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Transforming the care and research of inherited metabolic disorders through patient data.

flok: Our Stories, Our Data, Our Future
Designed by and for individuals with inborn errors of protein metabolism including PKU, MSUD, Tyrosinemia, Homocystinuria, Organic Acidemias, and Urea Cycle Disorders.

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Good enough
isn't good enough

Since their discovery, inborn errors of protein metabolism have been managed through balancing diet and labwork, with some recent Rx intervention. But labs & diet alone aren't the whole story.

flok is dedicated to using patient data to help develop new understandings of these disorders and their impacts on our physical, mental, emotional, and neuropsychological health. 

Only when we fully understand these disorders can we guide research and the development of treatments that address more than the few endpoints we've been measuring for decades. 

The flok research platform will employ machine learning to drive these insights. Learn more about the data we'll use and the experts we've gathered for this work

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Unlocking the Potential of Patient Data

How flok AI Will Change the IEM Landscape

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    flok AI utilizes cutting-edge biometric technology to track health indicators specific to metabolic disorders, including blood levels, amino acid profiles, and metabolic rate. These biometric measurements provide valuable insights into a patient's metabolic health status and help healthcare providers personalize treatment plans for better outcomes. 

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    By tracking food intake and analyzing nutrient content, flok AI helps patients make informed decisions about their diet, leading to improved health outcomes and better management of chronic conditions. flok AI's machine learning algorithms analyze patient dietary patterns to provide insight into nutritional patterns across individuals and disorders.

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    Patient-Reported Outcomes

    Patient-reported outcomes, including symptom severity and quality of life measures can be incorporate into treatment plans, healthcare providers can better understand the patient experience and make more informed decisions about care. flok AI's patient-reported outcome measures also help researchers gain a deeper understanding of disease progression and treatment efficacy. 

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    Example: Seizure Prediction

    Predicted Seizures in those with epilepsy 

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    Example: Heart Rate Variability

    Correlations between heart rate variability and other markers of heath.