Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis (Preprint) Article Swipe
Satoru Kodama
,
Kazuya Fujihara
,
Haruka Shiozaki
,
Chika Horikawa
,
MAYUKO H. YAMADA
,
Takaaki Sato
,
Yuta Yaguchi
,
Masahiko Yamamoto
,
Masaru Kitazawa
,
Midori Iwanaga
,
Yasuhiro Matsubayashi
,
Hirohito Sone
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.22458
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.22458
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. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.22458
- OA Status
- gold
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4249074052
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4249074052Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/preprints.22458Digital Object Identifier
- Title
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Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis (Preprint)Work title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-07-13Full publication date if available
- Authors
-
Satoru Kodama, Kazuya Fujihara, Haruka Shiozaki, Chika Horikawa, MAYUKO H. YAMADA, Takaaki Sato, Yuta Yaguchi, Masahiko Yamamoto, Masaru Kitazawa, Midori Iwanaga, Yasuhiro Matsubayashi, Hirohito SoneList of authors in order
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https://doi.org/10.2196/preprints.22458Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2196/preprints.22458Direct OA link when available
- Concepts
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Hypoglycemia, False positive paradox, Algorithm, Medicine, Meta-analysis, Machine learning, Diabetes mellitus, Artificial intelligence, Receiver operating characteristic, Internal medicine, Computer science, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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