As of June 3, 2020, according to WHO there have been more than 6.3 million confirmed cases worldwide and more than 379,941 deaths attributable to COVID-19 (https://covid19.who.int/). The clinical course and prognosis of patients with COVID-19 varies substantially, even among patients with similar age and comorbidities. Following exposure and initial infection with SARS-CoV-2, likely through the upper respiratory tract, patients can remain asymptomatic with active viral replication for days before symptoms manifest (Guan et al., 2020; Gandhi et al., 2020; Verity et al., 2020). The asymptomatic nature of initial SARS-CoV-2 infection patients may be exacerbating the rampant community transmission observed (Hoehl et al., 2020). It remains unknown why certain patients become symptomatic, and in those that do, the timeline of symptoms remains poorly characterized and non-specific. Symptoms may include fever, fatigue, myalgias, loss of appetite, loss of smell (anosmia), and altered sense of taste, in addition to the respiratory symptoms of dry cough, dyspnea, sore throat, and rhinorrhea, as well as gastrointestinal symptoms of diarrhea, nausea, and abdominal discomfort (Xiao et al., 2020). A small proportion of COVID-19 patients progress to severe illness, requiring hospitalization or intensive care management; among these individuals, mortality due to Acute Respiratory Distress Syndrome (ARDS) is higher (Zhang et al., 2020). The estimated average time from symptom onset to resolution can range from three days to more than three weeks, with a high degree of variability (Bi et al., 2020). The COVID-19 public health crisis demands a data science-driven approach to quantify the temporal dynamics of COVID-19 pathophysiology. However, for this there is a need to overcome challenges associated with manual curation of unstructured EHRs in a clinical context (Argenziano et al., 2020) and self-reporting outside of the clinical settings via questionnaires (Menni et al., 2020).

Here we introduce a platform for the augmented curation of the full-spectrum of patient symptoms from the Mayo Clinic EHRs for 77,167 patients with positive/negative COVID-19 diagnosis by PCR testing (see Materials and methods). The platform utilizes state-of-the-art transformer neural networks on the unstructured clinical notes to automate entity recognition (e.g. diseases, symptoms), quantify the strength of contextual associations between entities, and characterize the nature of association into ‘positive’, ‘negative’, ‘suspected’, or ‘other’ sentiments. We identify specific sensory, respiratory, and gastro-intestinal symptoms, as well as some specific combinations, that appear to be indicative of impending COVID_pos diagnosis by PCR testing. This highlights the potential for neural network-powered EHR curation to facilitate a significantly earlier diagnosis of COVID-19 than currently feasible.

The clinical determination of the COVID-19 status for each patient was conducted using the SARS-CoV-2 PCR (RNA) test approved for human nasopharyngeal and oropharyngeal swab specimens under the U.S. FDA emergency use authorization (EUA) (Mayo Clinic Laboratories, 2019). This PCR test resulted in 74,850 COVID_neg patient diagnoses and 2,317 COVID_pos patient diagnoses. The COVID_pos cohort had a mean age of 41.9 years (standard deviation = 19.1 years) and was 51% male and 49% female while the COVID_neg cohort had a mean age of 50.7 years (standard deviation = 21.4 years) and was 43% male and 57% female. Only 11 (0.5%) of COVID_pos and 196 (0.3%) of COVID_neg patients were hospitalized 7 days or more prior to PCR testing, indicating that the vast majority of patients were not experiencing serious illness prior to this time window. During the week prior to PCR testing, 135 (5.8%) of the COVID_pos and 5981 (8.0%) of the COVID_neg patients were hospitalized. Additionally, the frequencies of ICD10 diagnosis codes for these cohorts were found for the week prior to PCR testing, with unspecified acute upper respiratory infection appearing in over 20% of both cohorts (Supplementary file 1a-b). In order to investigate the time course of COVID-19 progression in patients and better define the presence or absence of symptoms, we used BERT-based deep neural networks to extract symptoms and their putative synonyms from the clinical notes for the week prior to the date when the COVID-19 diagnosis test was taken (see Materials and methods; Table 1). For the purpose of this analysis, all patients were temporally aligned, by setting the date of COVID-19 PCR testing to ‘day 0’, and the proportion of patients demonstrating each symptom derived from the EHR over each day of the week preceding testing was tabulated (Table 2). As a negative control, we included a non-COVID-19 symptom ‘dysuria’.

