Evidence map›Paper›PMID 33316422›Full record

ArticleJournal of biomedical informatics2021

Enabling personalized decision support with patient-generated data and attributable components.

Elliot G Mitchell, Esteban G Tabak, Matthew E Levine, Lena Mamykina, David J Albers

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Elliot G MitchellDepartment of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: egm2143@cumc.columbia.edu.
Esteban G TabakCourant Institute of Mathematical Sciences, New York, NY, USA. Electronic address: tabak@cims.nyu.edu.
Matthew E LevineCalifornia Institute of Technology, Pasadena, CA, USA. Electronic address: mlevine@caltech.edu.
Lena MamykinaDepartment of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: om2196@cumc.columbia.edu.
David J AlbersDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Pediatrics, Division of Informatics, University of Colorado, Aurora, CO, USA. Electronic address: david.albers@ucdenver.edu.

Funding

Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial.R01DK113189 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MAMYKINA, OLENA · 2019 to 2023
$3.3M
Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled TrialR56DK113189 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MAMYKINA, OLENA · 2017 to 2017
$176k
NIDDK NIH HHS R01 DK113189NIDDK NIH HHS R56 DK113189
6 · The paper itself

Abstract

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems.

Indexed as

Diabetes Mellitus, Type 2HumansMachine LearningMachine learningPatient decision supportPatient-generated health dataType 2 diabetes

Identifiers

PMID33316422
PMCPMC7968723

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.