Your browser doesn't support javascript.
Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis.
Wagner, Tyler; Shweta, Fnu; Murugadoss, Karthik; Awasthi, Samir; Venkatakrishnan, A J; Bade, Sairam; Puranik, Arjun; Kang, Martin; Pickering, Brian W; O'Horo, John C; Bauer, Philippe R; Razonable, Raymund R; Vergidis, Paschalis; Temesgen, Zelalem; Rizza, Stacey; Mahmood, Maryam; Wilson, Walter R; Challener, Douglas; Anand, Praveen; Liebers, Matt; Doctor, Zainab; Silvert, Eli; Solomon, Hugo; Anand, Akash; Barve, Rakesh; Gores, Gregory; Williams, Amy W; Morice, William G; Halamka, John; Badley, Andrew; Soundararajan, Venky.
  • Wagner T; nference, Cambridge, United States.
  • Shweta F; Mayo Clinic, Rochester, United States.
  • Murugadoss K; nference, Cambridge, United States.
  • Awasthi S; nference, Cambridge, United States.
  • Venkatakrishnan AJ; nference, Cambridge, United States.
  • Bade S; nference Labs, Bangalore, India.
  • Puranik A; nference, Cambridge, United States.
  • Kang M; nference, Cambridge, United States.
  • Pickering BW; Mayo Clinic, Rochester, United States.
  • O'Horo JC; Mayo Clinic, Rochester, United States.
  • Bauer PR; Mayo Clinic, Rochester, United States.
  • Razonable RR; Mayo Clinic, Rochester, United States.
  • Vergidis P; Mayo Clinic, Rochester, United States.
  • Temesgen Z; Mayo Clinic, Rochester, United States.
  • Rizza S; Mayo Clinic, Rochester, United States.
  • Mahmood M; Mayo Clinic, Rochester, United States.
  • Wilson WR; Mayo Clinic, Rochester, United States.
  • Challener D; Mayo Clinic, Rochester, United States.
  • Anand P; nference Labs, Bangalore, India.
  • Liebers M; nference, Cambridge, United States.
  • Doctor Z; nference, Cambridge, United States.
  • Silvert E; nference, Cambridge, United States.
  • Solomon H; nference, Cambridge, United States.
  • Anand A; nference Labs, Bangalore, India.
  • Barve R; nference Labs, Bangalore, India.
  • Gores G; Mayo Clinic, Rochester, United States.
  • Williams AW; Mayo Clinic, Rochester, United States.
  • Morice WG; Mayo Clinic, Rochester, United States.
  • Halamka J; Mayo Clinic Laboratories, Rochester, United States.
  • Badley A; Mayo Clinic, Rochester, United States.
  • Soundararajan V; Mayo Clinic, Rochester, United States.
Elife ; 92020 07 07.
Article in English | MEDLINE | ID: covidwho-635065
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
Semantic information from SemMedBD (by NLM)
1. Coughing PROCESS_OF Patients
Subject
Coughing
Predicate
PROCESS_OF
Object
Patients
2. Fever with chills PROCESS_OF Patients
Subject
Fever with chills
Predicate
PROCESS_OF
Object
Patients
3. Coughing PROCESS_OF Patients
Subject
Coughing
Predicate
PROCESS_OF
Object
Patients
4. Fever with chills PROCESS_OF Patients
Subject
Fever with chills
Predicate
PROCESS_OF
Object
Patients
ABSTRACT
Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVIDpos; n = 2,317) versus COVID-19-negative (COVIDneg; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVIDpos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Clinical Laboratory Techniques Type of study: Diagnostic study / Prognostic study Limits: Adult / Female / Humans / Male / Middle aged Language: English Year: 2020 Document Type: Article Affiliation country: ELife.58227

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Clinical Laboratory Techniques Type of study: Diagnostic study / Prognostic study Limits: Adult / Female / Humans / Male / Middle aged Language: English Year: 2020 Document Type: Article Affiliation country: ELife.58227