There is a growing body of evidence suggesting that severe COVID-19 outcomes may be associated with dysregulated coagulation (Tang et al., 2020), including stroke, pulmonary embolism, myocardial infarction, and other venous or arterial thromboembolic complications (Klok et al., 2020). This so-called COVID-19 associated coagulopathy (CAC) shares similarities with disseminated intravascular coagulation (DIC) and thrombotic microangiopathy but also has distinctive features (Levi et al., 2020). Given the significance of CAC to COVID-19 mortality, there is an urgent need for fine-grained resolution into the temporal manifestation of CAC, particularly in comparison to the broad-spectrum of other, better characterized coagulopathies. While there are studies suggesting associations between COVID-19 infection and mortality with thrombocytopenia, D-dimer levels, and prolongation of prothrombin time, the signatures of CAC onset and progression as well as their connection to clinical outcomes are not well defined (Tang et al., 2020; Gao et al., 2020; Panigada et al., 2020). An advanced understanding of this phenotype may aid in the risk stratification of patients, thus facilitating optimal monitoring strategies during disease evolution through the paradigm of precision medicine.

To this end, we instituted a holistic data science platform across an academic medical center that enables machine intelligence to augment the curation of phenotypes and outcomes from over 10 million electronic health record (EHR) clinical notes and associated 3.2 million lab tests from 2232 SARS-CoV-2 positive (COVID_pos) and 72,354 confirmed SARS-CoV-2 negative (COVID_neg) patients over a retrospectively defined 2-month observation period straddling the date of the PCR test. For the COVID_pos cohort, we center the 2-month observation period around the date of the first positive PCR test for SARS-CoV-2, and for the COVID_neg cohort, we center the 2-month observation period around the date of the first PCR test for SARS-CoV-2 (see Materials and methods). It is important to note that not all individuals infected by SARS-CoV-2 develop symptoms of COVID-19, but rather that a majority of patients are either asymptomatic or have mild-to-moderate symptoms not requiring hospitalization for COVID-19 (Wagner et al., 2020). Furthermore, the guidelines followed for PCR-testing included a routine screening of individuals, patients displaying COVID-19 symptoms as per the Mayo Clinic (Coronavirus disease, 2019) and CDC definitions (Website, 2020), and possibly contact with infected persons or underlying predisposing conditions (Wagner et al., 2020).

By compiling all available laboratory testing data for the 30 days preceding the first SARS-CoV-2 PCR positive diagnostic testing date (day 0), as well as the 30 days following the diagnostic testing date, and triangulating this information with medications and clinical outcomes, we were able to identify laboratory abnormalities significantly associated with the COVID_pos group. We identified coagulation-related parameters among this set of abnormalities and then studied aggregate as well as individual patient trajectories that could aid in extracting a temporal signature of CAC onset and progression. We also correlated these signals with the clinical outcomes of these patients.

In order to hone into longitudinal lab test trends that would apply at the individual patient level, we restricted our analysis to patients with available serial testing data, which had at least three test results of the same type during the observation period. After applying these inclusion criteria, 246 COVID_pos and 13,666 COVID_neg patients met study the inclusion criteria. The need for longitudinal data on the testing results, while constraining the study population size greatly, enables us to provide a fine-grained temporal resolution of CAC for the first time.

After filtering the patients with the available longitudinal testing data, the median age in the COVID_pos and COVID_neg groups were 60.8 years and 64.1 years, respectively (see Materials and methods and Table 1), and the numbers of males were 137 (56%) and 7129 (52%), respectively. The total numbers of pre-existing coagulopathies in the COVID_pos and COVID_neg groups were 31 (13%) and 3901 (29%), respectively. These counts of coagulopathies include the following phenotypes identified in the clinical notes from day −365 to day −31 relative to the PCR testing date: deep vein thrombosis, pulmonary embolism, myocardial infarction, venous thromboembolism, thrombotic stroke, cerebral venous thrombosis, and disseminated intravascular coagulation (see Table 1 for detailed breakdown). The number of COVID_pos patients hospitalized in the month prior to the SARS-CoV-2 PCR testing date was 41 (17%), compared to 1247 (9.1%) for the COVID_neg cohort.

