JMIR MEDICAL INFORMATICS

Lamer et al

Original Paper

Standardized Description of the Feature Extraction Process toTransform Raw Data Into Meaningful Information for Enhancing
Data Reuse: Consensus Study
Antoine Lamer1,2,3, PhD; Mathilde Fruchart1, MSc; Nicolas Paris3, MSc; Benjamin Popoff4, MD; Anaïs Payen1,
PharmD; Thibaut Balcaen5, MD; William Gacquer6, MSc; Guillaume Bouzillé7, MD, PhD; Marc Cuggia7, MD, Prof
Dr; Matthieu Doutreligne8,9, MSc; Emmanuel Chazard1, MD, Prof Dr
1

Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des Pratiques médicales, Lille, France

2

Fédération régionale de recherche en psychiatrie et santé mentale (F2RSM Psy), Hauts-de-France, Saint-André-Lez-Lille, France

3

InterHop, Rennes, France

4

Department of Anaesthesiology and Critical Care, Rouen University Hospital, Rouen, France

5

Medical Information Department, Amiens-Picardy University Hospital, Amiens, France

6

Digital Services Department, Amiens-Picardy University Hospital, Amiens, France

7

Institut national de la santé et de la recherche médicale (INSERM), LTSI-UMR 1099, Univ Rennes, CHU Rennes, Rennes, France

8

Mission Data, Haute Autorité de Santé, Saint-Denis, France

9

SoDa project team, National Institute for Research in Digital Science and Technology (INRIA), Saclay-Île de France, Gif-sur-Yvette, France

Corresponding Author:
Antoine Lamer, PhD
Univ. Lille
CHU Lille
ULR 2694 - METRICS: Évaluation des Technologies de santé et des Pratiques médicales
1 place de Verdun
Lille, 59000
FrancePhone: 33 320626969
Email: antoine.lamer@univ-lille.fr

Abstract
Background: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw datacannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards
with raw data for defining an algorithm.
Objective: The main purpose of this article is to present a standardized description of the steps and transformations required
during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify
how the features could be stored in the schema of a data warehouse.
Methods: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction
and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations,which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation
Medical Outcomes Partnership (OMOP) common data model (CDM).
Results: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8
retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition
and track aggregation) emerged. “Track” is a time-dependent signal or period of interest, defined by a statistical unit, a value,and 2 milestones (a start event and an end event). “Feature” is time-independent high-level information with dimensionality
identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value
or name of the variable. We propose the 2 tables “TRACK” and “FEATURE” to store variables obtained in feature extractionand extend the OMOP CDM.
Conclusions: We propose a standardized description of the feature extraction process. The process combined the 2 steps oftrack definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track
https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 1
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite
number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number ofpossibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.
(JMIR Med Inform 2022;10(10):e38936) doi: 10.2196/38936
KEYWORDS
feature extraction; data reuse; data warehouse; database; algorithm; Observation Medical Outcomes Partnership

Introduction

method of extraction may have substantial effects on the featuresgenerated [28].

The increasing implementation of electronic health records overthe last few decades has made a significant amount of clinicaldata available in electronic format [1,2]. Originally, electronic
health records were designed to collect and deliver data for
health care, administrative, or billing purposes. In addition tothese initial uses, they also offer opportunities for data reusedefined as “nondirect care use of personal health information”[3]. Thus, data reuse provides possibilities for research, qualityof care assessment, activity management, or public healthmanagement [4-10].

Lastly, the heterogeneity of local data models and vocabularies
complicates the pooling of data and the sharing of algorithms,
tools, and results [29-33]. Initiatives have emerged to promote
the reuse of data through “large-scale clinical data sharing andfederation” and the implementation of common data models(CDMs) [34-38]. Observational Health Data Sciences andInformatics (OHDSI) is a community developed from theObservational Medical Outcomes Partnership (OMOP) [39-42].
The OMOP CDM is dedicated to observational studies, medical
product safety surveillance, comparative effectiveness research,and patient-level predictive modeling. In this context, the
OHDSI community shares methods and tools for the use of the
OMOP CDM, which standardizes the structure and vocabularyof observational data. Around 2000 collaborators from 74
countries were involved in the OHDSI community in mid-2022[43]. Analyses could be successfully applied on this model andbe used at different data sites around the world [44,45].

When conducting research, the traditional approach consists ofprospectively and often manually collecting simple and specificdata according to the question addressed by the research
protocol, using a clinical report form [11]. These data correspondto inclusion criteria and variables, that is, outcomes (eg, thelength of stay in hospital or survival), exposures (eg, the taking
of a drug or a surgery procedure), and adjusting variables (eg,age, sex, and patient history). When performing a prospective
study, these data are defined upstream and are then collectedmanually in routine practice with human expertise, one record
at a time, and background is taken into account. If needed,
third-party data sources can be queried or caregiver expertisecan be sought. This approach is expensive and time-consuming,and it generally results in only a limited sample size for a single
use [7,11]. However, the final data set consists of explicit
information that does not need further computation.
In contrast, data reuse builds on data sources already available
at a low cost and offers a large volume of data [7]. Despite themany opportunities data reuse offers, its implementation presents
many difficulties, and primary data cannot be reused directly.
First, data reuse encounters data quality problems that arise
from the manner in which the data were entered or collected[12-16], and it requires a phase of data cleaning to deduplicate,filter, homogenize, or convert raw data [17,18]. Moreover,
information is not always directly available in the source
database and needs to be computed later from raw data whendefining an algorithm [19-23]. This is generally called “datatransformation” [24], “data aggregation” [25,26], or “feature
extraction” [27]. Even if feature extraction often approximately
answers the question, the process is not easy and brings
methodological issues. Indeed, features are extracted from astatic database (already saved and closed) for patients for whom
the care event has already been completed years earlier and fora large number of records. All scenarios must be taken into
consideration so as to avoid having to modify the extracted
records individually and by hand before the analysis. The