Altered or diminished sense of taste or smell (dysgeusia or anosmia) is the most significantly amplified signal in COVID_pos over COVID_neg patients in the week preceding PCR testing (Table 1; 27.1-fold amplification; p-value<<1E-100). This result suggests that anosmia and dysgeusia are likely the most salient early indicators of COVID-19 infection, including in otherwise asymptomatic patients. However, it must be noted that the prevalence of a symptom in the population must be taken into consideration. Thus, while anosmia/ dysgeusia see the most dramatic difference in prevalence between the COVID_pos and COVID_neg cohorts, the overall prevalence of 318 out of 77,167 patients, or 0.4%, precludes it from being a standalone predictor of infection. However, with the recent addition of anosmia and dysgeusia to the CDC guidelines (Mayo Clinic Laboratories, 2019) these symptoms will likely be reported more frequently as the pandemic progresses. Alternatively, fever/chills also have an increased signal in the COVID_pos compared to the COVID_neg cohort (2.6-fold amplification; p-value = 3.6E-169) while appearing in 10,171 out of 77,167 patients (13.2%).

Diarrhea is also significantly amplified in the COVID_pos patients for the week preceding PCR testing (Table 1; 1.4-fold; p-value = 3.5E-06). Some of these not yet-diagnosed COVID-19 patients that experience diarrhea prior to PCR testing may be unintentionally shedding SARS-CoV-2 fecally (Xu et al., 2020; Wu, 2020). Incidentally, epidemiological surveillance by waste water monitoring conducted recently in the state of Massachusetts observed SARS-CoV-2 RNA (Xu et al., 2020 Wu, 2020). The amplification of diarrhea in COVID_pos over COVID_neg patients for the week preceding PCR testing raises concern for other modes of viral transmission and highlights the importance of washing hands frequently in addition to wearing respiratory protection.

As may be expected, respiratory difficulty is enriched in the week prior to PCR testing in COVID_pos over COVID_neg patients (1.9-fold amplification; p-value = 1.1E-22; Table 1). Among other common phenotypes with significant enrichments in COVID_pos over COVID_neg patients, cough has a 2.2-fold amplification (p-value = 4.6E-129) and myalgia/arthralgia has a 2.0-fold amplification (p-value = 5.3E-34). Rhinitis is also a potential early signal of COVID_pos patients that requires some consideration (2.2-fold amplification, p-value = 2.25E-29). Finally, dysuria was included as a negative control for COVID-19, and consistent with this assumption, 0.69% of COVID_pos patients and 0.97% of COVID_neg patients had dysuria during the week preceding PCR testing.

Next, we considered the 325 possible pairwise combinations of 26 symptoms (Supplementary file 1c) for COVID_pos versus COVID_neg patients in the week prior to the PCR testing date (Supplementary file 1d). As expected from the previous results, altered sense of smell or taste (anosmia/dysgeusia) dominates in combination with many of the aforementioned symptoms as the most significant combinatorial signature of impending COVID_pos diagnosis (particularly along with cough, respiratory difficulty, and fever/chills). Examining the other 300 possible pairwise symptom combinations, excluding the altered sense of smell of taste, reveals other interesting combinatorial signals. The combination of cough and diarrhea is noted to be significant in COVID_pos over COVID_neg patients during the week preceding PCR testing; that is cough and diarrhea co-occur in 8.0% of COVID_pos patients and only 2.8% of COVID_neg patients, indicating a 2.8-fold amplification of this specific symptom combination (BH corrected p-value = 5.6E-32, Supplementary file 1d).

