Table 1 - uploaded by Damian Mingle
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Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel feature detection and engineering machine-learning framework is presented to address this need. First, the Rip Curl process is...
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... an effort to increase performance and accuracy we opted for an approach of feature selection to help reduce the number of descriptive features in the omentum dataset to just the subset that is most useful for prediction. Before we begin our discussion of approaches to feature selection, it is useful to distinguish between different types of descriptive features (Table 1): ...
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Citations
... The objective of our research is to make a machine learning model, which will predict the hospital readmission of a patient, and to identify the most significant features [14] [5], which contribute most to the readmission of a patient. To execute this research we require knowledge about healthcare domain related to readmission [6], diabetes and all the tests associated with it. Which will help us to understand the attributes and to find the most significant features [13] then we can reduce the hospital readmission up to a major extent [7]. ...
Hospital Readmission is considered as an effective measurement of service and care provided within the hospital. Emergency readmission to hospital is frequently used as a measure of the quality of a hospital because a high proportion of readmissions should be preventable if the preceding care is adequate. The objective of this study to develop a model to predict 30-day hospital readmission. We have data of 1-lac diabetes patients with 50 features. We used machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Adaboost and XGBoost for prediction. We achieved the highest accuracy 94% using Random forest among all other algorithms. The results from this study are encouraging and can help healthcare providers to improve their services.
... We ranked the top 5 features for this model by informativeness how informative they are relative to the other features of that age group. (Mingle, 2017). The "Number of emergency" feature and the feature engineered by dividing the number of emergency visits by the sum of lab procedures plus medications were both unique among all three models: ...
Hospital readmission is considered an effective measurement of care provided within healthcare. Being able to risk identify patients facing a high likelihood of unplanned hospital readmission in the next 30-days could allow for further investigation and possibly prevent the readmission. Current models, such as LACE, sacrifice accuracy in order to allow for end-users to have a straight forward and simple experience. This study acknowledges that while HbA1c is important, it may not be critical in predicting readmissions. It also investigates the hypothesis that using machine learning on a wide feature, making use of model diversity, and blending prediction will improve the accuracy of readmission risk predictions compared with existing techniques. A dataset originally containing 100,000 admissions and 56 features was used to evaluate the hypothesis. The results from the study are encouraging and can help healthcare providers improve inpatient diabetic care.
