A novel method to predict nonvisible symptoms using machine learning in cancer palliative care

2021 ◽  
Author(s):  
Kazuki Shimada ◽  
Satoru Tsuneto
Keyword(s):  
Machine Learning ◽  
Palliative Care ◽  
Informed Consent ◽  
Cancer Patients ◽  
Prediction Accuracy ◽  
Patients With Cancer ◽  
Machine Learning Model ◽  
Background Data ◽  
Novel Method ◽  
Clinical Survey

Abstract Patients with cancer at the end of life may find it difficult to express their symptoms if they can no longer communicate verbally because of deteriorating health. In this study, we assessed these symptoms using machine learning. We conducted a clinical survey of 213 cancer patients from August 2015 to August 2016. We divided the reported symptoms into two groups—visible and nonvisible symptoms. Our machine learning model used patient background data and visible symptoms to predict nonvisible symptoms: pain, dyspnea, fatigue, drowsiness, anxiety, delirium, inadequate informed consent, and spiritual issues. The highest and/or lowest values for prediction accuracy, sensitivity, and specificity, respectively, are as follows: 88.0%/55.5%, 84.9%/3·3%, and 96.7%/24.1%. This work will facilitate better assessment and management of symptoms in patients with cancer.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

419 STOP-Bang Score and History of Radiation Predicts Risk of Obstructive Sleep Apnea in Cancer Patients: A Machine Learning Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A166-A166
Author(s):  
Ankita Paul ◽  
Karen Wong ◽  
Anup Das ◽  
Diane Lim ◽  
Miranda Tan
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cancer Patients ◽  
Prediction Accuracy ◽  
Screening Tools ◽  
Cancer Type ◽  
Sleep Study ◽  
Obstructive Sleep ◽  
History Of

Abstract Introduction Cancer patients are at an increased risk of moderate-to-severe obstructive sleep apnea (OSA). The STOP-Bang score is a commonly used screening questionnaire to assess risk of OSA in the general population. We hypothesize that cancer-relevant features, like radiation therapy (RT), may be used to determine the risk of OSA in cancer patients. Machine learning (ML) with non-parametric regression is applied to increase the prediction accuracy of OSA risk. Methods Ten features namely STOP-Bang score, history of RT to the head/neck/thorax, cancer type, cancer stage, metastasis, hypertension, diabetes, asthma, COPD, and chronic kidney disease were extracted from a database of cancer patients with a sleep study. The ML technique, K-Nearest-Neighbor (KNN), with a range of k values (5 to 20), was chosen because, unlike Logistic Regression (LR), KNN is not presumptive of data distribution and mapping function, and supports non-linear relationships among features. A correlation heatmap was computed to identify features having high correlation with OSA. Principal Component Analysis (PCA) was performed on the correlated features and then KNN was applied on the components to predict the risk of OSA. Receiver Operating Characteristic (ROC) - Area Under Curve (AUC) and Precision-Recall curves were computed to compare and validate performance for different test sets and majority class scenarios. Results In our cohort of 174 cancer patients, the accuracy in determining OSA among cancer patients using STOP-Bang score was 82.3% (LR) and 90.69% (KNN) but reduced to 89.9% in KNN using all 10 features mentioned above. PCA + KNN application using STOP-Bang score and RT as features, increased prediction accuracy to 94.1%. We validated our ML approach using a separate cohort of 20 cancer patients; the accuracies in OSA prediction were 85.57% (LR), 91.1% (KNN), and 92.8% (PCA + KNN). Conclusion STOP-Bang score and history of RT can be useful to predict risk of OSA in cancer patients with the PCA + KNN approach. This ML technique can refine screening tools to improve prediction accuracy of OSA in cancer patients. Larger studies investigating additional features using ML may improve OSA screening accuracy in various populations Support (if any):

scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

scholarly journals Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4368 ◽  
Author(s):  
Chun-Wei Chen ◽  
Chun-Chang Li ◽  
Chen-Yu Lin
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Important Method ◽  
Gap Statistic ◽  
Machine Learning Model ◽  
Key Variables

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

scholarly journals ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages

2020 ◽  
Author(s):  
Ting Jin ◽  
Nam D Nguyen ◽  
Flaminia Talos ◽  
Daifeng Wang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Human Disease ◽  
Gene Networks ◽  
Regulatory Mechanisms ◽  
Disease Development ◽  
Lung Cancer Patients ◽  
Machine Learning Model

