Patient‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach

Abstract Background Dietary guidelines recommend limiting the intake of added sugars. However, despite the public health importance, most countries have not mandated the labeling of added sugar content on packaged foods and beverages, making it difficult for consumers to avoid products with added sugar, and limiting the ability of policymakers to identify priority products for intervention. Objective To develop a machine learning approach for the prediction of added sugar content in packaged products using available nutrient, ingredient, and food category information. Design The added sugar prediction algorithm was developed using k-Nearest Neighbors (KNN) and packaged food information from the US Label Insight dataset (n = 70,522). A synthetic dataset of Australian packaged products (n = 500) was used to assess validity and generalization. Performance metrics included the coefficient of determination (R2), mean absolute error (MAE), and Spearman rank correlation (ρ). To benchmark the KNN approach, the KNN approach was compared to an existing added sugar prediction approach that relies on a series of manual steps. Results Compared to the existing added sugar prediction approach, the KNN approach was similarly apt at explaining variation in added sugar content (R2 = 0.96 vs. 0.97 respectively) and ranking products from highest to lowest in added sugar content (ρ = 0.91 vs. 0.93 respectively), while less apt at minimizing absolute deviations between predicted and true values (MAE = 1.68 g vs. 1.26 g per 100 g or 100 mL respectively). Conclusions KNN can be used to predict added sugar content in packaged products with a high degree of validity. Being automated, KNN can easily be applied to large datasets. Such predicted added sugar levels can be used to monitor the food supply and inform interventions aimed at reducing added sugar intake.

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A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision-making

[1988] Proceedings. The Fourth Conference on Artificial Intelligence Applications ◽

10.1109/caia.1988.196121 ◽

2003 ◽

Cited By ~ 5

Author(s):

K. Chen ◽

S.C. Lu

Keyword(s):

Machine Learning ◽

Decision Making ◽

Learning Approach ◽

Automatic Synthesis ◽

Machine Learning Approach

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Multi-criteria decision-making for purchasing cell phones using machine learning approach

International Journal of Decision Sciences Risk and Management ◽

10.1504/ijdsrm.2017.090348 ◽

2017 ◽

Vol 7 (3) ◽

pp. 190

Author(s):

Kriti Shree ◽

Sarita Mohanty ◽

Sachi Nandan Mohanty

Keyword(s):

Machine Learning ◽

Decision Making ◽

Cell Phones ◽

Learning Approach ◽

Multi Criteria Decision Making ◽

Machine Learning Approach

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Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study (Preprint)

10.2196/preprints.22148 ◽

2020 ◽

Author(s):

Kazuya Fujihara ◽

Yasuhiro Matsubayashi ◽

Mayuko Harada Yamada ◽

Masahiko Yamamoto ◽

Toshihiro Iizuka ◽

...

Keyword(s):

Machine Learning ◽

Decision Making ◽

Logistic Regression ◽

Gold Standard ◽

Learning Approach ◽

Insulin Initiation ◽

Machine Learning Model ◽

Machine Learning Approach ◽

General Physicians

BACKGROUND Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. OBJECTIVE The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. METHODS Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. RESULTS The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). CONCLUSIONS Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists’ choice of the gold standard. Further study is needed before the use of machine learning–based decision support systems for insulin initiation can be incorporated into clinical practice.

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