To balance these clinical covariates and others between the two cohorts, we applied 1:10 propensity score matching to define a subset of 2460 patients from the COVID_neg cohort to use for the final statistical analysis (see Materials and methods). In particular, the general categories of covariates considered for balancing included: demographics, anticoagulant/antiplatelet medication use, medical history of pre-existing coagulopathies, and hospital admission status. Population-level characteristics of the COVID_pos, COVID_neg, and the final propensity score-matched COVID_neg (matched) cohorts are summarized in Table 1. We observe that the COVID_pos and COVID_neg (matched) cohorts are well-balanced along these covariates which are potential confounding variables for thrombotic events and coagulopathy-related lab tests during the study period.

Many studies on clinical characteristics and lab tests are shedding light on the spectrum of hematological parameters associated with COVID-19 patients. In an initial study of 41 patients from Wuhan, the blood counts in COVID_pos patients showed leukopenia and lymphopenia, and prothrombin time and D-dimer levels were higher in ICU patients than in non-ICU patients (Huang et al., 2020). Another study based on 343 Wuhan COVID_pos patients found that a D‐dimer level of at least 2.0 µg/mL could predict mortality with a sensitivity of 92.3% and a specificity of 83.3% (Zhang et al., 2020). An independent study of 43 COVID-19 patients found significant differences between mild and severe cases in plasma interleukin‐6 (IL‐6), D‐dimers, glucose, thrombin time, fibrinogen, and C‐reactive protein (p<0.05; Gao et al., 2020). While such studies indeed highlight that hematological and inflammatory abnormalities are prevalent in COVID_pos, a high-resolution temporal understanding of how these parameters evolve in COVID-19 patients post diagnosis has not been established. Specifically, in the wake of accumulating evidence for hypercoagulability in COVID_pos patients, there are important clinical questions emerging regarding the necessity of and guidelines for thromboprophylaxis in patient management.

DIC-like consumptive coagulopathy in COVID-19 has been a point of concern in severely ill COVID-19 patients. Particularly in patients with ARDS, multiple organ dysfunction syndrome (MODS) is the predominant cause of death. A recent study suggested that DIC was associated with MODS during the early stage of ARDS and that persistent DIC may also have a role in this association (Gando et al., 2020). Our study focusing on COVID-19 patients with longitudinal lab data suggests that COVID-19 is indeed associated with modulation of coagulation related parameters such as platelet counts, fibrinogen levels, and clotting time (Figure 2). However, the majority of thrombotic events in COVID-19 patients with longitudinal lab testing are not the result of a DIC-like consumptive coagulopathy, as this only occurs in a small subset (Table 4).

The ability to derive this longitudinal understanding of COVID-19 progression, including laboratory abnormalities and their associated clinical manifestations, mandates the synthesis of structured and unstructured EHR data (e.g. lab tests and clinical notes) at a large scale. The fact that tens of thousands of patients have undergone SARS-CoV-2 testing at major academic medical centers (AMCs) provides an abundance of potential data to perform this analysis but also poses significant challenges from a practicality standpoint. Manual review and curation of patient trajectories and associated testing results is not practical. It is not likely to provide comprehensive or even entirely accurate individual patient records. Rather, triangulation across datasets, including lab measurements, clinical notes, and prescription information, using a scalable digitized approach to extract structured data along with sentiment-surrounded clinical phenotypes and outcomes enables us to efficiently perform this analysis in a timely fashion.

