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نتیجه جستجو - مرگ و میر

تعداد مقالات یافته شده: 31
ردیف عنوان نوع
1 Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
توسعه الگوریتم های یادگیری ماشین برای پیش بینی مرگ و میر در آبسه اپیدورال ستون فقرات-2019
BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning. PURPOSE: The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA. STUDY DESIGN/SETTING: Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016. PATIENTS SAMPLE: Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA. OUTCOME MEASURES: In-hospital and 90-day postdischarge mortality. METHODS: Five machine learning algorithms (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed and assessed by discrimination, calibration, overall performance, and decision curve analysis. RESULTS: Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count, neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/. CONCLUSIONS: Machine learning algorithms show promise on internal validation for prediction of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms inindependent populations.
Keywords: Artificial intelligence | Healthcare | Machine learning | Mortality | Spinal epidural abscess | Spine surgery
مقاله انگلیسی
2 Patient Clustering Improves Efficiency of Federated Machine Learning to Predict Mortality and Hospital Stay Time Using Distributed Electronic Medical Records
وشه بندی بیمار باعث افزایش کارآیی یادگیری ماشین فدرال برای پیش بینی مرگ و میر و مدت زمان ماندن بیمارستان با استفاده از سوابق پزشکی الکترونیکی توزیع شده-2019
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geographical locations, and learnt one model for each community. Throughout the learning process, the data was kept local at hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline federated machine learning (FL) algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities’ performance difference could be explained by how dissimilar one community was to others.
Keywords: distributed clustering | autoencoder | federated machine learning | non-IID | critical care
مقاله انگلیسی
3 ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
ISeeU: یادگیری عمیق قابل تفسیر برای پیش بینی مرگ و میر در بخش مراقبت های ویژه-2019
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight into the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multiscale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Results show our model attains a ROC AUC of 0.8735 (± 0.0025) which is competitive with the state of the art of Deep Learning mortality models trained on MIMIC-III data, while remaining interpretable. Supporting code can be found at https://github.com/ williamcaicedo/ISeeU.
Keywords: Deep learning | MIMIC-III | ICU | Shapley Values
مقاله انگلیسی
4 Deep-learning model for predicting 30-day postoperative mortality
مدل یادگیری عمیق برای پیش بینی مرگ و میر 30 روز بعد از عمل-2019
Background: Postoperative mortality occurs in 1e2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deeplearning algorithm predicting postoperative 30-day mortality. Methods: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. Results: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835e0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790e0.860), random forest (0.848; 95% CI: 0.815e0.882), support vector machine (0.836; 95% CI: 0.802e870), and logistic regression (0.837; 95% CI: 0.803e0.871). Conclusions: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient’s risk for postoperative mortality.
Keywords: anaesthesiology | deep learning | machine learning | postoperative complications | risk prediction | surgery
مقاله انگلیسی
5 Infant deaths in Pudong, Shanghai, China: A retrospective study of the police data and comparison with the centre for disease control data
مرگ و میر نوزادان در پودونگ ، شانگهای ، چین: یک مطالعه گذشته نگر از داده های پلیس و مقایسه آن با مرکز کنترل داده های بیماری-2019
In China, every year many infants (< 1 year) are abandoned, but abandonment related deaths are rarely reported. In this study, the police records of infant deaths in Pudong, Shanghai have been explored, then, the police data were compared with the corresponding Centre for Disease Control (“CDC”) data. During the period 2004–2017, a total of 297 infant deaths were recorded by the police, including 87 sudden natural deaths (occurred outside hospitals) and 210 unnatural deaths. The CDC data were retrieved from a Chinese article. Joinpoint Trend Analysis was used to evaluate the trend of the police records on infant deaths, and Poisson regression was used to calculate the mortality rate ratio (“RR”) by gender and places of origin (local, migrant, unknown identity). It is observed that infants born to migrant mothers were more vulnerable to sudden natural deaths than their local counterparts (RR: 4.6, 95% CI: 2.8 to 8.1). 8 abandonment deaths and 187 suspicious abandonment deaths were spotted. Births to unmarried mothers, severe illnesses, and deformities could be important risk factors resulting in abandonments. However, the female gender was not a reason that led to the abandonments. Infant deaths related to abandonments/suspicious abandonments rapidly declined during the period 2004–2017. The CDC data showed that 27 infants died of unnatural causes during the period 2002–2013, while the police data recorded 182 unnatural infant deaths during the period 2004–2013, a shorter period but more unnatural deaths. Thus, the CDC data could have underreported the infant deaths.
Keywords: Infant | Unnatural death | Abandonment | China
مقاله انگلیسی
6 Big Data and Clinical Research in Traumatic Brain Injury
داده های بزرگ و تحقیقات بالینی در ضایعات مغزی آسیب دیده-2018
“T alk and die” in traumatic brain injury (TBI) was initially described in 1975 by Reilly et al clinically deteriorated after initial evaluation suggested 1 in patients who signs of mild brain injury. Description of the talk and die phenomenon evolved into theories of secondary injury in TBI, in which postinjury inflammation, edema, and loss of autoregulation exacerbated the primary injury, and was associated with worse outcomes.2 Since the 1970s, advances in medical care have allowed for a much better understanding of TBI as a multifaceted disease process. Initial clinical evaluation is interpreted in a nexus of imaging, neuromonitoring, and critical care. Over time, we have learned that “talking” after TBI tells only a small part of the story.