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

Beside clinical data tables, which are appropriate for the storageof individual low-level records (ie, procedure_occurrence,
condition_occurrence, and measurement), the OMOP CDM
was extended with 5 tables to store derived elements [46]. In
particular, the EPISODE table stores the abstracted episodes ofcare previously defined [47,48] and allows the extraction ofchemotherapy episodes from drug records in order to compare
anticancer treatment trajectories [49].
Feature extraction methods are poorly described when applied
to compute secondary information from retrospective databases.
They also lack an approach to store features in a persistent way
in a data warehouse. The purpose of this article is to propose astandardized description of the steps and transformations that
could help researchers to implement and document feature
extraction, and improve the reproducibility of retrospective
studies. It also includes identifying how features could be stored
in the schema of a data warehouse implemented with the OMOP
CDM.

Methods
Overview
This study involved the following 3 main steps: (1) thecollection of relevant study cases that applied feature extraction
and were based on the automatic and secondary use of data; (2)the standardized description of the feature extraction process,including the concepts, their characteristics, and the methods
that were common to the study cases; and (3) the proposal of
convenient tables to store features in the OMOP CDM.
JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 2
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Ethics Approval
This study did not require ethics approval as no personal data
were collected and no interventions were implemented.

Collection of Study Cases
We were seeking examples of retrospective observational studies
for which feature extraction operations had to be implemented.
These studies did not need to be conducted for a specific fieldof research, during a defined time period, or using a particular
data model. The prerequisite was to have transformed raw data
into usable information and to be able to describe the process.
We focused on studies performed with structured data and did
not investigate feature extraction from unstructured data suchas text, images, videos, or sound. We contacted researchers
from 7 teams involved in data reuse in France between
September 1, 2021, and December 31, 2021.
We conducted individual interviews and obtained handwritten
notes. The researchers were asked to describe (1) the objectiveof the study, (2) the database they used (ie, claims or clinicaldatabase), (3) the nature of the data and the terminologies, (4)the difficulties they encountered when extracting informationfrom raw data, (5) the features they had to extract to achieve
the objectives of the study, (6) the use they made of the featuresin the study (ie, inclusion criteria, explanatory variables, or
response variables), and (7) the steps that composed the feature
extraction and the parameters that characterized the features.
The inclusion criteria define the characteristics that subjects
must have to be included in a study. They usually include age,
type and stage of a disease, and surgical procedure. The response
variable is the target of a question in the study or experiment.
It is usually survival, length of hospital stay, recovery, orcomplication of a disease. The explanatory variable is thatvariable whose changes might affect the response variable. It
may be exposure to an event or to a treatment.
The studies were carried out on the following 2 types of
databases: claims databases and hospital clinical databases.
These 2 sources are relational databases with a tabular format.
Each table contains only 1 entity (eg, patients, stays, anddiagnoses), and each row corresponds to 1 record. The tables
are linked together by the mechanism of foreign keys, allowing
the identification of all the data of a patient or a stay, whatever
the category. Most of the columns are structured data (ie, 1 typeand 1 value per cell). These databases are usually queried using
the SQL language. They can then be processed with
programming languages, such as R and Python, to recalculate
new essential information or to adapt the structure of the data
to be able to analyze them more easily.
The claims databases were the French national hospitaldischarge database, referred to as Programme de médicalisation
des systèmes d'information (PMSI) [50], and the French national
claims database, referred to as Système National des Donnéesde Santé (SNDS) [51]. These nationwide databases collectstandardized discharge reports for all inpatient stays in French
nonprofit or for-profit hospitals. They include individual-level
data about the dates of admission and discharge, the hospitalcode number, the sector code and outcome (ie, discharge,
hospital transfer, and death), social demographics (ie, gender,
https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

Lamer et alage, and place of residence), diagnoses, and medical procedures
performed during the hospital stay. The diagnoses are coded
according to the French version of the International Statistical
Classification of Diseases and Related Health Problems, 10th
Revision (ICD10). The medical procedures are documented
according to the Classification Commune des Actes Médicaux(CCAM). In addition to these data, the SNDS database includes
consumption of care outside the hospital (ie, pharmacy visits,
general medical reimbursements, and nursing care). Prescribed
medications are documented with the Anatomical Therapeutic
Chemical (ATC) system, an international classification system,or with the Code Identifiant de la Présentation (CIP13).
The clinical databases were local hospital data warehouses
collecting all information about laboratory results, medical
procedures, diagnoses, and types of medical units and transfers
between them. Two databases included the details of anesthesia
procedures (ie, the steps of the surgical procedures, drug
administrations, and signals recorded by the equipment in theoperating room, eg, mean arterial pressure, heart rate, and tidal
volume) [52]. In these databases, vocabularies are local
terminologies developed by the software editor and updated by
the physician during practice. They cover drugs, measurements,and steps of the surgical procedure. The last database was the
Medical Information Mart for Intensive Care III database, alarge open-source medical record database of critical care stays,
publicly available in PhysioNet [53,54]. Diagnoses are
documented with the International Statistical Classification of
Diseases and Related Health Problems, 9th Revision (ICD9),and the procedures are documented with the Current Procedural
Terminology.

Standardized Description of the Feature Extraction
In the second step, we performed a hierarchical analysis of thetask (HAT) [55]. A HAT allows an understanding of the tasks
that users need to accomplish in order to achieve certain goals.
These tasks may be decomposed into several levels of subtasks,
up to having atomic operations. In this study, we carried out a
HAT to (1) understand the steps and transformations that the
researchers had to implement to transform raw data into featuresand (2) identify the successive states of data, from raw data tofeatures, describing the complexity and time dependency.
To do so, we asked them to describe the raw data they had at
the beginning, and which were the different transformations
they had to chain to obtain features. At each step, we described
the complexity and time dependency. We have illustrated thesuccession of subtasks for each case study, in collaboration withthe researcher involved in the study. From the obtained task
descriptions and illustrations, we grouped the tasks according
to the types of input and output data. Lastly, we propose adescription of these different states and transformations, basedon what was common to the study cases.

Evaluation of Feature Storage Possibilities in the
OMOP CDM
In the last part, we studied the existing tables of the OMOPCDM that could allow the storage of features without losing
information, that is, with adequate fields. In the reverse case,
we would propose new tables to conform to the OMOP standard.
JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 3
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
We would also define the attributes that would have to respect
the OMOP standard and keep track of how features werecomputed to ensure the reproducibility of the studies.

Results
Collection of Study Cases
Among the 15 people we contacted, 3 did not answer and 2
reported not performing feature extraction. Based on thesemistructured interviews, we collected 8 retrospective andobservational studies from teams in 3 French university hospitals(Amiens, Lille, and Rouen) and the French high authority ofhealth. Two of the studies were multisite studies, 4 used claims
databases, and 5 used clinical databases.
The features identified represented different types of variables
used for conducting retrospective analyses: inclusion criteria,
explanatory variables, and response variables. Generic features
were (1) occurrences of diagnoses, medical procedures, and age
as inclusion criteria; (2) occurrences of medical procedures,
occurrences of drug administrations, and transformations ofvital signs as explanatory variables; and (3) hospital andintensive care mortality, hospital stay duration, and passage in
intensive care as response variables. The study cases and themore complex features reported by the researchers are describedin Table 1.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

Lamer et al
These various study cases were based on complex (ie,heterogeneous,
multidimensional,
unbalanced,
andtime-dependent) raw data. The heterogeneity of these raw data
comes from the diversity of the variables involved to extract
secondary computed features. The first 5 study cases (SC1-5)
used measurements and transformed vital signs (arterial pressureand heart rate) or ventilatory signals (partial pressure of oxygen
and tidal volume), SC6 and SC7 used drug administrations, andSC7 used laboratory results. In addition to their heterogeneity,
the databases are multidimensional, which implies that the tables
that compose them have different dimensions (ie, statisticalunits). Thus, each patient will have a different number of records
in the other tables (procedures, diagnoses, measurements, drugs,etc), depending on the length of hospital stay, the care received,
and the duration of follow-up. This number of different records
from one patient to the other should however be reduced to one
line per statistical unit of the study. Next, the modalities ofvariables are numerous and unbalanced, that is, eachterminology has thousands of codes, some of which are widelyused, while others are almost never needed. As a result, at thetime of feature extraction, these thousands of codes generate as
many columns, with, for example, features reporting the code
as absent/present or the number, or reporting the number oftimes it has been documented. At last, raw data aretime-dependent variables, that is, variables that are notnecessarily constant over the course of the study.

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 4
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

Table 1. Description of study cases involving feature extraction for retrospective observational studies.
Study caseObjective of the study

Features needed to achieve the objectives of the study

SC1: Detection of hyperox- To evaluate the effect of hyperoxemia on ICUa mortality, during Explanatory variable: Weighted average of PaO b for2emia in mechanically ven- the first 24 h of ICU stay, in mechanically ventilated patientsmechanically ventilated patients with septic shock actilated patientswith septic shock according to the SEPSIS-3 criteria [56]cording to the SEPSIS-3 criteria. The measurements
are recorded at irregular intervals. The signal is reconstructed to give one measurement per second.
SC2: Duration of hypoten- To evaluate the impact of early blood pressure control in heavysion during heavy surgery surgeries on in-hospital mortality and length of stay

Explanatory variable: Duration of arterial pressure
spent with a drop of 10% from the average value,
during the procedure.

SC3: Duration of hypoten- To characterize the effect of hypotension during cesarean section Explanatory variable: Duration of systolic arterialsion during cesarean sec- with spinal anesthesia on fetal painpressure with a drop of 20% from a reference value
tion with spinal anesthesia
between induction and birth for a cesarean section withspinal anesthesia. The reference value is the meanvalue of the systolic arterial pressure between arrivalin the operating room and the induction.
SC4: Heart rate and admin- To assess the evolution of heart rate before and after the administration of atropine
istration of atropine (a medication used to treat bradycardia)

Explanatory variables: The median, minimum, andmaximum values of heart rate are computed during 2
periods of 10 minutes, designed around the administration of atropine.

SC5: Compliance withventilatory guidelines

To evaluate whether the recommendations in terms of ventilation Explanatory variable: End-tidal volume <8 mL/kg ofin the operating room have been carried out [57]ideal body weight during surgery.

SC6: Potentially inappropriate medications

To measure the impact of a therapeutic optimization intervention Explanatory variable: Number of drug administrations
included in an integrated care pathway on PIMc prevalence and from the French Laroche list [58] (potentially inappropriate medications) in the 90 days preceding the hospion hospital readmission in frail older people
talization.
Number of drug administrations from the French
Laroche list in the 90 days following the hospitalization.