By developing and deploying such a digitized platform on the entirety of EHR data from a large AMC, we have identified in an unbiased manner, laboratory test-based abnormalities that differentiate COVID_pos patients from COVID_neg patients. The abnormalities in coagulation-related tests, including fibrinogen and platelets, were intriguing in the context of literature reporting the occurrence of various clotting phenotypes in COVID-19 patients, including DIC-like consumptive coagulopathies along with more isolated clotting events in the lungs, central nervous system, and other tissues (Tang et al., 2020; Klok et al., 2020; Levi et al., 2020). Our finding that consumptive coagulopathy represents a minority of COVID-19 associated clotting events provides context for other studies, which have reported overt DIC or DIC-like disease in over 70% of non-survivors but far lower fractions of survivors (Tang et al., 2020). As the pandemic continues to evolve and the patient counts increase over the coming months, we will be monitoring and reporting any updates to the clinical and laboratory observations drawn in this study.

Notwithstanding the preliminary nature of the analysis presented in this study, the results highlight that consumptive coagulopathy should be considered in the minority of COVID_pos patients with significant serial reductions in platelet counts. It remains to be seen whether the post-diagnosis platelet increases or early hyperfibrinogenemia which we observed may contribute mechanistically to the clotting in the much larger non-DIC thrombotic COVID-19 population. It is important to note that despite the trend of increasing platelets, the platelet count only extended above the normal range (>450×10⁹/L) after the PCR date in few COVID_pos patients with serial measurements, and the development of such outright thrombocytosis was observed with similar frequencies in the thrombotic and non-thrombotic cohorts (Figure 5C). Further, the fact that several patients with elevated fibrinogen (i.e. >400 mg/dL) at presentation did not develop thromboses suggests that early hyperfibrinogenemia is not a singular driver of subsequent clotting events, but a small sample size (n = 10 patients; nine non-thrombotic vs. one thrombotic) limited the power of this analysis (Figure 6).

Despite these caveats, this linking of longitudinal trends to patient outcomes provides several useful pieces of clinical information. First, hyperfibrinogenemia is to be expected in COVID-19 patients around the time of diagnostic testing. Furthermore, declining fibrinogen levels shortly after diagnosis are also expected and likely represent the resolution of acute phase response in most patients rather than a decline secondary to the onset of consumptive coagulation. In addition, borderline or overt thrombocytopenia is common in COVID-19 patients at the time of clinical presentation, and the initial platelet count does not robustly predict patients who are likely to develop thromboses. After diagnosis, COVID-19 patients generally show an upward trend in platelets. Patients whose platelets trend down after diagnosis should be monitored, as platelet reductions after clinical presentation are associated with thromboses and significant reductions may be indicative of ongoing consumptive coagulopathy.

One unavoidable limitation of this study is that we restrict our analysis to patients which have longitudinal lab testing data available. While the inclusion criteria is naturally biased, we consider this study population to be of high clinical interest because these patients are highly enriched for severe thrombotic events during the study period (see Table 5). Further, in the propensity score matching step of the analysis, we are able to construct a control cohort that is similar to the COVID_pos cohort in these enriched dimensions. To provide additional color on the distinctive attributes of the study population, we provide a summary of the clinical characteristics of the study population versus all patients with PCR tests during the same time period (see Table 7). In addition, we provide the median numbers of lab tests per patient for selected coagulation-related lab tests (fibrinogen, platelets, PTT, APTT, D-dimer) and total lab tests (Tables 8 and 9).

It is important to note that while we center the study period around the PCR testing date, this date may not correspond to the same disease state of COVID-19 for each individual in the COVID_pos cohort. To account for the potential variability in disease progression, we have performed a sensitivity analysis on the time intervals (Table 3). Additionally, there are several covariates that may influence these longitudinal trends and should be explored further. For example, we have already considered whether previous or concomitant administration of anticoagulants or antiplatelet agents influences patient lab test results and/or outcomes. Similarly, in the future, we intend to explore whether longitudinal lab measurement trends differ between outpatient, inpatient, and ICU admitted patient cohorts. New datasets can also be utilized; for example, rather than grouping patients by the identified thromboembolic phenotypes extracted from the clinical notes alone, patients could be stratified by those who had imaging studies (duplex ultrasound, CT scan, etc.) performed, and phenotypes could be directly extracted from these procedural reports. As more data accumulates from COVID_pos and COVID_neg patients in the coming months, these analyses need to be expanded to assess similarities and differences in the temporal trends of laboratory test results among a wider range of patient subgroups relevant for COVID-19 outcomes, such as those who have pre-existing conditions (e.g. diabetes, hypertension, obesity, malignancies) or patients who are on specific medication (e.g. ACE inhibitors, statins, immunosuppressants).

