Satoru Kodama 1 , Kazuya Fujihara 2 , Haruka Shiozaki 2 , Chika Horikawa 3 , Mayuko Harada Yamada 2 , Takaaki Sato 2 , Yuta Yaguchi 2 , Masahiko Yamamoto 2 , Masaru Kitazawa 2 , Midori Iwanaga 2 , Yasuhiro Matsubayashi 2 , Hirohito Sone 2 Show Affiliations »
Abstract
BACKGROUND: Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. OBJECTIVE: The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). METHODS: Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. RESULTS: A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. CONCLUSIONS: Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users' Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients' risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682. ©Satoru Kodama, Kazuya Fujihara, Haruka Shiozaki, Chika Horikawa, Mayuko Harada Yamada, Takaaki Sato, Yuta Yaguchi, Masahiko Yamamoto, Masaru Kitazawa, Midori Iwanaga, Yasuhiro Matsubayashi, Hirohito Sone. Originally published in JMIR Diabetes (http://diabetes.jmir.org), 29.01.2021.
BACKGROUND: Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia . OBJECTIVE: The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). METHODS: Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. RESULTS: A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia ) were eligible. For detection of hypoglycemia , pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia , pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. CONCLUSIONS: Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users' Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients ' risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia , neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682. ©Satoru Kodama, Kazuya Fujihara, Haruka Shiozaki, Chika Horikawa, Mayuko Harada Yamada, Takaaki Sato, Yuta Yaguchi, Masahiko Yamamoto, Masaru Kitazawa, Midori Iwanaga, Yasuhiro Matsubayashi, Hirohito Sone. Originally published in JMIR Diabetes (http://diabetes .jmir.org), 29.01.2021.
Entities: CellLine
Chemical
Disease
Gene
Species
Keywords:
hypoglycemia; machine learning; meta-analysis
Year: 2021
PMID： 33512324 PMCID： PMC7880810 DOI： 10.2196/22458
Source DB: PubMed Journal: JMIR Diabetes ISSN： 2371-4379