Hilyoon’s Post

Predicting flares in rheumatic diseases with machine learning Rheumatic diseases, such as axial spondyloarthritis (axSpA) and rheumatoid arthritis (RA), are marked by unpredictable disease flares that adversely impact quality of life and long-term outcomes.

Predicting flares in rheumatic diseases with machine learning Rheumatic diseases, such as axial spondyloarthritis (axSpA) and rheumatoid arthritis (RA), are marked by unpredictable disease flares that adversely impact quality of life and long-term outcomes.

🌬️As breath analysis technology advances, diagnosing diseases becomes more comfortable and non-invasive. One of such diseases is cystic fibrosis. Cystic fibrosis, a genetic disorder affecting over 80,000 people worldwide, primarily impacts the gastrointestinal and pulmonary systems, leading to severe health challenges. Traditional diagnostic methods can be uncomfortable for patients. Breath analysis offers a game-changing solution with its ease and reliability. By detecting volatile organic compounds (VOCs) in breath, this method provides critical insights into the disease's progression and inflammation levels. Read more: https://lnkd.in/d9ySFxU5 #CysticFibrosis #BreathAnalysis #HealthcareInnovation #NonInvasiveDiagnostics #MedicalTechnology

Why rely on outdated claims-based datasets, when you can access nightly #EHR data to study #aorticstenosis outcomes at scale, track disease progression, understand patient journey's and the rich #clinicalhistory imbedded in physician notes? See below to unlock the power of Truveta today 👇 #rwe #cardiogy #heartdisease #cardiacconditions #healthcaredata

Truveta Research examined echocardiogram data extracted from clinical notes to classify patients with aortic stenosis at scale, and track disease progression. Learn about the study: https://tr.vet/3OWPGZ4 #EchoData #EchocardiogramData #Echocardiogram #AorticStenosis #AorticStenosisTreatment #CardiovascularData #CardioData #HealthData #HealthcareData #HealthAI #HealthInnovation #AI #Innovation #HealthcareAI #HealthcareInnovation #MedicalData #MedicalAI #MedicalInnovation

PALO ALTO, Calif., April 30, 2024 /PRNewswire/ -- Verantos, the global leader in high-validity real-world evidence at scale, today announced the launch of its Inflammatory Bowel Disease Pragmatic Registry, which provides rich and reliable data on ulcerative colitis and Crohn's disease patients, including specific subgroups to life sciences organizations. A pragmatic registry is a condition-specific, high-quality data set generated using artificial intelligence on routinely collected real-world data. By combining unstructured and structured data from electronic health records with linkage to claims and mortality data, Inflammatory Bowel Disease Pragmatic Registry offers an unparalleled view into disease severity, treatments, symptom control, resource utilization, and clinical outcomes variables.

Reveal for Liver Disease is setting a new benchmark in early liver disease detection. By utilizing complex datasets from millions of patients, it creates enhanced diagnostic capabilities compared to traditional methods. Dive into the underlying tech and data making it happen. https://hubs.ly/Q02YKhQt0 #LiverDisease #EarlyDetection #MASH #MASLD

ALSO IN THIS ISSUE Bird Flu Update Is microRNA the key to diagnosis of neurodegenerative disease? #sensitiveandspecific #diagnostics #biomedicaldiagnostics #precisionmedicine #birdflu #h5n1 #CBC #completebloodcount #neurodegenerativedisease #microRNA #ALS #Alzheimers

Overcoming Racial and Ethnic Biases in the Diagnosis of Patients With Alpha-1 Antitrypsin Deficiency in the United States Using a Machine-Learning Model. The Objective: To develop a prediction model to identify symptomatic patients of different races and ethnicities with likely risk of AATD using claims data from a large US database. Implicit and explicit biases are among many factors that contribute to disparities in health and health care. (Tackling Implicit Bias in Health Care Published July 9, 2022 N Engl J Med 2022;387:105-107 DOI: 10.1056/NEJMp2201180 ) In partnership with Takeda, we took to designing a machine learning process to find likely candidates and overcome racial biases in the detection of this disease that may result in serious lung or liver disease. AATD is largely underdiagnosed, with an estimated prevalence of 100,000 individuals with AATD in the US; however, fewer than 10,000 individuals are diagnosed (Ashenhurst JR, et al. Chest. 2022;161(2):373-381.) Previously, AATD was thought to affect only White individuals of European descent. Recent studies have shown that people of different races and ethnicities have genotypes consistent with those with moderate-to-severe AATD-related lung disease. (Quinn M, et al. Ther Clin Risk Manag. 2020;16:1243-1255. de Serres FJ, Blanco I. Ther Adv Respir Dis. 2012;6(5):277-295.) The Process: Data from the Komodo Health US claims database (April 26, 2016 to January 31, 2023) were divided into “positive,” “negative,” and “target” cohorts. A machine-learning model for detecting AATD was trained on positive and negative cohorts without using codes revealing AATD diagnosis and treatment.The learned model was applied to the target cohort to flag patients with likely undiagnosed AATD. Results: This approach produced a highly performant prediction model capable of detecting undiagnosed people living with AATD, validated by expert clinicians. (For a deeper look at how this unique ML process could be applied to other indications, please message us directly.)

By leveraging data from millions of patient records, our team has developed a sophisticated risk prediction #AI model that goes beyond traditional liver disease screening indicators. What's our approach? It's worth a few minutes to read. Andrei shares how we apply disciplined analytical methods to uncover subtle patterns that may indicate early-stage liver disease. This innovative risk stratification should help flag patients months or years earlier than conventional diagnosis methods.