Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)

The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)

The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence; Machine Learning; Electronic Medical Records

Detailed Description

Despite the rapid development of medicine and computer science in recent years, the medical treatment in modern clinical practice is often empirical and based on retrospective data. With the growing number of patients and their concentration in large tertiary centers, it becomes attractive to systematically collect clinical data and apply them to risk stratification models. However, with the increasing volume of data, manual data collection and processing becomes a challenge, as this approach is time consuming and costly for the healthcare systems. In addition, unstructured information, such as clinical notes, are very often written as free text that is unsuitable for direct analysis. The use of artificial intelligence is very promising and is going to rapidly change the future of medicine in the upcoming years. Due to the automated processes it offers, it is possible to quickly and reliably extract data for further processing. The results from its use can be easily extended to different healthcare systems, amplifying the knowledge produced and improving diagnostic and therapeutic accuracy, and ultimately positively affecting health services. Collecting the vast amount of data from different sources without compromising patients' personal data is a major challenge in modern science.

Electronically-registered clinical notes of patients who were hospitalized in the Cardiology ward of tertiary hospitals will be retrospectively collected, as well as additional files such as the laboratory and imaging examinations related to each hospitalization. Given the size of the participating clinics and the years during which the recording of electronic hospital records in electronic form was applied, it is estimated that the sample of patient records will be about 60.000. All information that could potentially be used to identify a person, such as name, ID number, postal code, place of residence, occupation, will be deleted from these electronic files. Only the age will be recorded, not the exact date of birth of each patient. Only the days of hospitalization will be recorded and not the exact dates of admission and discharge from the hospital. Thus, the data will not be able to be assigned to a specific subject, as no additional information or identifiers will be collected for the subjects. After the files are anonymized, each patient's clinical note will be linked with a specific key ("identifier"). The electronic file that contains the correlation of the "identifier" with the patient's clinical note will be stored in a secure hospital electronic location. The fully anonymized files will initially be manually analyzed to extract information into a database containing all of patients' clinical information, such as discharge diagnoses, medications, treatment protocols, laboratory and diagnostic tests. At the same time, a sample (1/3) of the clinical notes will be analyzed to identify the keywords or phrases associated with each diagnosis (for example, the atrial fibrillation diagnosis will probably be recorded as "atrial fibrillation", " AF ", etc.). By using this generated dictionary of keywords and by integrating artificial intelligence methods and text mining, such as natural language processing (NLP), an automated extraction of data and diagnoses from these electronic medical notes will be attempted. The reliability and accuracy of the computational methods will be evaluated internally, comparing the data extracted automatically with those recorded manually. In addition, the reliability and accuracy of these computational methods will be evaluated externally, applying these methods to 2/3 of the clinical notes in which no association between keywords and specific diagnoses was attempted.

Regarding Greece, the present study aims to be the first to analyze the usefulness of artificial intelligence for automated extraction and processing of unstructured clinical data from patients' medical clinical notes. The results of this study will have a positive impact on:

1. the automation of large-scale data analysis and processing procedures
2. the rapid epidemiological recording and utilization of clinical data
3. the early diagnosis of diseases
4. the development of phenotypic patient profiles that could benefit from targeted therapies
5. the development of clinical decision support systems that will provide information about the possible clinical course of patients after hospital discharge and assist medical decisions
6. the development and validation of prognostic models for major cardiovascular diseases

• Study Type: Observational
• Enrollment (Anticipated): 60000

Contacts and Locations

• Study Contact: Name: George Giannakoulas, MD, PhD; Phone Number: +30 2310994830; Email: ggiannakoulas@auth.gr
• Study Contact Backup: Name: Athanasios Samaras, MD; Phone Number: +30 2310994830; Email: ath.samaras.as@gmail.com

Study Locations

• Greece
  • Alexandroupoli, Greece
    • Not yet recruiting
    • University Cardiology Clinic, Democritus University of Thrace
    • Contact: George Chalikias, MD, PhD; Email: gchaliki@med.duth.gr
    • Principal Investigator: Dimitrios Tziakas, MD, PhD
  • Athens, Greece
    • Recruiting
    • 1st Department of Cardiology, Hippokration General Hospital
    • Contact: George Lazaros, MD, PhD; Email: glaz35@hotmail.com
    • Principal Investigator: Konstantinos Tsioufis, MD, PhD
  • Heraklion, Greece
    • Not yet recruiting
    • Department of Cardiology, Heraklion University Hospital
    • Contact: Alexandros Patrianakos, MD, PhD; Email: apatrianakos@yahoo.gr
    • Principal Investigator: Alexandros Patrianakos, MD, PhD
  • Ioannina, Greece
    • Not yet recruiting
    • University Cardiology Clinic, University of Ioannina
    • Contact: Aikaterini Naka, MD, PhD; Email: drkknaka@gmail.com
    • Principal Investigator: Aikaterini Naka, MD, PhD
  • Larissa, Greece
    • Recruiting
    • University General Hospital of Larissa, University of Thessaly
    • Contact: Gregory Giamouzis, MD, PhD; Email: grgiamouzis@gmail.com
    • Principal Investigator: Gregory Giamouzis, MD, PhD
  • Patras, Greece
    • Recruiting
    • Department of Cardiology, University of Patras Medical School
    • Contact: Periklis Davlouros, MD, PhD; Email: pdav@upatras.gr
    • Principal Investigator: Periklis Davlouros, MD, PhD
  • Thessaloniki, Greece
    • Not yet recruiting
    • 3rd Cardiology Department, Hippokration Hospital
    • Contact: Vassilios Vassilikos, MD, PhD; Email: vvassil@auth.gr
    • Principal Investigator: Vassilios Vassilikos, MD, PhD
  • Thessaloniki, Greece
    • Recruiting
    • Cardiology Department, George Papanikolaou General Hospital
    • Contact: John Zarifis, MD, PhD; Email: zarifis.john@gmail.com
    • Principal Investigator: John Zarifis, MD, PhD
  • Thessaloníki, Greece, 54636
    • Recruiting
    • 1st Cardiology Department, AHEPA University Hospital
    • Contact: Athanasios Samaras, MD, PhD; Email: ath.samaras.as@gmail.com
    • Principal Investigator: George Giannakoulas, MD, PhD
  • Thessaloníki, Greece
    • Recruiting
    • Laboratory of Medical Physics, Aristotle University of Thessaloniki
    • Contact: Panagiotis Bamidis, Prof; Email: pdbamidis@gmail.com
    • Principal Investigator: Panagiotis Bamidis, Prof

