Publications by authors named "Wooseok Ha"
Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions.
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- Delirium occurs in up to 50% of patients after high-risk surgeries and can negatively impact long-term health outcomes; the study aimed to use machine learning to predict this condition using sleep data from polysomnography (PSG) and questionnaires.
- Researchers analyzed data from 912 adults who had surgeries under general anesthesia, identifying key risk factors for delirium such as medication use, surgery duration, and sleep issues like REM episodes and daytime sleepiness.
- The machine learning model showed strong predictive capabilities, achieving an AUC of 0.84 when combining clinical and PSG data, emphasizing the importance of sleep-related factors in identifying patients at risk for delirium post-surgery.
Background: This study aimed to elucidate the nature and extent of the associations between diabetes mellitus (DM) and migraine through a systematic review and meta-analysis.
Methods: We searched the PubMed, Web of Science, and Scopus databases without a specified start date until June 2, 2024. Cross-sectional and cohort studies analyzing the risk of migraine in individuals with DM and vice versa were included.
Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification.
View Article and Find Full Text PDFArtificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload.
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- Migraine is a major global health issue, and triptans, introduced in the 1990s, have improved treatment options, but usage data from Asian countries, especially Korea, is limited.
- This study analyzed triptan usage trends in Korea from 2002 to 2019 using national health insurance data, focusing on prescription quantities, costs, and variations among medical specialists.
- Results showed a significant increase in triptan prescriptions and use, with a notable 24-fold rise in tablets prescribed; however, only about 10% of migraine patients received triptans by 2019, with higher prescribing rates among neurologists compared to other specialists.
Background: The pathogenesis of migraine remains unclear; however, a large body of evidence supports the hypothesis that immunological mechanisms play a key role. Therefore, we aimed to review current studies on altered immunity in individuals with migraine during and outside attacks.
Methods: We searched the PubMed database to investigate immunological changes in patients with migraine.
Advances in Exercise in the Clinical Trials of Migraine: A Scoping Review.
Curr Pain Headache Rep
August 2024
Purpose Of Review: This review aimed to investigate emerging evidence regarding the effectiveness of exercise for migraines, focusing on the results of recent trials. Additionally, it explored the possibility of exercise as a treatment for migraines.
Recent Findings: Between 2020 and 2023, five, four, one, and two trials were conducted regarding the effect of aerobic exercise, anaerobic exercise, Tai Chi, and yoga, respectively, on migraine; all studies showed significant effects.
Introduction: We aimed to investigate the risk factors associated with poststroke epilepsy (PSE) among patients with different subtypes of stroke, focusing on age-related risk and time-varying effects of stroke subtypes on PSE development.
Methods: A retrospective, nationwide, population-based cohort study was conducted using Korean National Health Insurance Service-National Sample Cohort data. Patients hospitalized with newly diagnosed stroke from 2005 to 2015 were included and followed up for up to 10 years.
Article Synopsis
- A study investigated the link between migraine and the risk of developing Parkinson's disease (PD) using data from the Korean National Health Insurance System.* -
- The research included over 214,000 patients with migraine and found that those with migraine had a 1.35 times higher risk of developing PD compared to those without migraine, despite a similar incidence between migraine patients with and without aura.* -
- The study concluded that managing comorbid conditions and chronic migraine may help reduce PD incidence, and further clinical trials are needed to clarify this relationship.*
Article Synopsis
- Early diagnosis and treatment of meningitis and encephalitis are critical, prompting a study to create and test an AI model for determining the causes of these conditions.
- The study involved analyzing data from 283 patients to develop the AI model and validating it with 220 additional patients, focusing on four potential causes: autoimmunity, bacteria, virus, and tuberculosis.
- The AI model significantly outperformed human clinicians in identifying the causes of meningitis and encephalitis, achieving high accuracy metrics, which underscores its potential for improving patient outcomes in these severe conditions.*
Spatial population genetic data often exhibits 'isolation-by-distance,' where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al.
View Article and Find Full Text PDFPurpose: We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x-ray spectrum that can accurately model the x-ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system.
Methods: Spectrum estimation is posed as an optimization problem with x-ray spectrum as unknown variables, and a Kullback-Leibler (KL)-divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process.