دانلود و نمایش مقالات مرتبط با Patient-specific::صفحه 1
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نتیجه جستجو - Patient-specific

تعداد مقالات یافته شده: 12
ردیف عنوان نوع
1 Distributed learning on 20 000+ lung cancer patients – The Personal Health Train
یادگیری توزیع شده بر روی 20 000+ بیمار مبتلا به سرطان ریه - آموزش بهداشت شخصی-2020
Background and purpose: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion: The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.
Keywords: Lung cancer | Big data | Distributed learning | Federated learning | Machine learning | Survival analysis | Prediction modeling | FAIR data
مقاله انگلیسی
2 Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery
استخراج داده های خاص و اختصاصی بیمار با فناوری های یادگیری ماشین برای پیش بینی لغو جراحی کودکان-2019
Background: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children’s risk of day-of-surgery cancellation. Methods and findings: We extracted five-year datasets (2012–2017) from the Electronic Health Record at Cincinnati Children’s Hospital Medical Center. By leveraging patient-specific information and contextual data, machine learning classifiers were developed to predict all patient-related cancellations and the most frequent four cancellation causes individually (patient illness, “no show,” NPO violation and refusal to undergo surgery by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI: [0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual causes of cancellation, “no show” and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an iterative step-forward approach was applied to identify key predictors which may inform the design of future preventive interventions. Conclusions: Our study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also families’ negative experiences.
Keywords: Pediatric surgery cancellation | Quality improvement | Predictive modeling | Machine learning
مقاله انگلیسی
3 Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery
استخراج داده های خاص و متنی از بیمار با فناوری های یادگیری ماشین برای پیش بینی لغو جراحی کودکان-2019
Background: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children’s risk of day-of-surgery cancellation. Methods and findings: We extracted five-year datasets (2012–2017) from the Electronic Health Record at Cincinnati Children’s Hospital Medical Center. By leveraging patient-specific information and contextual data, machine learning classifiers were developed to predict all patient-related cancellations and the most frequent four cancellation causes individually (patient illness, “no show,” NPO violation and refusal to undergo surgery by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI: [0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual causes of cancellation, “no show” and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an iterative step-forward approach was applied to identify key predictors which may inform the design of future preventive interventions. Conclusions: Our study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also families’ negative experiences.
Keywords: Pediatric surgery cancellation | Quality improvement | Predictive modeling | Machine learning
مقاله انگلیسی
4 ارزیابی مقاومتِ دریچه در دریچه های آئورت تقسیم بندی شده با استفاده از دینامیک سیالات محاسباتی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 29
تنگی دریچه آئورت با افزایش فشار بطن چپ وافت فشار دریچه ائورت همراه است . پزشکان بصورت مرتب از سونوگرافی داپلر بمنظور تخمین میزان شدت تنگی دریچه آئورت با براورد این افت فشار از سرعت جریان خون استفاده میکنند . با این حال ، این روش افت فشار تقریبی را براورد میکند ، و قادر به اندازه گیری فشار نسبی دیستال ریکاوری روی دریچه نیست . از انجا که افت فشار به جریان وابسته است ، بنابراین ارزیابی تاثیر چشمگیر تنگی روی بیمارانِ مبتلا به جریان پایین ،شیب غلظتی پایین بسیار دشوار است . پیشرفتهای اخیر در تکنیک های تقسیم بندی خاصِ بیماران ، انها را قادر به شبیه سازی دینامیک سیالات محاسباتی (CFD) از طریق دریچه ائورت میکند. در این مطالعه یک چارچوب شبیه سازی شده ارایه شده و از آن برای انالیز داده های مربوط به 18 بیمار استفاده شد . بطن و دریچه با استفاده از داده های بدست امده از تصویربرداری توموگرافی کامپیوتری چهار بعدی مجددا بازسازی شدند . حرکت بطن با استفاده از تصاویر پزشکی استخراج شد و بعنوان مدلی برای انقباض بطن و جریان خون متناظر به آن از طریق دریچه مورد استفاده قرار گرفت . ساده سازی چارچوب با تعریفِ دو مدل ساده سازی CFD انجام شد : مدل ساده وابسته به زمان و مدل حالت پایدار . ساده سازی مدل به دلایل چندی انجام شد بویژه در مورد افت فشار که بالاتر از 10 mmHg (میلیمتر جیوه) بود . علاوه بر این ، ما شاخص مقاومت دریچه را برای تعیین نتایج شبیه سازی پیشنهاد میکنیم . این شاخص را با معیارهای تعیین شده برای تصمیم گیری بالینی مورد مقایسه قرار دادیم ، برای مثال سرعت جریان خون و منطقه دریچه . مشخص شد که معیارهای اندازه گیری سرعت به تنهایی نمیتوانند میزان تنگی دریچه را منعکس نمیکنند . اینکار نشان میدهد که ترکیب داده های تصویر برداری چهار بعدی و CFD پتانسیلی برای بهبود معیارهای فیزیولوژیکی مربوط به اندازه گیری شدت تنگی دریچه ائورت خواهند بود .
واژگان کلیدی: تنگی دریچه آئورت | بیماری دریچه قلب | همودینامیک / دینامیک جریان خون | دینامیک سیالات محاسباتی | خاصِ بیمار .