We further investigated the temporal evolution of the proportion of patients with each symptom for the week prior to PCR testing (Table 2). Altered sense of taste or smell, cough, diarrhea, fever/chills, and respiratory difficulty were found to be significant discriminators of COVID_pos from COVID_neg patients between 4 to 7 days prior to PCR testing. During that time period, cough is significantly amplified (>3 fold, p-value<0.05) in the COVID_pos patient cohort over the COVID_neg patient cohort by 5.2-fold on day −7 (p-value = 8.4E-40), 4.31-fold on day −6(p-value = 7.44E-28), 3.6-fold on day −5 (p-value = 2.4E-18), and 3.1-fold on day −4 (p-value = 2.7E-12). The diminishing odds of cough as a symptom from 7 to 4 days preceding the PCR testing date is notable and this temporal pattern could potentially suggest that the duration of cough and other symptoms, in addition to their presence or absence, is a useful indicator of infection. Similarly, diarrhea is amplified in the COVID_pos patient cohort over the COVID_neg patient cohort for days furthest preceding from the PCR testing date, with an amplification of 2.2-fold on day −7 (p-value = 5.6E-04) and 1.8-fold on day −6 (p-value = 1.2E-02). Likewise, fever/chills and respiratory difficulty both show matching trends, with significant amplification in the COVID_pos cohort on days −7 to −4 and days −7 to −5, respectively. However, unlike diarrhea, cough, fever/chills, and respiratory difficulty, we find that change in appetite may be considered a subsequent symptom of impending COVID-19 diagnosis, with significant amplification in the COVID_pos cohort over the COVID_neg cohort on day −4 (3.7-fold, p-value = 3.53E-06), day −3 (3.1-fold, p-value = 3.4E-04), and day −2 (2.9-fold, p-value = 9.4E-05). The delay in the onset of change in appetite/intake compared to the other aforementioned symptoms indicates that this change only appears after other symptoms have already manifested and thus could be secondary to these symptoms rather than directly caused by infection.

This high-resolution temporal overview of the EHR-derived clinical symptoms as they manifest prior to the SARS-CoV-2 PCR diagnostic testing date for 77,167 patients has revealed specific enriched signals of impending COVID-19 onset. These clinical insights can help modulate social distancing measures and appropriate clinical care for individuals exhibiting the specific sensory (anosmia, dysgeusia), respiratory (cough, difficulty breathing), gastro-intestinal (diarrhea, change in appetite/intake), and other (fever/chills, arthralgia/myalgia) symptoms identified herein, including for patients awaiting conclusive COVID-19 diagnostic testing results (e.g. by SARS-CoV-2 RNA RT-PCR).

While PCR testing is the current diagnostic standard of COVID-19, identifying risk of a positive diagnosis earlier is essential to mitigate the spread of the virus. Patients with these symptom risk factors could be tested earlier, undergo closer monitoring, and be adequately quarantined to not only ensure better treatment for the patient, but to prevent the infection of others. Additionally, as businesses begin to reopen, understanding these risk factors will be critical in areas where comprehensive PCR testing is not possible. This study demonstrates how such symptoms can be extracted from highly unstructured institutional knowledge and synthesized using deep learning and neural networks (Devlin et al., 2019). Such augmented curation, providing fine-grained, temporal resolution of symptoms, can be applied toward supporting differential diagnosis of patients in a clinical setting. Expanding beyond one institution’s COVID-19 diagnostic testing and clinical care to the EHR databases of other academic medical centers and health systems will provide a more holistic view of clinical symptoms enriched in COVID_pos over COVID_neg patients in the days preceding confirmed diagnostic testing. This requires leveraging a privacy-preserving, federated software architecture that enables each medical center to retain the span of control of their de-identified EHR databases, while enabling the machine learning models from partners to be deployed in their secure cloud infrastructure. To this end, seamless multi-institute collaborations over an Augmented Intelligence platform, which puts patient privacy and HIPAA-compliance first, are being advanced actively over the Mayo Clinic’s Clinical Data Analytics Platform Initiative (CDAP). The capabilities demonstrated in this study for rapidly synthesizing unstructured clinical notes to develop an EHR-powered clinical diagnosis framework will be further strengthened through such a universal biomedical research platform.