In summary, this work demonstrates significant progress toward enabling scaled and digitized analyses of longitudinal unstructured and structured EHRs to identify variables (e.g. laboratory results) which are associated with relevant clinical phenotypes (e.g. COVID-19 diagnosis and outcomes). In doing so, we identified trends in lab test results which may be relevant to monitor in COVID-19 patients and warrant both clinical and mechanistic follow-up in more targeted and explicitly controlled prospective analyses.

After publication, the data will be made available to others upon reasonable requests to the corresponding author. A proposal with detailed description of study objectives and statistical analysis plan will be needed for evaluation of the reasonability of requests. Deidentified data will be provided after approval from the corresponding author and Mayo Clinic.

1. 1. Austin PC

   (2011) An introduction to propensity score methods for reducing the effects of confounding in observational studies

   Multivariate Behavioral Research 46:399–424.

   https://doi.org/10.1080/00273171.2011.568786

   • Google Scholar
2. 1. Austin PC

   (2014) A comparison of 12 algorithms for matching on the propensity score

   Statistics in Medicine 33:1057–1069.

   https://doi.org/10.1002/sim.6004

   • PubMed
   • Google Scholar
3. Report

   1. Coronavirus disease

   (2019)

   (COVID-19) - Symptoms and Causes - Mayo Clinic. (2020)

   Mayo clinic.

   • Google Scholar
4. Preprint

   1. Devlin J
   2. Chang MW
   3. Lee K
   4. Toutanov K

   (2018) BERT: pre-training of deep bidirectional transformers for language understanding

   arXiv.

   https://arxiv.org/abs/1810.04805

   • Google Scholar
5. 1. Gando S
   2. Fujishima S
   3. Saitoh D
   4. Shiraishi A
   5. Yamakawa K
   6. Kushimoto S
   7. Ogura H
   8. Abe T
   9. Mayumi T
   10. Sasaki J
   11. Kotani J
   12. Takeyama N
   13. Tsuruta R
   14. Takuma K
   15. Yamashita N
   16. Shiraishi SI
   17. Ikeda H
   18. Shiino Y
   19. Tarui T
   20. Nakada TA
   21. Hifumi T
   22. Otomo Y
   23. Okamoto K
   24. Sakamoto Y
   25. Hagiwara A
   26. Masuno T
   27. Ueyama M
   28. Fujimi S
   29. Umemura Y
   30. Japanese Association for Acute Medicine (JAAM) Focused Outcomes Research in Emergency Care in Acute Respiratory Distress Syndrome, Sepsis and Trauma (FORECAST) Study Group

   (2020) The significance of disseminated intravascular coagulation on multiple organ dysfunction during the early stage of acute respiratory distress syndrome

   Thrombosis Research 191:15–21.

   https://doi.org/10.1016/j.thromres.2020.03.023

   • PubMed
   • Google Scholar
6. 1. Gao Y
   2. Li T
   3. Han M
   4. Li X
   5. Wu D
   6. Xu Y
   7. Zhu Y
   8. Liu Y
   9. Wang X
   10. Wang L

   (2020) Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19

   Journal of Medical Virology 92:791–796.

   https://doi.org/10.1002/jmv.25770

   • PubMed
   • Google Scholar
7. 1. Huang C
   2. Wang Y
   3. Li X
   4. Ren L
   5. Zhao J
   6. Hu Y
   7. Zhang L
   8. Fan G
   9. Xu J
   10. Gu X
   11. Cheng Z
   12. Yu T
   13. Xia J
   14. Wei Y
   15. Wu W
   16. Xie X
   17. Yin W
   18. Li H
   19. Liu M
   20. Xiao Y
   21. Gao H
   22. Guo L
   23. Xie J
   24. Wang G
   25. Jiang R
   26. Gao Z
   27. Jin Q
   28. Wang J
   29. Cao B

