Artificial IntelligenceNCT05176769
Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)
An observational study of Artificial Intelligence and Machine Learning, run by AHEPA University Hospital at 9 sites in Greece.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence, Machine Learning, Electronic Medical Records
- How many people
- 60,000 participants
- Where
- 9 sitesGreece
- Study dates
- 14 Jan 2022to 1 Mar 2032
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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 .
Who may be able to join
- Hospitalised patients in Cardiology Departments in Greece
- Patients whose medical records are electronically stored in each hospital's computer/information systems
Who cannot join
- Patients that died during hospitalization, and thus no discharge letter was issued
Trial locations and contacts
9 sites in 1 country. Contact the study team or a site near you to ask about taking part.
9 sites in 1 country
Greece9 sites, 9 recruiting
- Not yet recruiting
University Cardiology Clinic, Democritus University of Thrace
Alexandroupoli
George Chalikias, MD, PhD, gchaliki@med.duth.gr
Dimitrios Tziakas, MD, PhD
- Recruiting
1st Department of Cardiology, Hippokration General Hospital
Athens
George Lazaros, MD, PhD, glaz35@hotmail.com
Konstantinos Tsioufis, MD, PhD
- Not yet recruiting
Department of Cardiology, Heraklion University Hospital
Heraklion
Alexandros Patrianakos, MD, PhD, apatrianakos@yahoo.gr
Alexandros Patrianakos, MD, PhD
- Recruiting
University General Hospital of Larissa, University of Thessaly
Larissa
Gregory Giamouzis, MD, PhD, grgiamouzis@gmail.com
Gregory Giamouzis, MD, PhD
- Recruiting
Department of Cardiology, University of Patras Medical School
Pátrai
Periklis Davlouros, MD, PhD, pdav@upatras.gr
Periklis Davlouros, MD, PhD
- Recruiting
1st Cardiology Department, AHEPA University Hospital
Thessaloniki, 54636
Athanasios Samaras, MD, PhD, ath.samaras.as@gmail.com
George Giannakoulas, MD, PhD
- Not yet recruiting
3rd Cardiology Department, Hippokration Hospital
Thessaloniki
Vassilios Vassilikos, MD, PhD, vvassil@auth.gr
Vassilios Vassilikos, MD, PhD
- Recruiting
Cardiology Department, George Papanikolaou General Hospital
Thessaloniki
John Zarifis, MD, PhD, zarifis.john@gmail.com
John Zarifis, MD, PhD
- Recruiting
Laboratory of Medical Physics, Aristotle University of Thessaloniki
Thessaloniki
Panagiotis Bamidis, Prof, pdbamidis@gmail.com
Panagiotis Bamidis, Prof
About this study
Official title: 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.
Read the 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:
- the automation of large-scale data analysis and processing procedures
- the rapid epidemiological recording and utilization of clinical data
- the early diagnosis of diseases
- the development of phenotypic patient profiles that could benefit from targeted therapies
- 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
- the development and validation of prognostic models for major cardiovascular diseases
- Keywords
- artificial intelligence, machine learning, medical records, digital health
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 1 year
Secondary outcomes (5)
Time to all-cause mortality
Length of time (months) until death from any cause during the follow-up period
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: up to 8 years (from hospital discharge until study primary completion date)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jan 2022
- Study start14 Jan 2022
- Primary completion1 Dec 2030 (estimated)
- Study completion1 Mar 2032 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 60,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- AHEPA University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Hippokration Hospital Athens, General Hospital of Larissa, University Hospital, Alexandroupolis, University General Hospital of Patras, University General Hospital of Heraklion, George Papanicolaou Hospital, Ippokrateio General Hospital of Thessaloniki
- Responsible party
- Principal investigator, George Giannakoulas, AHEPA University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05176769
- Sponsor's protocol ID
- 545/19.11.2021
- Status last verified
- Oct 2026
- First submitted
- 24 Nov 2021
- Last updated
- 27 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Study protocol, statistical analysis plan and results will become available through publications. The analytic code will become available in open source communities/repositories
Publications
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.
PubMed 29880128Krittanawong 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.
PubMed 28545640Madani 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.
