Clinical characteristics and prognostic factors for Crohn’s disease relapses using natural language processing and machine learning: a pilot study

Gomollón, Fernando; Gisbert, Javier P.; Guerra, Iván; Plaza, Rocío; Villarroya, R. Pajares; Almazán, Luis Moreno; Martín, M. Carmen; Antonaya, Mercedes Domínguez; Mendoza, María Isabel Vera; Aparicio, Jesús; Martı́nez, Vicente; Tagarro, Ignacio; Fernández-Nistal, Alonso; Lumbreras, Sara; Maté, Claudia; Montoto, Carmen

doi:10.1097/meg.0000000000002317

Cited by 24 publications

(16 citation statements)

References 66 publications

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“…To access the unstructured clinical information in EHRs, we used Savana's EHRead 1 technology [19][20][21][22][23][24]. Based on NLP and machine learning, this technology facilitates the extraction of Study design and timeline.…”

Section: Extracting the Unstructured Free Text From Ehrsmentioning

confidence: 99%

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing

et al. 2022

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Introduction and objectives Patients with type 2 diabetes (T2D) and stable coronary artery disease (CAD) previously revascularized with percutaneous coronary intervention (PCI) are at high risk of recurrent ischemic events. We aimed to provide real-world insights into the clinical characteristics and management of this clinical population, excluding patients with a history of myocardial infarction (MI) or stroke, using Natural Language Processing (NLP) technology. Methods This is a multicenter, retrospective study based on the secondary use of 2014–2018 real-world data captured in the Electronic Health Records (EHRs) of 1,579 patients (0.72% of the T2D population analyzed; n = 217,632 patients) from 12 representative hospitals in Spain. To access the unstructured clinical information in EHRs, we used the EHRead® technology, based on NLP and machine learning. Major adverse cardiovascular events (MACE) were considered: MI, ischemic stroke, urgent coronary revascularization, and hospitalization due to unstable angina. The association between MACE rates and the variables included in this study was evaluated following univariate and multivariate approaches. Results Most patients were male (72.13%), with a mean age of 70.5±10 years. Regarding T2D, most patients were non-insulin-dependent T2D (61.75%) with high prevalence of comorbidities. The median (Q1-Q3) duration of follow-up was 1.2 (0.3–4.5) years. Overall, 35.66% of patients suffered from at least one MACE during follow up. Using a Cox Proportional Hazards regression model analysis, several independent factors were associated with MACE during follow up: CAD duration (p < 0.001), COPD/Asthma (p = 0.021), heart valve disease (p = 0.031), multivessel disease (p = 0.005), insulin treatment (p < 0.001), statins treatment (p < 0.001), and clopidogrel treatment (p = 0.039). Conclusions Our results showed high rates of MACE in a large real-world series of PCI-revascularized patients with T2D and CAD with no history of MI or stroke. These data represent a potential opportunity to improve the clinical management of these patients.

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Section: Extracting the Unstructured Free Text From Ehrsmentioning

confidence: 99%

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing

et al. 2022

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“…With an estimated global prevalence ranging between 4.1 and 8.4 per 100,000 individuals 7 , ALS is considered a rare disease. From a clinical standpoint, diseases with low prevalence are best understood using population-based registries with available follow-up information across large numbers of patients 2 , 8 , 9 . A paramount source of real-world data (RWD) with these features is the clinical information in patients’ Electronic Health Records (EHRs).…”

Section: Introductionmentioning

confidence: 99%

“…A paramount source of real-world data (RWD) with these features is the clinical information in patients’ Electronic Health Records (EHRs). Particularly, the extraction and analysis of the unstructured clinical information in EHRs using artificial intelligence and machine learning tools (most notably Natural Language Processing, NLP) has yielded novel insights into patients’ clinical characteristics, disease management, prognosis, and epidemiological trends in different therapeutic areas 9 – 16 .…”

Section: Introductionmentioning

confidence: 99%

“…Using the EHRead® NLP technology 9 , 11 , 15 – 19 to analyze the unstructured clinical information in EHRs, this study aimed to identify ALS patients from the entire source population in the SESCAM Healthcare Network (Castilla-La Mancha, Spain) to (a) characterize their demographic and clinical profile, (b) determine the delay between symptom onset and diagnosis, and (c) determine the timing of disease-specific clinical events during the course of the disease.…”

Section: Introductionmentioning

confidence: 99%

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Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence

Segura

Medrano²,

Collazo³

et al. 2023

Sci Rep

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Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6–18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.

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“…Moreover, this study investigated the probability of developing MACE after presentation of CAD in T2DM patients and the potential risk or protective factors associated with its occurrence within this population. To do so, we used EHRead ® (Madrid, Spain), a technology that applies Natural Language Processing (NLP) and machine learning, to extract, organize, and analyze the unstructured clinical information that health professionals register in patients’ electronic health records (EHRs) [ 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing

González‐Juanatey

Anguita‐Sánchez

Barrios

et al. 2022

JCM

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Patients with Type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) are at high risk of developing major adverse cardiovascular events (MACE). This is a multicenter, retrospective, and observational study performed in Spain aimed to characterize these patients in a real-world setting. Unstructured data from the Electronic Health Records were extracted by EHRead®, a technology based on Natural Language Processing and machine learning. The association between new MACE and the variables of interest were investigated by univariable and multivariable analyses. From a source population of 2,184,662 patients, we identified 4072 adults diagnosed with T2DM and CAD (62.2% male, mean age 70 ± 11). The main comorbidities observed included arterial hypertension, hyperlipidemia, and obesity, with metformin and statins being the treatments most frequently prescribed. MACE development was associated with multivessel (Hazard Ratio (HR) = 2.49) and single coronary vessel disease (HR = 1.71), transient ischemic attack (HR = 2.01), heart failure (HR = 1.32), insulin treatment (HR = 1.40), and percutaneous coronary intervention (PCI) (HR = 2.27), whilst statins (HR = 0.73) were associated with a lower risk of MACE occurrence. In conclusion, we found six risk factors associated with the development of MACE which were related with cardiovascular diseases and T2DM severity, and treatment with statins was identified as a protective factor for new MACE in this study.

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Clinical characteristics and prognostic factors for Crohn’s disease relapses using natural language processing and machine learning: a pilot study

Abstract: Supplemental Digital Content is available in the text.

Cited by 24 publications

References 66 publications

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing

Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence

Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing

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