scholarly journals Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257361
Author(s):  
Joanna F. Dipnall ◽  
Richard Page ◽  
Lan Du ◽  
Matthew Costa ◽  
Ronan A. Lyons ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Distal Radius ◽  
Prediction Models ◽  
Registry Data ◽  
Unstructured Data ◽  
Clinical Registry ◽  
Fracture Characteristics ◽  
Wrist Fractures ◽  
Patient Reported ◽  
Informed Treatment

Background Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The “Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)” study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. Methods and design Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. Discussion The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.

scholarly journals Cohort profile: the St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry, a pragmatic prospective database defining outcomes in total hip and knee replacement patients

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040408
Author(s):  
Daniel Gould ◽  
Sharmala Thuraisingam ◽  
Cade Shadbolt ◽  
Josh Knight ◽  
Jesse Young ◽  
...  
Keyword(s):  
Registry Data ◽  
Prescribing Patterns ◽  
Audit And Feedback ◽  
Economic Evaluations ◽  
Tertiary Referral Hospital ◽  
Clinical Registry ◽  
Patient Reported ◽  
Prospective Database ◽  
Future Plans

PurposeThe St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry is an institutional clinical registry housed at a tertiary referral hospital in Australia. The SMART Registry is a pragmatic prospective database, which was established to capture a broad range of longitudinal clinical and patient-reported outcome data to facilitate collaborative research that will improve policy and practice relevant to arthroplasty surgery for people with advanced arthritis of the hip or knee. The purpose of this cohort profile paper is to describe the rationale for the SMART Registry’s creation, its methods, baseline data and future plans for the Registry. A full compilation of the data is provided as a reference point for future collaborators.ParticipantsThe SMART Registry cohort comprises over 13 000 consecutive arthroplasty procedures in more than 10 000 patients who underwent their procedure at St Vincent’s Hospital Melbourne, since January 1998. Participant recruitment, data collection and follow-up is ongoing and currently includes up to 20 years follow-up data.Findings to dateSMART Registry data are used for clinical audit and feedback, as well as for a broad range of research including epidemiological studies, predictive statistical modelling and health economic evaluations. At the time of writing, there were 46 publications from SMART Registry data, with contributions from more than 67 coauthors.Future plansWith the recent linking of the SMART Registry with Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data through the Australian Institute of Health and Welfare, research into prescribing patterns and health system utilisation is currently underway. The SMART Registry is also being updated with the Clavien-Dindo classification of surgical complications.

scholarly journals Validating health conditions in a clinical registry using administrative data algorithms

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Lisa Lix ◽  
Lisa Zhang ◽  
Lin Yan ◽  
Tolu Sajobi ◽  
Richard Sawatzky ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Blood Pressure ◽  
Back Pain ◽  
Administrative Data ◽  
High Blood Pressure ◽  
Registry Data ◽  
Health Conditions ◽  
Clinical Registry ◽  
Patient Reported ◽  
Reported Health

IntroductionClinical registries are a potentially valuable resource to study the effects of medical interventions on outcomes, particularly patient-reported outcomes like health-related quality of life, which are not included in administrative data. However, because clinical registries are primarily intended for patient management and not for research, their validity must be established. Objectives and ApproachOur objective was to validate patient self-reported health conditions in a clinical registry. Study data were from a population-based regional joint replacement registry in the Canadian province of Manitoba. The clinical registry data were linked to administrative health data. Validated administrative data algorithms for 12 conditions were defined using diagnosis codes in hospital and physician records and medication codes in prescription drug records for the period up to three years prior to the joint replacement surgery. Accuracy of the clinical registry data was estimated using Cohen’s kappa coefficient, sensitivity, specificity, and positive and negative predictive values (PPV; NPV); 95% confidence intervals were also estimated. Analyses were stratified by joint type, age group, and sex. ResultsThe study cohort included 20,592 individuals (average age 66.3 years; 58.4% female); 8,424 (40.9%) had a total hip replacement. Sensitivity of the clinical registry data ranged from 16% (anemia) to more than 70% (diabetes, high blood pressure, rheumatoid arthritis); specificity was greater than 92% for all conditions, except back pain and high blood pressure. PPV ranged from 19% (back pain) to 83% (diabetes). Chance-adjusted agreement was very good for diabetes (kappa: 0.74), moderate for heart disease and high blood pressure (kappa range: 0.41-0.53) and poor or fair for back pain, anemia, cancer, depression, kidney disease, liver disease, rheumatoid arthritis and stomach ulcers (kappa range: 0.14-0.37). Estimates varied by sex (i.e., generally higher agreement for females) and age (i.e., generally lower agreement for older age groups), but not joint type. Conclusion/ImplicationsSelf-reported health conditions in registry data had good validity for conditions with clear diagnostic criteria, but low validity for conditions that are difficult to diagnose or rare (e.g., cancer). Linked registry and administrative data is strongly recommended to ensure valid and accurate comorbidity measures when developiong risk prediction models and conducting inter-jurisdictional comparisons of patient-reported outcome measures.