مقاله ترجمه شده
5 A personalized diet and exercise recommender system for type 1 diabetes self-management: An in silico study
یک سیستم توصیه گر رژیم غذایی و ورزش برای خود مدیریت دیابت نوع 1: یک مطالعه سیلیکو-2019
Management of diet and exercise levels needs to be personalized for patients with Type 1 Diabetes (T1D) to reduce the number of hypoglycemia events and to achieve a good glycemic control. This study developed a model-based Recommender system that could provide an (optimal) personalized intervention on diet and exercise for T1D patients, which could be potentially implemented as a mobile application (app) for self-management of T1D in the future work. At each intervention time, the Recommender makes prediction of blood glucose based on a patient-specific model of glucose dynamics, and then provides optimal interventions, which could be a meal/snack size or a target heart rate during exercise, by minimizing a risk function with constraints under a future time horizon. Simulations were conducted to evaluate the Recommender through 30 virtual subjects generated from a modified UVa/Padova simulator with an added exercise-glucose subsystem. The performance of the Recommender was compared to two self-management schemes: the Starter scheme and the Skilled scheme, where the Skilled represents an off-line optimal scheme providing a lower bound on the risk index. Compared to the Starter, the Recommender reduced the mean Low Blood Glucose Index by 84% and reduced the Blood Glucose Risk Index by 49% (P < 0.05), and it had comparable performance as the Skilled. The Recommender also reduced the number of hypoglycemia events during and post-exercise compared to the Starter and the Skilled.
Keywords: Recommender system | Type 1 diabetes | Intervention | Self-management | Finite-horizon optimization
مقاله انگلیسی
6 Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring
هوش مصنوعی و آرتروپلاستی در یک مؤسسه واحد: برنامه های کاربردی دنیای واقعی آموزش ماشین به داده های بزرگ ، مراقبت های مبتنی بر ارزش ، سلامت موبایل و نظارت از راه دور بیمار-2019
Background: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine. Methods: In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement. Results: We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion. Conclusion: The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.
Keywords: machine learning | arthroplasty | value | big data | remote patient monitoring
مقاله انگلیسی
7 Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model
پیش بینی قبل از عمل از معیارهای ارزش و یک مدل پرداخت خاص برای بیمار برای آرتروپلاستی کامل باسن اولیه: توسعه و اعتبارسنجی یک مدل یادگیری عمیق-2019
Background: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. Methods: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. Results: The dynamic model demonstrated “learning” in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. Conclusion: The deep learning ANN demonstrated “learning” with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.
Keywords: deep learning | artificial intelligence | total hip | payment model | prediction
مقاله انگلیسی
8 Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model
معیار ارزش پیش بینی یادگیری عمیق قبل از عمل برای اولیه آرتروپلاستی کامل زانو: توسعه و اعتبار مدل شبکه عصبی مصنوعی-2019
Background: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity. Methods: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a riskbased PSPM. Results: The dynamic model demonstrated “learning” in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively. Conclusion: Our deep learning model demonstrated “learning” with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity.
Keywords: machine learning | total knee arthroplasty (TKA) | artificial neural network | deep learning | artificial intelligence
مقاله انگلیسی
9 Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System
پیش بینی مصارف صرع با استفاده از داده های بزرگ و یادگیری عمیق: به سوی سیستم سیار-2018
Background: Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individuals needs. Methods: Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. Results: The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. Conclusion: This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.
Keywords: Epilepsy ، Seizure prediction ، Artificial intelligence، Deep neural networks ، Mobile medical devices ، Precision medicine
مقاله انگلیسی
10 Study for development of a patient-specific 3D printed craniofacial medical device: Design based on 3D virtual biomodels/ CAD/ RP
مطالعه برای توسعه یک دستگاه پزشکی سر و صورت بیمار خاص چاپ سه بعدی: طراحی بر اساس مدل های بیولوژیکی RP/CAD-2018
Reconstructive surgery field is looking for sustainable alternatives on its procedures, specifically on affordable alternatives to manufacturing processes for medical devices. This paper outlines a design process to develop 3D printed medical devices for reconstructive surgery in a specific patient who had congenital, oncological or traumatic fracture cases in the face and/or skull. The implementation of the design process strategy was supported using endogenous capabilities and resources at Universidad Industrial de Santander, which could be applied in developing countries. So, this approach might offer solutions adapted to the South American population. For this reason, the research framework was a specific patient implant method, defined by modeling integration technologies, those supported by 3D virtual biomodels, computer-aided design, and rapid prototyping tools. A deep research has carried out with some surgeons within reconstructive surgery field, identifying needs and requirements for each case through an iterative process. Expected results of this paper were a workflow definition for 3D printed medical device development for a specific patient. Five cases were processed through the proposed workflow. From those cases, main identified outputs were related to devices such as implants, prosthesis surgical cutting guides, and pre-surgical planning to be performed. The proposed workflow showed strong viability implementation in future services among different academics and health facilities located in developing countries.
Keywords : patient-specific implant PSI | 3D printed medical device | biomodels | virtual engineering
مقاله انگلیسی
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