Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods

Friberg, Julia; Qazi, Abdul H.; Boyle, Brenden; Franciscus, Carrie L.; Vaughan-Sarrazin, Mary; Westerman, Dax; Patterson, Olga V.; Parr, Sharidan K.; Matheny, Michael E.; Arya, Shipra; Smolderen, Kim G.; Lund, Brian C.; Gobbel, Glenn T.; Girotra, Saket

doi:10.1161/circinterventions.121.011092

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Thus, our goal is to develop a model that can detect and predict the progression of heart disease and CAD from clinical notes. The prediction of heart disease risk factor using clinical and statistical approaches has attracted a lot of attention over the past ten years 16 – 20 because this process is very complex. Several techniques have been applied to clinical concept extraction such as simple pattern matching, statistical systems, and machine learning.…”

Section: Introductionmentioning

confidence: 99%

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques

Houssein

Mohamed

Ali

2023

Sci Rep

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Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease in clinical notes can help with disease progression modeling and clinical decision-making. Many studies have attempted to detect risk factors for heart disease, but none have identified all risk factors. These studies have proposed hybrid systems that combine knowledge-driven and data-driven techniques, based on dictionaries, rules, and machine learning methods that require significant human effort. The National Center for Informatics for Integrating Biology and Beyond (i2b2) proposed a clinical natural language processing (NLP) challenge in 2014, with a track (track2) focused on detecting risk factors for heart disease risk factors in clinical notes over time. Clinical narratives provide a wealth of information that can be extracted using NLP and Deep Learning techniques. The objective of this paper is to improve on previous work in this area as part of the 2014 i2b2 challenge by identifying tags and attributes relevant to disease diagnosis, risk factors, and medications by providing advanced techniques of using stacked word embeddings. The i2b2 heart disease risk factors challenge dataset has shown significant improvement by using the approach of stacking embeddings, which combines various embeddings. Our model achieved an F1 score of 93.66% by using BERT and character embeddings (CHARACTER-BERT Embedding) stacking. The proposed model has significant results compared to all other models and systems that we developed for the 2014 i2b2 challenge.

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Section: Introductionmentioning

confidence: 99%

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques

Houssein

Mohamed

Ali

2023

Sci Rep

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Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients

Song,

Tomihama,

Roh

et al. 2024

Annals of Vascular Surgery

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Long-term Outcomes of Peripheral Artery Disease In Veterans: Analysis of the PEripheral ARtery Disease Long-term Survival Study (PEARLS)

Girotra,

Li,

Vaughan-Sarrazin

et al. 2024

Preprint

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BackgroundContemporary research in peripheral artery disease (PAD) remains limited due to lack of a national registry and low accuracy of diagnosis codes to identify PAD patients in electronic health records.Methods & ResultsLeveraging a novel natural language processing (NLP) system that identifies PAD with high accuracy using ankle brachial index (ABI) and toe-brachial index (TBI) values, we created a registry of 103,748 patients with new onset PAD patients in the Veterans Health Administration (VHA). Study endpoints include mortality, cardiovascular (hospitalization for acute myocardial infarction or stroke) and limb events (hospitalization for critical limb ischemia or major amputation) and were identified using VA and non-VA encounters. The mean age was 70.6 years; 97.3% were males, and 18.5% self-identified as Black race. The mean ABI value was 0.78 (SD: 0.26) and the mean TBI value was 0.51 (SD: 0.19). Nearly one-third (32.4%) patients were currently smoking and 35.4% formerly smoked. Prevalence of hypertension (86.6%), heart failure (22.7%), diabetes (54.8%), renal failure (23.6%), and chronic obstructive pulmonary disease (35.4%) was high. At 1-year, 9.4% of patients had died. The 1-year incidence of cardiovascular events was 5.6 per 100 patient-years and limb events was 4.5 per 100 patient-years.ConclusionsWe have successfully launched a registry of >100,000 patients with a new diagnosis of PAD in the VHA, the largest integrated health system in the U.S. The ncidence of death and clinical events in our cohort is high. Ongoing studies will yield important insights regarding improving care and outcomes in this high-risk group.

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Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods

Cited by 3 publications

References 29 publications

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques

Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients

Long-term Outcomes of Peripheral Artery Disease In Veterans: Analysis of the PEripheral ARtery Disease Long-term Survival Study (PEARLS)

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