SC7: Drug-drug interactions

To estimate the probability of the occurrence of INRd changeseffor each DDI rule involving VKA [59]Explanatory variable: Administration of VKA withanother drug defined in a DDI rule. Raw ATCg codes
are mapped to wider categories by taking into accountthe active substances and the administration route. The
period of interest started the day after the 2 drugs had
been administered together and ended 4 days after the
first of the 2 drugs was discontinued.
Response variable: VKA potentiation with at least one
value of INR ≥5 or VKA inhibition with at least one
value of INR ≤1.5.

SC8: Compliance withhguidelines for COPD patientsaTo assess the percentage of suspect COPD patients having func- Explanatory variable: Suspect COPD patients definedtional respiratory exploration for diagnosis
as patients aged more than 40 years with one of severalof the following treatments: bronchodilators, 3 antibiotic therapies for respiratory infection, or nicotinic
substitutes.

ICU: intensive care unit.

b

PaO2: partial pressure of oxygen.

c

PIM: potentially inappropriate medication.

d

INR: international normalized ratio.

e

DDI: drug-drug interactions.

f

VKA: vitamin K antagonist.

g

ATC: Anatomical Therapeutic Chemical.

h

COPD: chronic obstructive pulmonary disease.

Standardized Description of the States andTransformations Related to Feature Extraction
Figure 1 provides the complete description of SC6. First, raw
records of administrative data were transformed into a new typeof record corresponding to the occurrence of hospital stay (stephttps://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

1). We will refer to this period as “track” in the rest of themanuscript. Then, this track was transformed to obtain a second
track representing the 90 days before hospital stay (90_days)(step 2). Drug administrations included in the Laroche list wereidentified from raw records, and the periods of administrationof drug A and drug B were computed based on the dates of
JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 5
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
administration and the duration of treatment, in steps 3 and 4,
respectively. Similar tracks were computed for all drugs includedin the Laroche list, but for the clarity of the figure, we havechosen to illustrate only the first 2 drugs. After these 4 steps,
comparisons between tracks were realized successively. This
allowed comparisons of the tracks of administration of drug A
and drug B to track 90_days, in steps 5 and 6, respectively. The
results were joined in a common track to obtain the tracks of
the administration of Laroche list items during track 90_days

Lamer et al(step 7). Lastly, the number of distinct items was counted toobtain the final feature, that is, the number of drugs from theLaroche list administered in the 90 days preceding the hospital
stay.
Table 2 summarizes these transformations, as well as the input
and output data of each transformation. Standardized
descriptions of all other study cases and feature extraction
processes are available in Multimedia Appendix 1 andMultimedia Appendix 2.

Figure 1. Standardized description of study case 6.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 6
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

Table 2. Input data, transformations, and output data for each step involved in the feature extraction of study case 6 (potentially inappropriate medications).
Step

Input data

Transformation

Output data

1

Raw data: Hospital stay

Selection of fields “admission date” and“discharge date”

Track: Hospital stay2

Track: Hospital stay

Computing the previous 90 days

Track: 90 days before hospital stay3

Raw data: Drug administration

Selection of drugs included in the Laroche
list

Track: Drug A

4

Raw data: Drug administration

Selection of drugs included in the Laroche
list

Track: Drug B

5

Track: 90 days before hospital stay + Track:
Drug A

Intersection of the 2 tracks

Track: Drug A (Laroche)/90 days before hospital stay6

Track: 90 days before hospital stay + Track:
Drug B

Intersection of the 2 tracks

Track: Drug B (Laroche)/90 days before hospital stay7

Track: Drug A (Laroche)/90 days before hospital stay + Track: Drug B (Laroche)/90 days
before hospital stay

Union of the 2 tracks

Track: Drug Laroche list/90 days before hospital stay8

Track: Laroche list/90 days before hospital stay Count distinct (drug Laroche list/90 days be- Feature: Number of drugs from the Laroche
fore hospital stay)list prescribed in the 90 days before hospitalstayStates and Transformations
Based on the study cases and the HAT, we identified that data
went through 2 states (track and feature) and benefited from 2
transformations (track definition and track aggregation). Table
3 summarizes the differences between the raw data, track, andfeature, as well as the definitions of the 2 transformations. The
whole process of feature extraction is illustrated for several
types of raw data in Figure 2, and is fully described below.
The step of track definition aims at reducing the dimensions ofraw data to the statistical unit of the study, which is the elementof the population on which the statistical study is conducted.
The statistical unit may refer to not only a patient, but also ahospital, hospital stay (SC6), specialized unit stay (SC1), or aprocedure (SC2, SC3, SC4, and SC5), depending on the purposeof the study. During track definition, the data may be rebuilt orcomputed based on operations such as the selection of variables
and values, the mappings between codes of terminologies (SC6and SC7), the detection of the passage of values beyond athreshold (SC2 and SC3), or the application of any other expert
rule (SC5, SC6, and SC7).
Track is an intermediate state between raw data and features.
It results from the first operation and remains a time-dependent
signal, defined by a statistical unit, a type of track, a value, ora set of values. The type of track may be the passage in a careunit, the administration of a drug, a health condition
characterized by a diagnosis, or a heart rate signal. The value
represents the track state, with a binary value for an on/off state
or a quantitative value for a signal. Conditional operations may
also be applied between tracks to generate new ones (eg, fordetecting the simultaneous administration of 2 drugs). Basedon this definition, Table 4 presents the tracks for the 8 study
cases.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

