this manuscript is to demonstrate a novel data processing system and its potential for use in data classification. The data processing approach was used to develop a machine learning-based sentiment classification model to describe property damage fraud in vehicle accidents and the indicators of fraudulent claims. To this end, Singular Value Decomposition-based components and correlation-based composite variables were created. Machine learning models were then developed, with predictors and composite variables selected based on standard feature selection procedures. Five machine learning models were used: Boosted Trees, Classification and Regression Trees, Random Forests, Artificial Neural Networks, and Support Vector Machines. For all models, the models with composite variables achieved higher accuracy rates, and among these models, the artificial neural network was the model with the highest accuracy performance at 76.56%.

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Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing

Cited by 35 publications

References 13 publications

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Quality assessment of functional status documentation in EHRs across different healthcare institutions

A novel data processing approach to detect fraudulent insurance claims for physical damage to cars

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