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. 2019 Jun 1;179(6):836-838.
doi: 10.1001/jamainternmed.2018.8558.

Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning

Alvin Rajkomar  1 , Anjuli Kannan  1 , Kai Chen  1 , Laura Vardoulakis  1 , Katherine Chou  1 , Claire Cui  1 , Jeffrey Dean  1
Affiliations

Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning

Alvin Rajkomar et al. JAMA Intern Med. .

Abstract

This study assesses the feasibility of using machine learning to automatically populate a review of systems of all symptoms discussed in an encounter between a patient and a clinician.

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Conflict of interest statement

Conflict of Interest Disclosures: All authors are employed by and own stock in Google. In addition, as part of a broad-based equity portfolio intending to mirror the US and International equities markets (eg, MSCI All Country World, Russell 3000), Jeff Dean holds individual stock positions in many public companies in the health care and pharmacological sectors, and also has investments in managed funds that also invest in such companies, as well as limited partner and direct venture investments in private companies operating in these sectors. All other health care–related investments are managed by independent third parties (institutional managers) with whom Jeff Dean has no direct contact and over whom Jeff Dean has no control. The authors have a patent pending for the machine learning tool described in this study. No other conflicts are reported.

Figures

Figure.
Figure.. Study Design
Description of how data were used to construct the model, how subsets were labeled, and where metrics were calculated.
See this image and copyright information in PMC

References

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