دانلود مقاله انگلیسی رایگان:ترکیبی از یک سیستم منبع تحلیلی داده های بزرگ با یک الگوریتم هوش مصنوعی برای شناسایی آستانه های دوز پرتودرمانی از نظر بالینی برای دیسفاژی در بیماران سر و گردن - 2020
بلافاصله پس از پرداخت دانلود کنید
دانلود مقاله انگلیسی داده های بزرگ رایگان
  • Combination of a big data analytics resource system with an artificial intelligence algorithm to identify clinically actionable radiation dose thresholds for dysphagia in head and neck patients Combination of a big data analytics resource system with an artificial intelligence algorithm to identify clinically actionable radiation dose thresholds for dysphagia in head and neck patients
    Combination of a big data analytics resource system with an artificial intelligence algorithm to identify clinically actionable radiation dose thresholds for dysphagia in head and neck patients

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Combination of a big data analytics resource system with an artificial intelligence algorithm to identify clinically actionable radiation dose thresholds for dysphagia in head and neck patients


    ترجمه فارسی عنوان مقاله:

    ترکیبی از یک سیستم منبع تحلیلی داده های بزرگ با یک الگوریتم هوش مصنوعی برای شناسایی آستانه های دوز پرتودرمانی از نظر بالینی برای دیسفاژی در بیماران سر و گردن


    منبع:

    Sciencedirect - Elsevier - Advances in Radiation Oncology, Journal Pre-proof: doi:10:1016/j:adro:2019:12:007


    نویسنده:

    Charles S. Mayo, PhD, Michelle Mierzwa, MD, Jean M. Moran, PhD, Martha M. Matuszak, PhD, Joel Wilkie, MD, Grace Sun, BS, John Yao, PhD, Grant Weyburn, BS, Carlos J. Anderson, PhD, Dawn Owen, MD, Arvind Rao, PhD


    چکیده انگلیسی:

    Purpose/Objective(s): We combined clinical practice changes, standardizations and technology to automate aggregation, integration and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). We then developed novel artificial intelligence (AI) algorithms, coupled to the BDARS, to identify structure DVH metrics associated with dysphagia. Materials/Methods: From the BDARS harmonized data of ≥ 22,000 patients, we identified 132 patients recently treated for head and neck cancer who also demonstrated dysphagia scores that worsened from base line to a maximum grade ≥ 2. We developed a method that used both physical and biologically corrected (α/β =2.5) DVH curves to test both absolute and percentage volume based DVH metrics. Combining a statistical categorization algorithm with machine learning (SCA-ML) the method provided more extensive detailing of response threshold evidence than either approach alone. A sensitivity guided, minimum input ML model was iteratively constructed to identify the key structure DVH metric thresholds. Results: Seven swallowing structures producing 738 candidate DVH metrics were ranked for association with dysphagia using SCA-ML scoring. Structures included superior pharyngeal constrictor (SPC), inferior pharyngeal constrictor (IPC), larynx, esophagus. Bilateral parotid and submandibular gland (SG) structures were categorized by relative mean dose (e.g SG_High, SG_Low) as a dose vs tumor centric analog to contra and ipsilateral designations. Structure – DVH metrics with high SCA-ML scores included SPC:D20%[EQD2Gy] ≥ 47.7, SPC:D25%[Gy] ≥ 50.4, IPC:D35%[Gy] ≥ 61.7, Parotid_Low:D60%[Gy] ≥ 13.2 and SG_High:D35%[Gy] ≥ 61.7. Larynx:D25%[Gy] ≥ 21.2 and SG_Low:D45%≥28.2 had high SCA-ML scores, but were segmented on fewer than 90% of plans. A model based on SPC:D20%[EQD2Gy] alone had sensitivity and AUC of 0.88 ±0.13 and 0.74 ± 0.17 respectively. Conclusion: This study provides practical demonstration of combining big data with AI to increase volume of evidence in clinical learning paradigms


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 33
    حجم فایل: 2486 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi