A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies
یک مدل پیش بینی مبتنی بر یادگیری ماشینی از تشکیل فیستول پس از براکی تراپی بینابینی برای بدخیمی های ژنتیکی بومی محلی-2019
PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. There is a clinical need to identify patients at high risk for fistula formation such that treatment may be managed to prevent this toxic side effect. This work aims to develop a fistula prediction model framework using machine learning based on patient, tumor, and treatment features. METHODS AND MATERIALS: This retrospective study included 35 patients treated at our institution using interstitial brachytherapy for various gynecological malignancies. Five patients developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction framework. A nonlinear support vector machine was used to build the prediction model. Sequential backward feature selection and sequential floating backward feature selection methods were used to determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling technique was used to generate synthetic fistula cases for model training. RESULTS: Seven mixed data features were selected by both sequential backward selection and sequential floating backward selection methods. Our prediction model using these features achieved a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity. CONCLUSIONS: A machine-learningebased prediction model of fistula formation has been developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy. This model may be clinically impactful pending refinement and validation in a larger series.
Keywords: Machine learning | Support vector machine | Interstitial brachytherapy | Gynecologic cancer
Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture
مدلهای یادگیری ماشینی می توانند پارگی آنوریسم را تشخیص دهند و ویژگیهای بالینی مرتبط با پارگی را شناسایی کنند-2019
- BACKGROUND: Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture. - METHODS: We performed a retrospective review of patients with intracranial aneurysms detected on vascular imaging at our institution between 2002 and 2018. The dataset was used to train 3 ML models (random forest, linear support vector machine [SVM], and radial basis function kernel SVM). Relative contributions of individual predictors were derived from the linear SVM model. - RESULTS: Complete data were available for 845 aneurysms in 615 patients. Ruptured aneurysms (n [ 309, 37%) were larger (mean 6.51 mm vs. 5.73 mm; P [ 0.02) and more likely to be in the posterior circulation (20% vs. 11%; P < 0.001) than unruptured aneurysms. Area under the receiver operating curve was 0.77 for the linear SVM, 0.78 for the radial basis function kernel SVM models, and 0.81 for the random forest model. Aneurysm location and size were the 2 features that contributed most significantly to the model. Posterior communicating artery, anterior communicating artery, and posterior inferior cerebellar artery locations were most highly associated with rupture, whereas paraclinoid and middle cerebral artery locations had the strongest association with unruptured status. -CONCLUSIONS: ML models are capable of accurately distinguishing ruptured from unruptured aneurysms and identifying features associated with rupture. Consistent with prior studies, location and size show the strongest association with aneurysm rupture.
Key words : Aneurysm | Aneurysm rupture | Artificial intelligence | Machine learning | Subarachnoid hemorrhage
Automatic staging model of heart failure based on deep learning
مدل مرحله بندی خودکار نارسایی قلبی مبتنی بر یادگیری عمیق-2019
Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learningyielded new techniques to train deep neural networks, which resulted in highly successful applica-tions in many pattern recognition tasks such as object detection and speech recognition. To improve thediagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based modelson combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-timeand dynamically. We employed the data segmentation and data augmentation pre-processing datasetto make the classification performance of the proposed architecture better. Specifically, this paper useconvolutional neural network (CNN) as a feature extractor instead of training the entire network toextract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine theabove feature set with other clinical features, feed the combined features to RNN for classification, andfinally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paperachieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% fortwo seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%,96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid tohelp clinicians confirm their diagnosis.
Keywords:Heart failure | Staging model | Deep learning | Deep CNN-RNN model
Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status
استفاده از یادگیری ماشین برای طبقه بندی افراد مبتلا به اختلال در مصرف الکل بر اساس وضعیت به دنبال یافتن درمان-2019
Objective: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD) seeks treatment.
Method: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADTs were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models.
Results: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy.
Interpretation: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying b7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment. Funding: Funding for this work was provided by the Intramural Research Programs of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA) and the Center for Information Technology (CIT).
Research in Context: Evidence Before This Study: Less than 10% of persons who meet lifetime criteria for Alcohol Use Disorder (AUD) receive treatment. As the etiology of AUD represents a complex interaction between neurobiological, social, environmental and psychological factors, low treatment utilization likely stems from barriers on multiple levels. Given this issue, it is important from both a research and clinical standpoint to determine what characteristics are associated with treatment utilization in addition to merely asking individuals if they wish to enter treatment. At the level of clinical research, if there are phenotypic differences between treatment and nontreatment-seekers that directly influence outcomes of early-phase studies, these phenotypic differences are a potential confound in assessing the utility of an experimental treatment for AUD. ...
Keywords: Machine learning | Treatment utilization | Alcohol Use Disorder
Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: A comparative investigation in machine learning paradigm
تعیین نشانگرهای زیستی مربوط به پیش بینی سرطان پستان با استفاده از خصوصیات آنتروپومتریک و کلینیکی: بررسی مقایسه ای در پارادایم یادگیری ماشین-2019
Early detection of breast cancer plays crucial role in planning and result of associated treatment. The purpose of this article is threefold: (i) to investigate whether or not clinical features obtained using routine blood analysis combined with anthropometric measure- ments can be utilized for envisaging breast cancer using predictive machine learning techniques; (ii) to explore the role of various machine learning components such as feature selection, data division protocols and classification to determine suitable biomarkers for breast cancer prediction; and (iii) to evaluate a recent database of clinical and anthropometric measurements acquired from normal individuals and individuals suffering from breast cancer. A database consisting of anthropometric and clinical attributes is used in the experiments. Various feature selection and statistical significance analysis methods are used to determine the relevance of various features. Furthermore, popular classifiers such as kernel based support vector machine (SVM), Naïve Bayesian, linear discriminant, quadratic discriminant, logistic regression, K-nearest neighbor (K-NN) and random forest were implemented and evaluated for breast cancer risk prediction using these features. Results of feature selection techniques indicate that among the nine features considered in this study, glucose, age and resistin are found to be most relevant and effective biomarkers for breast cancer prediction. Further, when these three features are used for classification, the medium K-NN classifier achieves the highest classification accuracy of 92.105% followed by medium Gaussian SVM which achieves classification accuracy of 83.684% under hold out data division protocol.
Keywords: Breast cancer biomarkers | Machine learning | Expert systems | Clinical features | Feature selection
Systemic sclerosis: clinical features and management
اسکلروز سیستمیک: ویژگی های بالینی و مدیریت-2018
Systemic sclerosis (SSc) differs from other multisystem connective tis sue/autoimmune diseases in that its clinical features result mainly from a combination of fibrosis and vascular abnormality (rather than from inflammation). This has major implications for management. SSc is associated with high morbidity and mortality, and is often very painful and disabling. There are two major subtypes, defined on the basis of the extent of skin involvement: limited (often previously referred to as CREST) and diffuse cutaneous. The two subtypes have very different natural histories, autoantibody associations and prognoses, and require different approaches to management, at least in their early stages. The two most characteristic features of SSc are Ray naud’s phenomenon (which can be very severe) and skin thickening (ʻsclerodermaʼ). Although both cause troublesome, often disabling symptoms, it is the internal organ involvement of the disease that can be life-threatening. This article discusses recent advances in early diagnosis, clinical features and the approach to investigation and management. It is an exciting time for clinicians with an interest in SSc, because following on from the development of new treatments for several organ-based complications (e.g. pulmonary arterial hyper tension, digital ulceration), several promising ʻdisease-modifyingʼ ther apies (including antifibrotics) are currently being studied in clinical trials.
Keywords: MRCP; pulmonary fibrosis; pulmonary hypertension; Raynaud’s phenomenon; scleroderma; scleroderma renal crisis; sys temic sclerosis