Improving healthcare operations management with machine learning

Pianykh, Oleg S.; Guitron, Steven; Parke, Darren; Zhang, Chengzhao; Pandharipande, Pari V.; Brink, James A.; Rosenthal, Daniel

doi:10.1038/s42256-020-0176-3

Cited by 50 publications

(31 citation statements)

References 36 publications

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“…We find that the simpler interpretable models, coupled with optimized feature selection, perform just as well as the complex non-interpretable models. This contributes to the discussion on using interpretable ML models for high-stake decision-making [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil

et al. 2020

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Background Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems.

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Section: Introductionmentioning

confidence: 99%

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil

et al. 2020

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“…Since the categorical target variable is binary in nature, it was labeled as 0 for no liver disease and 1 for liver disease. Thus, SVM [34] works by following two steps:…”

Section: Support Vector Machine Classificationmentioning

confidence: 99%

“…Finally, FP indicates the number of negatives that the model classified as positives and FN represents the number of positives that the machine classified as negatives. To visualize the performance of the model, the receiver operating characteristic (ROC) curve [34] was used. It plots the sensitivity against the 1-specificity for different cuts of points.…”

Section: Evaluations Of the Statistical Learning Modelsmentioning

confidence: 99%

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Statistical Machine Learning Approaches to Liver Disease Prediction

Mostafa

Hasan

Williamson

et al. 2021

Livers

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Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p<0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.

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“…From this analysis, valuable insights can be extracted to guide medical professionals on operational decisions and patient care. At the operational level, machine learning can be used to assist facilities with scheduling, forecasting expenses [63], information retrieval [27], and reducing patient wait times [57]. Machine learning for patient care can assist medical providers by detecting and diagnosing illnesses [28], suggesting personalized treatment plans [70], monitoring physiological signals [34], and forecasting epidemic trends [66].…”

Section: Introductionmentioning

confidence: 99%

Medical Provider Embeddings for Healthcare Fraud Detection

Johnson

Khoshgoftaar

2021

SN COMPUT. SCI.

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Advances in data mining and machine learning continue to transform the healthcare industry and provide value to medical professionals and patients. In this study, we address the problem of encoding medical provider types and present four techniques for learning dense, semantic embeddings that capture provider specialty similarities. The first two methods (GloVe and Med-W2V) use pre-trained word embeddings to convert provider specialty descriptions to phrase embeddings. Next, HcpsVec and RxVec embeddings are constructed from publicly available big data using specialty-procedure and specialtydrug occurrence matrices, respectively. We evaluate the learned provider type embeddings on two real-world medicare fraud classification problems using logistic regression (LR), random forest (RF), gradient boosted tree (GBT), and multilayer perceptron (MLP) learners. Through repetition, statistical analysis, and feature importance measures, we confirm that semantic embeddings for provider types significantly improve fraud classification results. Finally, t-SNE visualizations are used to show that the learned provider type embeddings capture meaningful specialty characteristics and provider type similarities. Our primary contributions are two novel methods for encoding medical specialties using procedure-level statistics and the evaluation of four encoding techniques on two large-scale healthcare fraud classification tasks. Since all data sources are publicly available, these encoding techniques can be readily adopted and applied in future machine learning applications in the healthcare industry.

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Improving healthcare operations management with machine learning

Cited by 50 publications

References 36 publications

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil

Statistical Machine Learning Approaches to Liver Disease Prediction

Medical Provider Embeddings for Healthcare Fraud Detection

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