The step of track aggregation extracts final information fromtracks during a specified period of interest. The extraction
method reduces the multidimensionality and releases from thedependence on time. These methods are usual statistical
functions (eg, minimum, maximum, mean, median, count,duration, and delay).
The period of interest is defined by a start date and an end date,
which may come from different sources as follows: theadministration of a drug, the step of a procedure, the visit witha health care professional, or the visit to a health care unit. For
each date, there could be more than one candidate event. For
example, in SC3, the start of the anesthesia procedure may be
documented with 4 different events as follows: induction event,hypnotic administration, intubation, and mechanical ventilation.
In the same way, the end of the anesthesia procedure may be
defined by the following 2 events: extubation or the end of theanesthesia event. In this case, a priority rule based on expert
knowledge or an aggregation operation (first or last event)selects the main event. Lastly, a time interval may be added tothe start and end dates of the period to create an artificial period
as follows: the 90 days preceding or following hospitalization(SC6).
At the end of the process, feature is a single value associatedwith a label (the feature name). In a feature, time is implicit andis no longer formalized by a date in the record. It may besometimes represented in the name of the variable, with, forexample, the mean value of arterial pressure before induction(eg, mean_map_before_induction). It may also be represented
in the value of the feature itself (eg, for a delay or a duration).
The feature depends greatly on the context of the study; thus,
in SC2 and SC3, the same raw signal produces 2 distinct featuresthat depend on the extraction methods and the periods of interest.
Table 5 describes the features identified in our 8 study cases,
according to the statistical unit, period, signal, and extraction
method.

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 7
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

Table 3. Definitions and comparisons of the states and transformations involved in the feature extraction.
States and transformations Description
Raw data (state)

ExampleTime dimension Complexity

Heterogeneous, multidimensional, and time-dependent
Raw measurements of
low-level clinical data: demographic data, patient flow,mean arterial pressurelaboratory results, drug administrations, procedures, diagnoses, and measurements.

Yes

Yes

Yes

Reduced

The time dimension is always beside the value as an attribute.
Track definition (transfor- Reduction of the initial dimensions to the statistical unit
mation)and standardization of the data representation through an
infinite possibility of operations with high expert knowledge.

Resampling of the signal

Conservation of the time dimension.
Conditional operations may be performed on tracks togenerate new tracks.
Track (state)

Homogeneous and time-dependent signal, defined by ahomogeneous statistical unit, a type of track, and a set oftime-stamped values.

Resampled signal with one Yes
measurement per second

No

Aggregation (minimum
and mean values) of measurements recorded between the start and end of
the anesthesia procedure

ReducedNo

Time-independent high-level information with dimension- Minimum and mean values Implicit
ality identical to the statistical unit of the study, defined by of mean arterial pressurea label and a value.
during the anesthesia proThe time dimension has become implicit in the value (eg, cedureNo

The time dimension remains beside each track.
Track aggregation (transformation)

Reduction of the time dimension: a period of interest, a
track, and an extraction method based on a finished numberof operations (minimum, maximum, median, sum, count,etc).
The time dimension is reduced to obtain a single value,with time embedded in the variable name or inside thevalue.

Feature (state)in a delay or a duration) or name of the variable (eg, a value
at day 1).

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 8
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

Figure 2. Feature extraction process transforming raw data into features.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 9
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

Table 4. Definition of tracks used in the study cases.
Study case and statistical unit

Track

Value(s)SC1: Hyperoxemia in mechanically ventilated patients
ICUa stayFirst 24 hours of ICU stay for mechanically ventilated patients ICU stay=1
with septic shock

ICU stay

Resampled PaO2b

PaO2 repeated measurements

SC2: Duration of hypotension during general anesthesia
Heavy surgery

General anesthesia procedure

General anesthesia procedure=1

Heavy surgery

Average value of mean arterial pressure

Average value

Heavy surgery

Episode of mean arterial pressure below 90% of the averagevalue

Episode=1

SC3: Duration of hypotension during cesarean section with spinal anesthesia
Cesarean section with spinal Arrival in the operating room to induction of anesthesia
anesthesia

Reference period=1

Cesarean section with spinal Induction of anesthesia to birthanesthesia

Spinal anesthesia=1

Cesarean section with spinal Average value of the systolic arterial pressure between arrival Average value
anesthesia
in the operating room and induction of anesthesia
Cesarean section with spinal Episode of systolic arterial pressure below 80% of the average Episode=1
anesthesia
value
SC4: Heart rate and administration of atropine
Administration of atropine

Before administration of atropine

Before=1

Administration of atropine

After administration of atropine

After=1

SC5: Compliance with ventilatory guidelines
Anesthesia procedure withmechanical ventilation

Surgery

Surgery=1

SC6: Potentially inappropriate medications
Hospital stay

Before hospital stay

Before hospital stay=1

Hospital stay

After hospital stay

After hospital stay=1

Hospital stay

Administration of drug X from the Laroche list

Drug X=1

Patient

Administration of drug X (raw code)

Drug X=1

Patient

Administration of a drug family (ATCc category)ATC category=1

Patient

Concomitant administration of a VKAd with a drug definedConcomitant administration=1

SC7: Drug-drug interactionsein a DDI rulePatient

INRf ≥5

Episode of INR ≥5

Patient

INR ≤1.5

Episode of INR ≤1.5

Patient

Concomitant administration of a VKA with a drug defined in VKA potentiation=1
a DDI rule and INR ≥5

Patient

Concomitant administration of a VKA with a drug defined in VKA inhibition=1
a DDI rule and INR ≤1.5

SC8: Compliance with guidelines for COPD patients
Patient

Administration of one of several drugs among bronchodilators Drug X ≥1
or nicotinic substitutes (ATC codes)

Patient

Administration of 3 antibiotic therapies for respiratory infec- Drug X ≥3tion (ATC codes)

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 10(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
Study case and statistical unit
Patient

Lamer et al

Track

Value(s)Exposure to at least one of the drugs specific to suspected

Exposure to COPD-specific drugs=1

COPDg
PatientaInduction of spirometry or functional respiratory exploration Episode=1

ICU: intensive care unit.

b

PaO2: partial pressure of oxygen.

c

ATC: Anatomical Therapeutic Chemical.

d

VKA: vitamin K antagonist.

e

DDI: drug-drug interaction.

f

INR: international normalized ratio.

g

COPD: chronic obstructive pulmonary disease.