Key words : Mortality ، Risk factor ، Skull fracture ، Subdural hematoma ، Talk and die ، Traumatic brain injury
مقاله انگلیسی
7 Acute adrenal crisis and mortality in adrenal insufficiency: Still a concern in 2018!
بحران و مرگ و میر ناشی از آدرنال در نارسایی بالای آدرنال: در 2018 هنوز نگرانی وجود دارد!-2018
Despite established replacement therapy, mortality in patients suffering from chronic adrenal insufficiency is increasing. This may be partly explained by the fact that lack of adrenal stress hormones impairs the body’s capacity to deal adequately with stress situations, resulting in life threatening adrenal crises. Since many such situations are of rapid onset, concepts that allow for quick response to emergencies are particularly important. Optimal education for patients and relatives, improved awareness on the part of health professionals and the development of new easy-to-use drugs for acute therapy are of prime importance.
Keywords: Adrenal insufficiency; Adrenal crisis; Mortality; Morbidity; Infection; Addison’s disease
مقاله انگلیسی
8 Mortality prediction based on imbalanced high-dimensional ICU big data
پیش بینی مرگ و میر بر اساسداده های بزرگ ICU عدم تعادل بعد بالا -2018
With the development of biomedical equipment and healthcare level, large amounts of data have been brought out in hospital, especially in Intensive Care Unit (ICU). However, how to better exploit meaningful information from these rich data still remains a challenge. This paper focuses on ICU mortality prediction, which is a typical example of second use of ICU big data. Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution and time asynchronization etc. To solve these challenges, a series of analytical methods and tools, including variables selection, preprocessing, feature extraction & feature selection and predictive modeling, have been utilized and developed. High-dimensional and unbalanced natures of the ICU data badly affect the performance of classifiers. We modified the cost-sensitive principal component analysis (CSPCA), which is denoted by MCSPCA, to handle these problems in feature extraction stage. As for parameter optimization, a variant of standard particle swarm optimization called chaos particle swarm optimization (CPSO) was adopted for its capacity of finding optimal solution. In order to obtain the best prediction model, different algorithms were investigated and their AUC performances were evaluated in a large real world benchmark data. The final results show that our proposed method improved the performance of the traditional machine learning methods, in which the support vector machine (SVM) reach best AUC performance of 0.7718. This study gives a paradigm to handle similar problems in big health data and helps promote healthcare services.
Keywords: Health data processing ، Analytical tools ، Modified cost-sensitive principal ، component analysis ، Support vector machine ، Chaos particle swarm optimization
مقاله انگلیسی
9 کاهش درگیری انسان و تمساح در سریلانکا: منطقه مورد مطالعه رودخانه نیلوالا در ناحیه مارتا
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 15
انسان ها و تمساح ها در سال های زیادی در سریلانکا به ویژه در نزدیکی منطقه رودخانه نیلوالا در ناحیه ماترا، زندگی می کنند، اما مرگ و میر آنها به ندرت رخ می¬دهد. با این حال، طی دهه گذشته، برای انسان ها در منطقه رودخانه نیلوالا، عمدتا طی سال های 2005، 2008، 2009، 2012، 2013، 2014 و 2015، خطرات وجود تمساح ها افزایش یافته است. حدود 26 حمله و کشته شدن 18 انسان توسط تمساح های آب شور از سال 2000 در این منطقه ثبت شده است. در مقابله با این حملات، مردم این منطقه چندین تمساح را کشته و تمساح های آب شور در معرض خطر قرار گرفته اند. بنابراین، بررسی کاهش درگیری بین انسان و تمساح در منطقه رودخانه نیلوالا در سریلانکا ضروری است. این مطالعه اساسا بر اساس داده های اولیه و ثانویه صورت گرفت. داده های اولیه از مصاحبه های نیمه ساختاری فراهم شد. حجم نمونه شامل چهل و پنج (45) پاسخ دهنده بود. داده های ثانویه از طریق کتاب های منتشر شده، گزارش های تحقیق، سمپوزیوم، مقالات مجلات و وب سایت ها و غیره جمع آوری شده است. داده ها از منابع مختلف و با استفاده از روش های کیفی و کمی آنالیز فراهم شد و در قالب نقشه ، متون و جداول ارائه شده است. این مطالعه نشان داد که حفاری شن ، افزایش جمعیت، استفاده از رودخانه برای نیازهای روزانه مانند شرب، حمام ، شستشوی لباس و ماهیگیری، ساختمان های غیر مجاز در رودخانه، جنگل های پوشیده ازخاروخاشاک، جریان آهسته رودخانه، علت اصلی درگیری تمساح انسان در منطقه رودخانه نیلوالا است. این مطالعه نشان می دهد که پیلادووا، فورت و تیهگدا، آسیب پذیر ترین مناطق درگیری های انسان و تمساح است. "Kimbulkotuwa" یا (تمساح بدون محوطه)Crocodile Excluding Enclosure (CEEs) یک روش اصلی برای مقابله با درگیری های انسان و تمساح در این منطقه است.
کليدواژگان: درگیری انسان-تمساح | کاهش | رود نیلوالا | فقر
مقاله ترجمه شده
10 مجموعه داده ها از نمونه های ارتش متحد برای مطالعه انتخاب محل و شبکه های اجتماعی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 15
ما داده های عمومی موجود را که توسط پروژه برنامه های شاخص های اولیه ارزیابی شده NIA اغلب به عنوان داده های ارتش متحد را تهیه کردیم و زیر مجموعه ای از این داده ها را در " شبکه های اجتماعی پایدار" مورد استفاده قرار دادیم: سربازان کارآزموده در جنگ داخلی که در طول زندگی با یکدیگر همکاری می کنند. " (کاستا و همکاران، آینده) [1]. این زیرمجموعه داده می تواند برای تکمیل و تکثیر استفاده شود و همچنین نشان می دهد که چگون داده های اصلی تکمیلی مشتق شده از بایگانی اداری می تواند مورد استفاده قرار گیرد.
کلمات کلیدی: شبکه های اجتماعی | مهاجرت | مرگ و میر
مقاله ترجمه شده
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