PubMed 30828647Boag 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.
PubMed 29888035Hashir 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.
PubMed 32592755Diller 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.
PubMed 30689812Samaras A, Bekiaridou A, Papazoglou AS, Moysidis DV, Tsoumakas G, Bamidis P, Tsigkas G, Lazaros G, Kassimis G, Fragakis N, Vassilikos V, Zarifis I, Tziakas DN, Tsioufis K, Davlouros P, Giannakoulas G; CardioMining Study Group. Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study. BMJ Open. 2023 Apr 3;13(4):e068698. doi: 10.1136/bmjopen-2022-068698.
PubMed 37012018
Study at a glance
From the registry record, last updated by the sponsor on 27 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)
- ClinicalTrials.gov ID
- NCT05176769
- Acronym / sponsor ID
- 545/19.11.2021
- Keywords
- artificial intelligence, machine learning, medical records, digital health
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 60,000 participants (estimated)
- Lead sponsor
- AHEPA University HospitalUniversity, hospital or other
- Collaborators
- Hippokration Hospital Athens, General Hospital of Larissa, University Hospital, Alexandroupolis, University General Hospital of Patras, University General Hospital of Heraklion, George Papanicolaou Hospital, Ippokrateio General Hospital of Thessaloniki
- Study start
- 14 Jan 2022 (actual)
- Primary completion
- 1 Dec 2030 (estimated)
- Study completion
- 1 Mar 2032 (estimated)
- First posted
- 4 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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:
- the automation of large-scale data analysis and processing procedures
- the rapid epidemiological recording and utilization of clinical data
- the early diagnosis of diseases
- the development of phenotypic patient profiles that could benefit from targeted therapies
- 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
- the development and validation of prognostic models for major cardiovascular diseases
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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 .
Key inclusion criteria (2 listed)
- Hospitalised patients in Cardiology Departments in Greece
- Patients whose medical records are electronically stored in each hospital's computer/information systems
Key exclusion criteria (1 listed)
- Patients that died during hospitalization, and thus no discharge letter was issued
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of artificial intelligence to automatically extract clinical data from patients' medical records compared with traditional manual data extraction methods
Time frame: 1 year
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
5 secondary outcomes
Time to all-cause mortality
Time frame: up to 8 years (from hospital discharge until study primary completion date)
Time to incident major cardiovascular diseases
Time frame: up to 8 years (from hospital discharge until study primary completion date)
Time to rehospitalization for cardiovascular reasons
Time frame: up to 8 years (from hospital discharge until study primary completion date)
Time to stroke or systemic embolism
Time frame: up to 8 years (from hospital discharge until study primary completion date)
Time to acute coronary syndrome
Time frame: up to 8 years (from hospital discharge until study primary completion date)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Oct 2026.
- Background: PubMed 29880128Johnson 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.
- Background: PubMed 28545640Krittanawong 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.
- Background: PubMed 30828647Madani 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.
- Background: PubMed 29888035Boag 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.
- Background: PubMed 32592755Hashir 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.
- Background: PubMed 30689812Diller 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.
- Derived from this study: PubMed 37012018Samaras A, Bekiaridou A, Papazoglou AS, Moysidis DV, Tsoumakas G, Bamidis P, Tsigkas G, Lazaros G, Kassimis G, Fragakis N, Vassilikos V, Zarifis I, Tziakas DN, Tsioufis K, Davlouros P, Giannakoulas G; CardioMining Study Group. Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study. BMJ Open. 2023 Apr 3;13(4):e068698. doi: 10.1136/bmjopen-2022-068698.
- Individual participant dataSharing planned: Yes. Study protocol, statistical analysis plan and results will become available through publications. The analytic code will become available in open source communities/repositories
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
9 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Machine Learning, run by AHEPA University Hospital at 9 sites in Greece.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 27 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 9 sites in Greece. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact George Giannakoulas, MD, PhD (2310994830 ext. +30, ggiannakoulas@auth.gr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of artificial intelligence to automatically extract clinical data from patients' medical records compared with traditional manual data extraction methods (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05176769, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Oct 2026; AzTrials last refreshed its copy on 2 Nov 2026.