Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients

Ferroni, Patrizia; Zanzotto, Fabio Massimo; Scarpato, Noemi; Riondino, Silvia; Nanni, Umberto; Roselli, Mario

doi:10.1177/0272989x16662654

Cited by 70 publications

(64 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The machine learning algorithms used in our study were Random Forests, Artificial Neural Network and Support Vector Machine, because they are among the algorithms that are currently most widely and successfully used for clinical data (2–7). Each one of them represents a different algorithm “family,” each with radically different internal algorithm structures (16).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

et al. 2018

View full text Add to dashboard Cite

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed.Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53–0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77–0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88–0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00–0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Machine learning belongs to the domain of artificial intelligence and provides a promising tool in pursuing personalized outcome prediction, which is increasingly used in medicine (2–7). The machine learning methodology allows discovering empirical patterns in data through automated algorithms.…”

Section: Introductionmentioning

confidence: 99%

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Chatbots 1 [39,37] have conquered our new window-on-the-world -our smartphonesand, from there, they help with everyday tasks such as managing our agenda, answering our factoid questions or being our learning companions [16,4]. In medicine, computers can already help in formulating diagnoses [2,18,10] by looking at data doctors generally neglect. Artificial Intelligence is preparing a wonderful future where people are released from the burden of repetitive jobs.…”

Section: Introductionmentioning

confidence: 99%

Viewpoint: Human-in-the-loop Artificial Intelligence

Zanzotto¹

2019

jair

Self Cite

223

View full text Add to dashboard Cite

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves.In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.

show abstract

“…Authors in [15] used Gated Recurrent Unit [16] combined with Support Vector Machine [17] algorithms for diagnosing breast cancer on WDBC dataset. Some of the research works have used multiple kernel learning [18] method for various healthcare problems [19][20] and provides the way to examine the learned model. One of the authors combined SVM [21] and Random optimization [22] methods for demographic, clinical and biochemical data prediction.…”

Section: Related Workmentioning

confidence: 99%

“…From the table-4, it is concluded that proposed CNN outperforms than the other approaches and it is decided that it is highly suitable for medical image processing and analyzation. To evaluate the performance of the proposed CNN, the obtained accuracy is compared with the existing research works, have carried out the similar research works given in author in [17] obtained 60% on DDSM dataset, author in [18] obtained 88% on BCDR-F03 dataset, Duraisamy&Emperumal (2017) obtained 85% on MIAS and BCDR dataset and author in [19] obtained 60% on CBIS-DDSM dataset. From the overall existing system, author in [19] obtained the highest accuracy.…”

Section: Datasetmentioning

confidence: 99%

Deep Convolution Neural Network Based Breast Cancer Bigdata Analysis for Crowd Cloud Sourcing

2020

IJITEE

View full text Add to dashboard Cite

Breast cancer is one of the dangerous diseases leads fast death among women. Several kinds of cancers are affecting people, but breast cancer affects highly women. In medical industry removal of women breasts or major surgery is taken forward as the solution, where it reoccurs after surgery also. Only solution to save women from breast cancer is to identify and detect the earlier stage of cancer and provide necessary treatment. Hence various research works have been focused on finding good solution for diagnosing and classifying the cancer stages as benign, malignant or severe malignant. Still the accuracy of classification needs to be improved on complex breast cancer datasets. Few of the earlier research works have proposed machine learning algorithms, which are semiautomatic and accuracy is also not high. Thus, to provide a better solution this paper aimed to use one of the deep learning algorithms such as Convolution Neural Networks for diagnosing various kinds of breast cancer dataset. From the experimental results, it is obtained that the proposed deep learning algorithms outperforms than the other algorithms.

show abstract

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients

Cited by 70 publications

References 34 publications

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

Viewpoint: Human-in-the-loop Artificial Intelligence

Deep Convolution Neural Network Based Breast Cancer Bigdata Analysis for Crowd Cloud Sourcing

Contact Info

Product

Resources

About