There are a few caveats that must be considered when relying solely on EHR inference to track symptoms preceding the PCR testing date. In addition to concerns regarding testing accuracy, there is an inherent delay in PCR testing, which arises because both the patient and physician must decide the symptoms warrant PCR testing. More specifically, to be tested, the patient must first consider the symptoms serious enough to visit the clinic and then the physician must determine the symptoms present a possibility of COVID infection. The length of this delay could also be influenced by how well-informed the public is of COVID-19 signs and symptoms, the availability of PCR testing, and the hospital protocols used to determine which patients get tested. Each of these factors would be absent or limited at the beginning of a pandemic but would increase or improve over time. This makes synchronization across patients difficult because the delay between symptom onset and PCR testing changes over time. For example, patients infected early in the pandemic would be less inclined to visit the clinic with mild symptoms, while those infected later have more information and more cause to get tested earlier. Similarly, in the early stages of the COVID-19 pandemic when PCR testing was limited, physicians were forced to reserve tests for more severe cases or for those who were in direct contact with a COVID_pos individual, whereas now PCR testing is more widespread. In each case, the delay between symptom onset and PCR testing would be expected to change over time for a given patient population.

Additionally, there are caveats surrounding data availability when working with such real-world datasets, including data sparsity and reporting. For example, while each patient had at least one clinic visit, accompanied by physician notes, between days −7 and 0, only 6 (0.3%) of the COVID_pos and 372 (0.5%) of the COVID_neg patients had notes from all 7 days prior to PCR testing. Moreover, the number of patients with notes prior to PCR testing tends to decrease with each day prior to the PCR testing date for both cohorts (Supplementary file 1e). With regard to reporting, mild symptoms, particularly those seemingly unrelated to presentation for clinical care, such as anosmia, may go unreported. Finally, it should also be noted that COVID_neg patients may not necessarily be representative of a ‘healthy’ cohort comparable to a randomly selected group from the general population, as these patients each had a reason for seeking out COVID-19 PCR testing. With all of these caveats in mind, the fact that the temporal distribution of symptoms significantly differs between the COVID_pos and COVID_neg patients remains and demonstrates that synchronization using the PCR testing date is apt for the real-world data analysis described herein. Thus, by understanding the temporal progression of symptoms prior to PCR testing, we aim to reduce the delay between infection and testing in the future.

As we continue to understand the diversity of COVID-19 patient outcomes through holistic inference of EHR systems, it is equally important to invest in uncovering the molecular mechanisms (Anand et al., 2020) and gain cellular/tissue-scale pathology insights through large-scale patient-derived biobanking and multi-omics sequencing (Venkatakrishnan et al., 2020). To correlate patterns of molecular expression with EHR-derived symptom signals of COVID-19 disease progression, a large-scale bio-banking system has to be created. Such a system will enable deep molecular insights into COVID-19 to be gleaned and triangulated with SARS-CoV-2 tropism and patient outcomes, allowing researchers to better evaluate disease staging and synchronize patients for analyses similar to those presented here. Ultimately, connecting the dots between the temporal dynamics of COVID_pos and COVID_neg clinical symptoms across diverse patient populations to the multi-omics signals from patient-derived bio-specimen will help advance a more holistic understanding of COVID-19 pathophysiology. This will set the stage for a precision medicine approach to the diagnostic and therapeutic management of COVID-19 patients.

The EHR dataset where augmented curation was conducted from the Mayo Clinic records was accessed under IRB 20-003278, "Study of COVID-19 patient characteristics with augmented curation of Electronic Health Records (EHR) to inform strategic and operational decisions". The EHR data cannot be shared or released due to HIPAA regulations. Contact corresponding authors for additional details regarding the IRB, and please refer to the Mayo Clinic IRB website for further details on our commitment to patient privacy (https://www.mayo.edu/research/institutional-review-board/overview). The summary statistics derived from the EHRs are enclosed within the manuscript.

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Author details

1. Tyler Wagner

   nference, Cambridge, United States

   Contribution

   Data curation, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   FNU Shweta

   Competing interests

   is an employee of nference and has financial interests in the company.