   (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

   The Lancet 395:497–506.

   https://doi.org/10.1016/S0140-6736(20)30183-5

   • Google Scholar
8. 1. Klok FA
   2. Kruip M
   3. van der Meer NJM
   4. Arbous MS
   5. Gommers D
   6. Kant KM
   7. Kaptein FHJ
   8. van Paassen J
   9. Stals MAM
   10. Huisman MV
   11. Endeman H

   (2020) Incidence of thrombotic complications in critically ill ICU patients with COVID-19

   Thrombosis Research 191:145–147.

   https://doi.org/10.1016/j.thromres.2020.04.013

   • PubMed
   • Google Scholar
9. 1. Levi M
   2. Thachil J
   3. Iba T
   4. Levy JH

   (2020) Coagulation abnormalities and thrombosis in patients with COVID-19

   The Lancet Haematology 7:e438–e440.

   https://doi.org/10.1016/S2352-3026(20)30145-9

   • Google Scholar
10. 1. Panigada M
    2. Bottino N
    3. Tagliabue P
    4. Grasselli G
    5. Novembrino C
    6. Chantarangkul V
    7. Pesenti A
    8. Peyvandi F
    9. Tripodi A

    (2020) Hypercoagulability of COVID-19 patients in intensive care unit: a report of thromboelastography findings and other parameters of hemostasis

    Journal of Thrombosis and Haemostasis 18:1738–1742.

    https://doi.org/10.1111/jth.14850

    • PubMed
    • Google Scholar
11. 1. Tang N
    2. Li D
    3. Wang X
    4. Sun Z

    (2020) Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia

    Journal of Thrombosis and Haemostasis 18:844–847.

    https://doi.org/10.1111/jth.14768

    • PubMed
    • Google Scholar
12. 1. Wagner T
    2. Shweta F
    3. Murugadoss K
    4. Awasthi S
    5. Venkatakrishnan AJ
    6. Bade S
    7. Puranik A
    8. Kang M
    9. Pickering BW
    10. O'Horo JC
    11. Bauer PR
    12. Razonable RR
    13. Vergidis P
    14. Temesgen Z
    15. Rizza S
    16. Mahmood M
    17. Wilson WR
    18. Challener D
    19. Anand P
    20. Liebers M
    21. Doctor Z
    22. Silvert E
    23. Solomon H
    24. Anand A
    25. Barve R
    26. Gores G
    27. Williams AW
    28. Morice WG
    29. Halamka J
    30. Badley A
    31. Soundararajan V

    (2020) Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis

    eLife 9:e58227.

    https://doi.org/10.7554/eLife.58227

    • PubMed
    • Google Scholar
13. Website

    1. Website

    (2020) Symptoms of coronavirus

    Accessed May 13, 2020.

    https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
14. 1. Xu P
    2. Zhou Q
    3. Xu J

    (2020) Mechanism of thrombocytopenia in COVID-19 patients

    Annals of Hematology 99:1205–1208.

    https://doi.org/10.1007/s00277-020-04019-0

    • PubMed
    • Google Scholar
15. 1. Yang X
    2. Yang Q
    3. Wang Y
    4. Wu Y
    5. Xu J
    6. Yu Y
    7. Shang Y

    (2020) Thrombocytopenia and its association with mortality in patients with COVID-19

    Journal of Thrombosis and Haemostasis 18:1469–1472.

    https://doi.org/10.1111/jth.14848

    • PubMed
    • Google Scholar
16. 1. Zhang L
    2. Yan X
    3. Fan Q
    4. Liu H
    5. Liu X
    6. Liu Z
    7. Zhang Z

    (2020) D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19

    Journal of Thrombosis and Haemostasis 18:1324–1329.

