Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability

DeVries, Zachary; Locke, Eric; Hoda, Mohamad; Moravek, Dita; Phan, Kim; Stratton, Alexandra; Kingwell, Stephen; Wai, Eugene K.; Phan, Philippe

doi:10.1016/j.spinee.2021.02.007

Cited by 47 publications

(21 citation statements)

References 25 publications

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“…Because at this stage the data used is still imbalanced, to determine which ratio to use as a baseline, we can see from the resulting F1-Score. F1-score can be used on imbalanced data with high true negative class for the assessment of prediction algorithm [19].…”

Section: Resultsmentioning

confidence: 99%

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Rahmanda

Setiawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Customer opinion is an important aspect in determining the success of a company or service provider. By determining the sentiment of the existing opinion, the company can use it as an evaluation material to improve the quality of the service or product provided. Sentiment analysis can be used as a measure of opinion sentiment with input data in the form of a corpus which will be classified into positive or negative classes to obtain the level of customer satisfaction with a product or service. Aspect-based sentiment analysis can be used by companies to analyze more specifically and find out what aspects need to be improved. In this research, an aspect-based sentiment analysis was conducted on Telkomsel users on Twitter. The data used is 16,992 tweets from users who discuss several aspects such as Telkomsel's services and signals in Twitter. In this research Word2Vec was used for feature expansion to minimize vocabulary mismatch caused by limited words in tweets. The results showed that Word2Vec, Synthetic Minority Oversampling Technique (SMOTE), and Boosting algorithm combination with Logistic Regression classifier achieve highest accuracy of 95.10% for signal aspect and using hyperparameters makes the service aspect get the highest accuracy of 93.34%.

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Section: Resultsmentioning

confidence: 99%

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Rahmanda

Setiawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…The AUC [ 39 ] evaluates the merit of the obtained recommendation results from the perspective of sample probability, which is simply introduced as the probability of acquiring a positive sample, that is, greater than the negative sample in a pair of randomly selected samples (one positive sample and one negative sample); the model obtained from training is used to predict these two samples, as shown in Equation ( 18 ).

where M denotes the number of positive samples, N denotes the number of negative samples, and M × N represents the total number of sample pairs, P positive refers to the probability of getting a positive sample for the prediction, P negative refers to the probability of getting a negative sample for the prediction.…”

Section: Experiments and Resultsmentioning

confidence: 99%

TAFM: A Recommendation Algorithm Based on Text-Attention Factorization Mechanism

Zhang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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The click-through rate (CTR) prediction task is used to estimate the probabilities of users clicking on recommended items, which are extremely important in recommender systems. Recently, the deep factorization machine (DeepFM) algorithm was proposed. The DeepFM algorithm incorporates a factorization machine (FM) to learn not only low-order features but also the interactions of higher-order features. However, DeepFM lacks user diversity representations and does not consider the text. In view of this, we propose a text-attention FM (TAFM) based on the DeepFM algorithm. First, the attention mechanism in the TAFM algorithm is used to address the diverse representations of users and goods and to mine the features that are most interesting to users. Second, the TAFM model can fully learn text features through its text component, text attention component, and N-gram text feature extraction component, which can fully explore potential user preferences and the diversity among user interests. In addition, the convolutional autoencoder in the TAFM can learn some higher-level features, and the higher-order feature mining process is more comprehensive. On the public dataset, the better performing models in the existing models are deep cross network (DCN), DeepFM, and product-based neural network (PNN), respectively, and the AUC score metrics of these models hover between 0.698 and 0.699. The AUC score of our design model is 0.730, which is at least 3% higher than that of the existing models. The accuracy metric of our model is at least 0.1 percentage points higher than that of existing models.

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“…Secondly, the actual number of operons compared to single genes most likely results in an unbalanced dataset. In these two scenarios, the F1 score has been proven to be a better metric than the accuracy score to evaluate algorithm performance [49]. Similarly, when datasets are unbalanced, Condition-specific mapping of operons using dynamic and static genome data precision and recall were demonstrated as better evaluators for a model's classification performance; and precision-recall curves were more useful and robust than the ROC curves.…”

Section: Comparison To Existing Algorithmsmentioning

confidence: 99%

Condition-Specific Mapping of Operons (COSMO) using dynamic and static genome data

Calvert-Joshua

Bendou

Heusden

et al. 2022

Preprint

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An operon is a set of adjacent genes which are transcribed into a single messenger RNA. Operons allow prokaryotes to efficiently circumvent environmental stresses. It is estimated that about 60% of the Mycobacterium tuberculosis genome is arranged into operons, which makes them interesting drug targets in the face of emerging drug resistance. We therefore developed COSMO - a tool for operon prediction in M. tuberculosis using RNA-seq data. We analyzed four algorithmic parameters and benchmarked COSMO against two top performing operon predictors. COSMO outperformed both predictors in its accuracy and in its ability to distinguish operons activated under distinct conditions.

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Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability

Cited by 47 publications

References 25 publications

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

TAFM: A Recommendation Algorithm Based on Text-Attention Factorization Mechanism

Condition-Specific Mapping of Operons (COSMO) using dynamic and static genome data

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