scholarly journals Comparison of machine-learning algorithms for the prediction of current procedural terminology (CPT) codes from pathology reports

2022 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Joshua Levy ◽  
Nishitha Vattikonda ◽  
Christian Haudenschild ◽  
Brock Christensen ◽  
Louis Vaickus
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Current Procedural Terminology ◽  
Pathology Reports

scholarly journals Comparison of Machine Learning Algorithms for the Prediction of Current Procedural Terminology (CPT) Codes from Pathology Reports

2021 ◽  
Author(s):  
Joshua Levy ◽  
Nishitha Vattikonda ◽  
Christian Haudenschild ◽  
Brock Christensen ◽  
Louis Vaickus
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Pathology Report ◽  
Free Text ◽  
Medical Procedure ◽  
Current Procedural Terminology ◽  
Learning Methods ◽  
Clinical Narrative ◽  
Pathology Reports

AbstractBackgroundPathology reports serve as an auditable trail of a patient’s clinical narrative containing important free text pertaining to diagnosis, prognosis and specimen processing. Recent works have utilized sophisticated natural language processing (NLP) pipelines which include rule-based or machine learning analytics to uncover patterns from text to inform clinical endpoints and biomarker information. While deep learning methods have come to the forefront of NLP, there have been limited comparisons with the performance of other machine learning methods in extracting key insights for prediction of medical procedure information (Current Procedural Terminology; CPT codes), that informs insurance claims, medical research, and healthcare policy and utilization. Additionally, the utility of combining and ranking information from multiple report subfields as compared to exclusively using the diagnostic field for the prediction of CPT codes and signing pathologist remains unclear.MethodsAfter passing pathology reports through a preprocessing pipeline, we utilized advanced topic modeling techniques such as UMAP and LDA to identify topics with diagnostic relevance in order to characterize a cohort of 93,039 pathology reports at the Dartmouth-Hitchcock Department of Pathology and Laboratory Medicine (DPLM). We separately compared XGBoost, SVM, and BERT methodologies for prediction of 38 different CPT codes using 5-fold cross validation, using both the diagnostic text only as well as text from all subfields. We performed similar analyses for characterizing text from a group of the twenty pathologists with the most pathology report sign-outs. Finally, we interpreted report and cohort level important words using TF-IDF, Shapley Additive Explanations (SHAP), attention, and integrated gradients.ResultsWe identified 10 topics for both the diagnostic-only and all-fields text, which pertained to diagnostic and procedural information respectively. The topics were associated with select CPT codes, pathologists and report clusters. Operating on the diagnostic text alone, XGBoost performed similarly to BERT for prediction of CPT codes. When utilizing all report subfields, XGBoost outperformed BERT for prediction of CPT codes, though XGBoost and BERT performed similarly for prediction of signing pathologist. Both XGBoost and BERT outperformed SVM. Utilizing additional subfields of the pathology report increased prediction accuracy for the CPT code and pathologist classification tasks. Misclassification of pathologist was largely subspecialty related. We identified text that is CPT and pathologist specific.ConclusionsOur approach generated CPT code predictions with an accuracy higher than that reported in previous literature. While diagnostic text is an important information source for NLP pipelines in pathology, additional insights may be extracted from other report subfields. Although deep learning approaches did not outperform XGBoost approaches, they may lend valuable information to pipelines that combine image, text and -omics information. Future resource-saving opportunities exist for utilizing pathology reports to help hospitals detect mis-billing and estimate productivity metrics that pertain to pathologist compensation (RVU’s).

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset
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