scholarly journals An Insight on Machine Learning Algorithms and its Applications

2019 ◽  
Vol 8 (11S2) ◽  
pp. 432-436
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Life Sciences ◽  
Simulation Models ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Management Techniques ◽  
The Impact

Machine Learning (ML) furnishes the ability of insights on automatic recognizing patterns and determining the prediction models for the structured and unstructured data even in the absence of explicit programming instructions. Today, the impact of Artificial Intelligence (AI) has grown up to several heights, ranging from Life sciences to the Management techniques. The integration of ML led to reduce or eliminate the errors in the prediction, classification and simulation models. The objective of the paper is to represent the ML objectives, explore the various ML techniques and algorithms with its applications in the various fields.

Sports Medicine and Artificial Intelligence: A Primer

2021 ◽  
pp. 036354652110086
Author(s):  
Prem N. Ramkumar ◽  
Bryan C. Luu ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Benedict U. Nwachukwu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Sports Medicine ◽  
Injury Risk ◽  
Industrial Revolution ◽  
Improve Patient Care ◽  
Great Promise ◽  
Patient Reported ◽  
Athlete Injury ◽  
Improve Patient ◽  
Applications Of Ai

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

scholarly journals Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanna Iivanainen ◽  
Jussi Ekstrom ◽  
Henri Virtanen ◽  
Vesa V. Kataja ◽  
Jussi P. Koivunen
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Adverse Events ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Patient Reported Outcomes ◽  
Prediction Models ◽  
Accuracy Score ◽  
Patient Reported ◽  
Auc Value

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019

Predictors of Management of Distal Radius Fractures in Patients Aged >65 Years

Hand ◽  
2021 ◽  
pp. 155894472110172
Author(s):  
Amanda Walsh ◽  
Nelson Merchan ◽  
David N. Bernstein ◽  
Bailey Ingalls ◽  
Carl M. Harper ◽  
...  
Keyword(s):  
Distal Radius ◽  
Operative Management ◽  
Nonoperative Treatment ◽  
Distal Radius Fractures ◽  
Radius Fractures ◽  
Total N ◽  
Fracture Characteristics ◽  
Time Period ◽  
Tertiary Center ◽  
Comorbidity Index

Background Treatment of distal radius fractures (DRFs) in patients aged >65 years is controversial. The purpose of this study was to identify what patient and fracture characteristics may influence the decision to pursue surgical versus nonsurgical treatment in patients aged >65 years sustaining a DRF. Methods We queried our institutional DRF database for patients aged >65 years who presented to a single academic, tertiary center hand clinic over a 5-year period. In all, 164 patients treated operatively were identified, and 162 patients treated nonoperatively during the same time period were selected for comparison (total N = 326). Demographic variables and fracture-specific variables were recorded. Patient and fracture characteristics between the groups were compared to determine which variables were associated with each treatment modality (operative or nonoperative). Results The average age in our cohort was 72 (SD: 11) years, and 274 patients (67%) were women. The average Charlson Comorbidity Index (CCI) was 4.1 (SD: 2.1). The CCI is a validated tool that predicts 1-year mortality based on patient age and a list of 22 weighted comorbidities. Factors associated with operative treatment in our population were largely related to the severity of the injury and included increasing dorsal tilt (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.05-1.12; P < .001) and AO Classification type C fractures (OR, 5.42; 95% CI, 2.35-11.61; P < .001). Increasing CCI was the only factor independently associated with nonoperative management (OR, 0.84; 95% CI, 0.72-0.997; P = .046). Conclusion Fracture severity is a strong driver in the decision to pursue operative management in patients aged >65 years, whereas increasing CCI predicts nonoperative treatment.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques

1998 ◽  
Vol 1635 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Wael H. Awad ◽  
Bruce N. Janson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Linear Regression ◽  
Prediction Models ◽  
Washington State ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Training Process ◽  
Truck Accidents ◽  
High Level

Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results.

Artificial intelligence based prediction models: sales forecasting application in automotive aftermarket

2021 ◽  
pp. 1-13
Author(s):  
Mert Girayhan Türkbayrağí ◽  
Elif Dogu ◽  
Y. Esra Albayrak
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Demand Characteristic ◽  
Product Portfolio ◽  
Ann Model ◽  
Nonlinear Relationships ◽  
Relationship Of ◽  
Automotive Aftermarket

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance.

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