2. FNU Shweta

   Mayo Clinic, Rochester, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Tyler Wagner

   Competing interests

   has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

   "This ORCID iD identifies the author of this article:" 0000-0001-6634-6272

3. Karthik Murugadoss

   nference, Cambridge, United States

   Contribution

   Software, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   is an employee of nference and has financial interests in the company.

4. Samir Awasthi

   nference, Cambridge, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   is an employee of nference and has financial interests in the company.

5. AJ Venkatakrishnan

   nference, Cambridge, United States

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   is an employee of nference and has financial interests in the company.

   "This ORCID iD identifies the author of this article:" 0000-0003-2819-3214

6. Sairam Bade

   nference Labs, Bangalore, India

   Contribution

   Software, Formal analysis, Investigation, Methodology

   Competing interests

   is an employee of nference and has financial interests in the company.

7. Arjun Puranik

   nference, Cambridge, United States

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization, Methodology

   Competing interests

   is an employee of nference and has financial interests in the company.

8. Martin Kang

   nference, Cambridge, United States

   Contribution

   Data curation, Validation, Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   is an employee of nference and has financial interests in the company.

9. Brian W Pickering

   Mayo Clinic, Rochester, United States

   Contribution

   Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

10. John C O'Horo

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Validation, Investigation, Visualization, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

11. Philippe R Bauer

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

12. Raymund R Razonable

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

13. Paschalis Vergidis

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

14. Zelalem Temesgen

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

15. Stacey Rizza

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

16. Maryam Mahmood

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

17. Walter R Wilson

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

18. Douglas Challener

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

    "This ORCID iD identifies the author of this article:" 0000-0002-6964-9639

19. Praveen Anand

    nference Labs, Bangalore, India

    Contribution

    Formal analysis, Investigation, Methodology, Writing - review and editing

    Competing interests

    is an employee of nference and has financial interests in the company.

    "This ORCID iD identifies the author of this article:" 0000-0002-2478-7042

20. Matt Liebers

    nference, Cambridge, United States

    Contribution

    Supervision, Visualization, Project administration

    Competing interests

    is an employee of nference and has financial interests in the company.

21. Zainab Doctor

    nference, Cambridge, United States

    Contribution

    Supervision, Investigation, Visualization, Project administration

    Competing interests

    is an employee of nference and has financial interests in the company.

22. Eli Silvert

    nference, Cambridge, United States

    Contribution

    Visualization, Writing - original draft, Writing - review and editing

    Competing interests

    is an employee of nference and has financial interests in the company.

23. Hugo Solomon

    nference, Cambridge, United States

    Contribution

    Visualization, Writing - original draft, Writing - review and editing

    Competing interests

    is an employee of nference and has financial interests in the company.

24. Akash Anand

    nference Labs, Bangalore, India

    Contribution

    Software, Formal analysis, Validation, Methodology

    Competing interests

    is an employee of nference and has financial interests in the company.

25. Rakesh Barve

    nference Labs, Bangalore, India

    Contribution

    Resources, Software, Formal analysis, Supervision, Investigation, Methodology, Project administration

    Competing interests

    is an employee of nference and has financial interests in the company.

26. Gregory Gores

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

27. Amy W Williams

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

28. William G Morice II

    1. Mayo Clinic, Rochester, United States
    2. Mayo Clinic Laboratories, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

29. John Halamka

    Mayo Clinic, Rochester, United States

    Contribution

    Resources, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

30. Andrew Badley

    Mayo Clinic, Rochester, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    Badley.Andrew@mayo.edu

    Competing interests

    has a Financial Conflict of Interest in technology used in the research and with Mayo Clinic may stand to gain financially from the successful outcome of the research. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and is being conducted in compliance with Mayo Clinic Conflict of Interest policies.

31. Venky Soundararajan

    nference, Cambridge, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    venky@nference.net

    Competing interests

    is an employee of nference and has financial interests in the company.

    "This ORCID iD identifies the author of this article:" 0000-0001-7434-9211

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