Table 5. Definitions of the characteristics for each feature of the study cases.
Study case

Statistical unit

PeriodTrack

Extraction method

SC1: Hyperoxemia in mechanically ventilated patients

ICUa stayFirst 24 hours of ICU stayfor mechanically ventilated
patients with septic shock

Resampled PaO2b

Weighted average

SC2: Hypotension during
anesthesia

General anesthesia procedure

Anesthesia period

Mean arterial pressure

Sum of the duration of
episodes of mean arterialpressure with a drop of 10%
from the reference value

Anesthesia period

Systolic arterial pressure

Total duration of systolic
arterial pressure below 80%of the reference value

SC3: Duration of hypoten- Cesarean section withsion during cesarean section spinal anesthesia
with spinal anesthesia

SC4 :Heart rate and adminis- Administration of atropine Periods of 10 minutes before Heart rate
tration of atropine
and after the administrationof atropine

Median, minimum, andmaximum values of heartrate

SC5: Compliance with ven- Anesthesia procedure with Surgery period
tilatory guidelines
mechanical ventilation

End-tidal volume

Mean end-tidal/ideal bodyweight >8

SC6: Potentially inappropri- Hospital visit
ate medications

Before hospital stay; after
hospital stay

Administration of medications Count of inappropriate drug
administration according to
the French Laroche list.

SC7: Drug-drug interactions Patient

Day after the 2 drugs havebeen administered together
and until 4 days after the
first of the 2 drugs was discontinued.

Concomitant administration

Count of VKA potentiation.

of a VKAc with a drug de-Count of VKA inhibition.

fined in a DDId rule and INRe
≥5.
Concomitant administrationof a VKA with a drug definedin a DDI rule and INR ≤1.5.

SC8: Compliance with

Patientf

guidelines for COPD patientsaYear following exposure to
one of the drugs specific to
COPD

Administration of medications Count of the administrationof drugs specific to COPD
Binary indicator of FREg
induction

ICU: intensive care unit.

b

PaO2: partial pressure of oxygen.

c

VKA: vitamin K antagonist.

d

DDI: drug-drug interaction.

e

INR: international normalized ratio.

f

COPD: chronic obstructive pulmonary disease.

g

FRE: functional respiratory exploration.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 11(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Evaluation of Feature Storage Possibilities in the
OMOP CDM
Five tables already exist in the OMOP CDM (DRUG_ERA,
DOSE_ERA,CONDITION_ERA,EPISODE,andEPISODE_EVENT) for storing elements derived from raw data[46]. These tables cover the storage of spans of time when thepatient is exposed to a specific drug ingredient (DRUG_ERA),
when the patient is exposed to a constant dose of a specific drug
ingredient (DOSE_ERA), or when the patient is assumed tohave a given condition (CONDITION_ERA). These existingtables are suitable for pharmacoepidemiology studies with thecomparison of periods of drug exposure and the resulting
adverse events or evolution of the disease. The studies require
only diagnosis and medication data from the tables
CONDITION_OCCURRENCE and DRUG_EXPOSURE [39].
However, other types of data also need to be retransformed toobtain usable information for statistical analysis (in particular,
procedures, measurements, biology results, or any types of steps
in patient care). At this point, 2 alternatives allow other typesof derived elements to be stored. The first approach involves
adding an era table for each raw information that can betransformed into an era (ie, a measurement era, procedure era,
biology era, etc). The second approach involves proposing ageneric era table that would cover all types of raw data. With
these 2 approaches, there would still be a lack of storage for the
final features, which do not have the same structure as eras or
episodes, since they are only an association of a value and alabel, independent of time.

Lamer et al
For this reason, on the one hand, the table TRACK could
complement the model and store intermediate data (ie, all typesof tracks and eras), which would ultimately be used to computefeatures, and on the other hand, the table FEATURE could
extend the OMOP CDM for storing secondary computed datafrom measurements, procedures, observations, and stays, which
would be used for the analysis and would need to be stored ona long-term basis.
These 2 new conceptual tables are illustrated in Figure 3. They
comply with the OMOP guidelines in terms of field name andtable organization [60]. For both tables, foreign keys reference
the person, the visit, the visit details, the main concept(TRACK_CONCEPT_ID and FEATURE_CONCEPT_ID),and the type of this concept (TRACK_TYPE_CONCEPT_ID
and FEATURE_TYPE_CONCEPT_ID). Similarly, the 2 tables
provide core fields to store continuous values(VALUE_AS_NUMBER)
orcategoricalvalues(VALUE_AS_CONCEPT_ID). The specificity of TRACK
involves the preservation of the time dimension through thefields TRACK_START_DATE and TRACK_END_DATE. In
the FEATURE table, in the case where a patient could present
the same feature several times (eg, on different days), a foreign
key to the EPISODE table allows differentiation of theoccurrences of a feature [47]. Both tables also have the usual
fields to store the source values expressed with local
vocabularies.

