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Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach.
Metadata
Journalartificial intelligence in medicine4.383Date
2020 May 06
5 months ago
Type
Research Support, Non-U.S. Gov't
Journal Article
Volume
2020-05 / 105 : 101847
Author
Bernardini M 1, Morettini M 2, Romeo L 3, Frontoni E 4, Burattini L 5
Affiliation
  • 2. Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona, Italy. Electronic address: [email protected]
  • 3. Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona, Italy; Cognition, Motion and Neuroscience and Computational Statistics and Machine Learning, Istituto Italiano di Tecnologia, Genova, Italy. Electronic address: [email protected]
  • 4. Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona, Italy. Electronic address: [email protected]
  • 5. Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona, Italy. Electronic address: [email protected]
Doi
PMIDMESH
Abstract
Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients' information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.
Keywords: Clinical Decision Support System Electronic Health Record Machine Learning Predictive Medicine Temporal Analysis Type 2 Diabetes
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Artif Intell Medartificial intelligence in medicine
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LocationNetherlands
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