Abstract Motivation Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a ‘black box’, barely providing biological and clinical interpretability from the box. Results To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value &lt; 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine. Availabilityand implementation ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Using a supportive care framework to understand and improve palliative care among cancer patients in Africa

2015 ◽  
Vol 14 (3) ◽  
pp. 284-301 ◽  
Author(s):  
David S. Busolo ◽  
Roberta L. Woodgate
Keyword(s):  
Palliative Care ◽  
Supportive Care ◽  
Cancer Patients ◽  
Care Needs ◽  
African Countries ◽  
Research Education ◽  
Patients With Cancer ◽  
Care Services ◽  
Incidence And Mortality ◽  
Palliative Care Services

ABSTRACTObjective:Cancer incidence and mortality are increasing in Africa, which is leading to greater demands for palliative care. There has been little progress in terms of research, pain management, and policies related to palliative care. Palliative care in Africa is scarce and scattered, with most African nations lacking the basic services. To address these needs, a guiding framework that identifies care needs and directs palliative care services could be utilized. Therefore, using the supportive care framework developed by Fitch (Fitch, 2009), we here review the literature on palliative care for patients diagnosed with cancer in Africa and make recommendations for improvement.Method:The PubMed, Scopus, CINAHL, Web of Science, Embase, PsycINFO, Social Sciences Citation Index, and Medline databases were searched. Some 25 English articles on research from African countries published between 2004 and 2014 were selected and reviewed. The reviewed literature was analyzed and presented using the domains of the supportive care framework.Results:Palliative care patients with cancer in Africa, their families, and caregivers experience increasing psychological, physical, social, spiritual, emotional, informational, and practical needs. Care needs are often inadequately addressed because of a lack of awareness as well as deficient and scattered palliative care services and resources. In addition, there is sparse research, education, and policies that address the dire situation in palliative care.Significance of Results:Our review findings add to the existing body of knowledge demonstrating that palliative care patients with cancer in Africa experience disturbing care needs in all domains of the supportive care framework. To better assess and address these needs, holistic palliative care that is multidomain and multi-professional could be utilized. This approach needs to be individualized and to offer better access to services and information. In addition, research, education, and policies around palliative care for cancer patients in Africa could be more comprehensive if they were based on the domains of the supportive care framework.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

The glimmering embers: Experiences of hope among cancer patients in palliative home care

2011 ◽  
Vol 9 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Louise Olsson ◽  
Gunnel Östlund ◽  
Peter Strang ◽  
Eva Jeppsson Grassman ◽  
Maria Friedrichsen
Keyword(s):  
Palliative Care ◽  
Home Care ◽  
Cancer Patients ◽  
Comparative Method ◽  
Healthcare Providers ◽  
Palliative Home Care ◽  
Patients With Cancer ◽  
Core Category ◽  
Sense Of Time ◽  
The Creation

AbstractObjective:The experience of hope among cancer patients in palliative care is important information for healthcare providers, but research on the subject is sparse. The aim of this article was to explore how cancer patients admitted to palliative home care experienced the significance of hope and used hope during 6 weeks throughout the last phase of their life, and to assess their symptoms and hope status during 6 weeks throughout the last phase of their lives.Method:Eleven adult patients with cancer participated in 20 interviews and completed seven diaries. The participants were recruited from two palliative care units in the southeast of Sweden. The method used was Grounded Theory (GT), and analysis was based on the constant comparative method.Results:The core category, glimmering embers, was generated from four processes: (1) The creation of “convinced” hope, with a focus on positive events, formed in order to have something to look forward to; (2) The creation of “simulated hope,” including awareness of the lack of realism, but including attempts to believe in unrealistic reasons for hope; (3) The collection of and maintaining of moments of hope, expressing a wish to “seize the day” and hold on to moments of joy and pleasure; and (4) “Gradually extinct” hope, characterized by a lack of energy and a sense of time running out.Significance of results:The different processes of hope helped the patients to continue to live when they were close to death. Hope should be respected and understood by the professionals giving them support.

scholarly journals Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy

2019 ◽  
Vol 60 (6) ◽  
pp. 818-824 ◽  
Author(s):  
Takuya Mizutani ◽  
Taiki Magome ◽  
Hiroshi Igaki ◽  
Akihiro Haga ◽  
Kanabu Nawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Malignant Glioma ◽  
Survival Time ◽  
Treatment Duration ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Learning Model ◽  
Machine Learning Model ◽  
Prescription Dose ◽  
Leave One Out

ABSTRACT The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose–volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike’s information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P &lt; 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model.

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