Figure 3. Data model for the storage of periods and features in a relational database, compliant with the Observational Medical Outcomes Partnership(OMOP) common data model. FK: foreign key; PK: primary key.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 12(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Discussion
Principal Findings
In this article, we propose a standardized description of thefeature extraction process, which is implemented whentransforming
heterogeneous,
multidimensional,
andtime-dependent raw data into valuable information for
conducting observational retrospective studies. The process
combines 2 steps (track definition and track aggregation). Trackdefinition aims at transforming raw data into multiple tracks
representing the periods of interest or reconstructing a signal.
Track aggregation computes usable information from a final
track for applying an extraction method during a period ofinterest. The resulting features are the 1-dimensional andtime-independent variables that will be included in the statisticalanalysis.
By dividing the feature extraction into these 2 steps, thedifficulty is managed during track definition. The first step aims
at creating tracks, with a common unit adequate for the statisticalunit of the study and a homogeneous temporal scale. Tracks
then allow the application of an infinite number of complex
transformations, such as the mapping of concepts for thedetection of drug-drug interactions (SC7). These transformations
require great expertise with regard to the data and are mainly
implemented on a custom basis. On the contrary, track
aggregation is a very simple operation, with a finite number ofpossibilities.

Strengths of the Study
The definitions of the transformations are based on various
cases, and they were carried out on different databases fromseveral centers. Feature extraction is the algorithmic translationof expert knowledge. Our work shows that this process requires
the sequencing of several transformations, including, for track
definition, the choice of (1) a time-dependent signal or an
already available track, (2) a statistical unit, (3) a type of track,and (4) a value or a set of values, and track aggregation is the
final transformation based on (5) a track, which is performed
during (6) a period of interest and involves (7) an extraction
method. The formalization and documentation of these 7 items
should enhance the reproducibility of studies and the sharingof features between collaborators, by removing the ambiguity
about what is being calculated.

LimitsIn this study, we focused on feature extraction based on expert
rules and did not take into account feature extraction based on
deep learning techniques [61,62]. In this case, although the aim
is also to reduce the dimensionality of the source data, there isno need to interpret features, which are often abstract anddesigned to result in the best prediction model without being
interpreted [62]. Recent advances in natural language processing[63-65] could be leveraged to automatize the extraction of
relevant clinical features from clinical text [66]. Once the featureof interest has been well defined, a small annotation campaign
should be conducted to fine-tune and evaluate pretrained model
performances. Afterwards, the extracted feature can be
integrated in our workflow as a new structured piece ofhttps://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

Lamer et al
information. The impressive results of large language models
suggest that a few labeled examples are sufficient to fine-tune
these models [67]. Three limitations must be explored before
using these models. First, due to the variability of the wordingsof clinical concepts, it has not been proved that a large language
model can capture every targeted feature. Second, the computing
intensiveness is incompatible with large-scale information
retrieval. Third, the ability to conduct quick targeted annotation
campaigns for precise clinical terms requires appropriate toolingand processes. We have not provided any use cases involving
text. However, both tracks and features could be constructedfrom, for example, the presence of a symptom or the reportingof a scale in a consultation report. Such extraction from raw
text raises the question of the automatic detection of specificconcepts in text and the performance of the tools used for this.
Although some features, such as the length of stay, are genericand frequently used, the majority remain dependent on the study
context. The period of interest and the extraction method areproxies for what is expected by the clinician or researcher, andthe feature would need to be manually evaluated to ensure its
validity [49].
Even if SNOMED CT (Systematized Nomenclature of Medicine- Clinical Terms) and ICD10 propose aggregate concepts, suchas “Hypotension following procedure” (SNOMED CT code
16055431000119108), “Decreased mean arterial pressure”(SNOMED CT code 31013001), or “Hypotension” (ICD10 codeI95), these concepts are only a part of the label of a feature, andthey do not document how to compute the feature or mention
the period (ie, surgery, anesthesia, intensive care unit stay, or
first day of hospitalization). Standardized concepts that fully
document features are yet to be defined in these terminologies.
At present, we cannot judge the generalization of our proposal.
However, this study is the first to propose a standardized
description of feature extraction from structured databases. The
approach remains to be evaluated by comparing it with otherstudy cases, particularly from other countries.
The next step of this project is the implementation of an R
package with functions dedicated to the definition andaggregation of tracks. This package would rely on the OMOPCDM and allow reproducibility of feature extraction. Attention
will need to be paid to the physical implementation of the 2tables and, in particular, to the storage of tracks, which can bevoluminous and can impact performance with regard to queries
and response times. Finally, it would be relevant to implement
a data mart with features arranged in columns (when they arestill stored in rows in the feature table) to gain time whenbuilding tables to construct cohorts.

Conclusions
We have clarified the process of feature extraction implemented
when conducting retrospective observational studies. We
identified 2 transformations (track definition and track
aggregation) to transform complex raw data into tracks andfeatures. Track definition requires high expertise, but reduces
the complexity of data and simplifies the reduction of time
dimensionality during track aggregation.

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 13(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Lamer et al

Authors' Contributions
AL, MF, and EC contributed to study conception and design, and drafted the manuscript. All authors provided their study cases
and approved the manuscript.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Description of the study cases.
[DOCX File , 15 KB-Multimedia Appendix 1]

Multimedia Appendix 2Standardized description of the tracks and features implemented for each study case.
[PDF File (Adobe PDF File), 100 KB-Multimedia Appendix 2]

References
1.
2.

3.

4.
5.

6.

7.
8.

9.