    https://doi.org/10.1111/jth.14859

    • PubMed
    • Google Scholar

Author details

1. Colin Pawlowski

   nference, inc, Cambridge, United States

   Contribution

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

   Competing interests

   This author is an employee of nference with financial interests in the company

   "This ORCID iD identifies the author of this article:" 0000-0003-2781-7507

2. Tyler Wagner

   nference, inc, Cambridge, United States

   Contribution

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

   Competing interests

   This author is an employee of nference with financial interests in the company

3. Arjun Puranik

   nference, inc, Cambridge, United States

   Contribution

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

   Competing interests

   This author is an employee of nference with financial interests in the company

4. Karthik Murugadoss

   nference, inc, Cambridge, United States

   Contribution

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

   Competing interests

   This author is an employee of nference with financial interests in the company

5. Liam Loscalzo

   nference, inc, Cambridge, United States

   Contribution

   Formal analysis, Supervision, Investigation, Methodology, Project administration

   Competing interests

   This author is an employee of nference with financial interests in the company

6. AJ Venkatakrishnan

   nference, inc, Cambridge, United States

   Contribution

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

   Competing interests

   This author is an employee of nference with financial interests in the company

7. Rajiv K Pruthi

   Mayo Clinic, Rochester, United States

   Contribution

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

   Competing interests

   One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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.

8. Damon E Houghton

   Mayo Clinic, Rochester, United States

   Contribution

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

   Competing interests

   One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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.

9. John C O'Horo

   Mayo Clinic, Rochester, United States

   Contribution

   Supervision, Investigation, Project administration, Writing - review and editing

   Competing interests

   One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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. William G Morice II

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

    Contribution

    Supervision, Investigation, Methodology, Project administration

    Competing interests

    One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and 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. The author is also involved in the Mayo Clinic Laboratories.

11. Amy W Williams

    Mayo Clinic, Rochester, United States

    Contribution

    Supervision, Investigation, Project administration, Writing - review and editing

    Competing interests

    One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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. Gregory J Gores

    Mayo Clinic, Rochester, United States

    Contribution

    Supervision, Investigation, Project administration, Writing - review and editing

    Competing interests

    One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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. John Halamka

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

    Contribution

    Supervision, Project administration

    Competing interests

    One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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. Andrew D Badley

    Mayo Clinic, Rochester, United States

    Contribution

    Supervision, Validation, Project administration, Writing - review and editing

    Competing interests

    One or more of the investigators associated with this project and Mayo Clinic have a Financial Conflict of Interest in technology used in the research and that the investigator(s) and Mayo Clinic may stand to gain financially from the successful outcome of the research. Mayo Clinic is an investor in nference. 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. Elliot S Barnathan

    Janssen pharmaceutical companies of Johnson & Johnson (J&J), Spring House, United States

    Contribution

    Supervision, Validation, Investigation, Methodology, Writing - review and editing

    Competing interests

    This author is an employee of the Janssen pharmaceutical companies of J&J with financial interests in the company

16. Hideo Makimura

    Janssen pharmaceutical companies of Johnson & Johnson (J&J), Spring House, United States

    Contribution

    Supervision, Validation, Investigation, Methodology, Writing - review and editing

    Competing interests

    This author is an employee of the Janssen pharmaceutical companies of J&J with financial interests in the company

17. Najat Khan

    Janssen pharmaceutical companies of Johnson & Johnson (J&J), Spring House, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing - review and editing

    Competing interests

    This author is an employee of the Janssen pharmaceutical companies of J&J with financial interests in the company

18. Venky Soundararajan

    nference, inc, Cambridge, United States

    Contribution

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

    For correspondence

    venky@nference.net

    Competing interests

    The author is an employee of nference and has financial interests in the company. Outside the submitted work, Venky Soundararajan is listed as inventor of the following patent: "Systems, methods, and computer readable media for visualization of semantic information and inference of temporal signals indicating salient associations between life science entities" (US20180082197A1).

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

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