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

All patients who were hospitalized in the Cardiology ward of tertiary hospitals and have available electronically-stored clinical notes/hospitalization documents will be included in the study .

Description

Inclusion Criteria:

• Hospitalised patients in Cardiology Departments in Greece
• Patients whose medical records are electronically stored in each hospital's computer/information systems

Exclusion Criteria:

• Patients that died during hospitalization, and thus no discharge letter was issued

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of artificial intelligence to automatically extract clinical data from patients' medical records compared with traditional manual data extraction methods	Rate of accurate extraction of clinical data (medical history, discharge diagnoses, medication, etc.) from unstructured clinical notes using automated artificial intelligence methods compared with traditional methods of manual data extraction	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Time to all-cause mortality	Length of time (months) until death from any cause during the follow-up period	up to 8 years (from hospital discharge until study primary completion date)
Time to incident major cardiovascular diseases	Length of time (months) until development of heart failure, diabetes mellitus or coronary artery disease during the follow-up period	up to 8 years (from hospital discharge until study primary completion date)
Time to rehospitalization for cardiovascular reasons	Length of time (months) until rehospitalization for cardiovascular reasons during the follow-up period	up to 8 years (from hospital discharge until study primary completion date)
Time to stroke or systemic embolism	Length of time (months) until stroke or systemic embolism during the follow-up period	up to 8 years (from hospital discharge until study primary completion date)
Time to acute coronary syndrome	Length of time (months) until acute coronary syndrome during the follow-up period	up to 8 years (from hospital discharge until study primary completion date)

Collaborators and Investigators

• Sponsor: AHEPA University Hospital
• Collaborators: George Papanicolaou Hospital; University General Hospital of Heraklion; University General Hospital of Patras; Ippokrateio General Hospital of Thessaloniki; University Hospital, Ioannina; University Hospital, Alexandroupolis; General Hospital of Larissa; Hippokration Hospital Athens

Publications and helpful links

General Publications

• Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1:6. doi: 10.1038/s41746-017-0013-1. Epub 2018 Mar 21.
• Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.
• Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017 May 30;69(21):2657-2664. doi: 10.1016/j.jacc.2017.03.571.
• Boag W, Doss D, Naumann T, Szolovits P. What's in a Note? Unpacking Predictive Value in Clinical Note Representations. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:26-34. eCollection 2018.
• Hashir M, Sawhney R. Towards unstructured mortality prediction with free-text clinical notes. J Biomed Inform. 2020 Aug;108:103489. doi: 10.1016/j.jbi.2020.103489. Epub 2020 Jun 25.
• Diller GP, Kempny A, Babu-Narayan SV, Henrichs M, Brida M, Uebing A, Lammers AE, Baumgartner H, Li W, Wort SJ, Dimopoulos K, Gatzoulis MA. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Eur Heart J. 2019 Apr 1;40(13):1069-1077. doi: 10.1093/eurheartj/ehy915.

Study record dates

Study Major Dates

• Study Start (Actual): January 14, 2022
• Primary Completion (Anticipated): December 1, 2022
• Study Completion (Anticipated): March 1, 2023

Study Registration Dates

• First Submitted: November 24, 2021
• First Submitted That Met QC Criteria: December 15, 2021
• First Posted (Actual): January 4, 2022

Study Record Updates

• Last Update Posted (Actual): March 8, 2022
• Last Update Submitted That Met QC Criteria: February 19, 2022
• Last Verified: February 1, 2022

More Information

Terms related to this study

• Keywords: machine learning; artificial intelligence; digital health; medical records
• Other Study ID Numbers: 545/19.11.2021

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Yes
• IPD Plan Description: Study protocol, statistical analysis plan and results will become available through publications. The analytic code will become available in open source communities/repositories
• IPD Sharing Supporting Information Type: Study Protocol; Statistical Analysis Plan (SAP); Analytic Code

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No