10.
11.
12.

13.
14.
15.
16.

Weng C, Kahn MG. Clinical Research Informatics for Big Data and Precision Medicine. Yearb Med Inform 2016 Nov
10(1):211-218 [FREE Full text] [doi: 10.15265/IY-2016-019] [Medline: 27830253]
Adler-Milstein J, DesRoches CM, Kralovec P, Foster G, Worzala C, Charles D, et al. Electronic Health Record Adoption
In US Hospitals: Progress Continues, But Challenges Persist. Health Aff (Millwood) 2015 Dec;34(12):2174-2180. [doi:10.1377/hlthaff.2015.0992] [Medline: 26561387]
Safran C, Bloomrosen M, Hammond WE, Labkoff S, Markel-Fox S, Tang PC, Expert Panel. Toward a national framework
for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc
2007;14(1):1-9 [FREE Full text] [doi: 10.1197/jamia.M2273] [Medline: 17077452]
Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013 Jan
01;20(1):117-121 [FREE Full text] [doi: 10.1136/amiajnl-2012-001145] [Medline: 22955496]
Forrest CB, Margolis PA, Bailey LC, Marsolo K, Del Beccaro MA, Finkelstein JA, et al. PEDSnet: a National PediatricLearning Health System. J Am Med Inform Assoc 2014;21(4):602-606 [FREE Full text] [doi: 10.1136/amiajnl-2014-002743][Medline: 24821737]
McGlynn EA, Lieu TA, Durham ML, Bauck A, Laws R, Go AS, et al. Developing a data infrastructure for a learning healthsystem: the PORTAL network. J Am Med Inform Assoc 2014;21(4):596-601 [FREE Full text] [doi:10.1136/amiajnl-2014-002746] [Medline: 24821738]
Safran C. Reuse of clinical data. Yearb Med Inform 2014 Aug 15;9:52-54 [FREE Full text] [doi: 10.15265/IY-2014-0013][Medline: 25123722]
De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B, et al. Using electronic health records for clinicalresearch: the case of the EHR4CR project. J Biomed Inform 2015 Feb;53:162-173 [FREE Full text] [doi:10.1016/j.jbi.2014.10.006] [Medline: 25463966]
Lin K, Schneeweiss S. Considerations for the analysis of longitudinal electronic health records linked to claims data to
study the effectiveness and safety of drugs. Clin Pharmacol Ther 2016 Aug 12;100(2):147-159. [doi: 10.1002/cpt.359][Medline: 26916672]
Meystre SM, Lovis C, Bürkle T, Tognola G, Budrionis A, Lehmann CU. Clinical Data Reuse or Secondary Use: Current
Status and Potential Future Progress. Yearb Med Inform 2017 Sep 11;26(01):38-52. [doi: 10.15265/iy-2017-007]
Krishnankutty B, Bellary S, Kumar NBR, Moodahadu LS. Data management in clinical research: An overview. Indian J
Pharmacol 2012 Mar;44(2):168-172 [FREE Full text] [doi: 10.4103/0253-7613.93842] [Medline: 22529469]
Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse forclinical research. J Am Med Inform Assoc 2013 Jan 01;20(1):144-151 [FREE Full text] [doi: 10.1136/amiajnl-2011-000681][Medline: 22733976]
Safran C. Medicine based upon data. J Gen Intern Med 2013 Dec;28(12):1545-1546 [FREE Full text] [doi:10.1007/s11606-013-2549-3] [Medline: 23838902]
Weiskopf NG, Bakken S, Hripcsak G, Weng C. A Data Quality Assessment Guideline for Electronic Health Record Data
Reuse. EGEMS (Wash DC) 2017 Sep 04;5(1):14 [FREE Full text] [doi: 10.5334/egems.218] [Medline: 29881734]
Wendl C, Duftschmid G, Gezgin D, Popper N, Miksch F, Rinner C. A Web-Based Tool to Evaluate Data Quality of Reused
Health Data Assets. Stud Health Technol Inform 2017;236:204-210. [Medline: 28508797]
Wang Z, Dagtas S, Talburt J, Baghal A, Zozus M. Rule-Based Data Quality Assessment and Monitoring System in Healthcare
Facilities. Stud Health Technol Inform 2019;257:460-467. [Medline: 30741240]

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 14(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
17.
18.
19.

20.

21.

22.

23.

24.
25.
26.

27.

28.

29.

30.
31.

32.

33.

34.

35.

36.
37.

Rahm E, Do HH. Data Cleaning: Problems and Current Approaches. Better Evaluation. URL: https://www.
betterevaluation.org/sites/default/files/data_cleaning.pdf [accessed 2022-09-24]
Weng C. Clinical data quality: a data life cycle perspective. Biostat Epidemiol 2020;4(1):6-14. [doi:10.1080/24709360.2019.1572344] [Medline: 32258941]
Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, et al. Automated identification of postoperativecomplications within an electronic medical record using natural language processing. JAMA 2011 Aug 24;306(8):848-855.
[doi: 10.1001/jama.2011.1204] [Medline: 21862746]
Smischney NJ, Velagapudi VM, Onigkeit JA, Pickering BW, Herasevich V, Kashyap R. Derivation and validation of asearch algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit. BMC Med Inform
Decis Mak 2014 Jun 25;14:55 [FREE Full text] [doi: 10.1186/1472-6947-14-55] [Medline: 24965680]
Tien M, Kashyap R, Wilson GA, Hernandez-Torres V, Jacob AK, Schroeder DR, et al. Retrospective Derivation andValidation of an Automated Electronic Search Algorithm to Identify Post Operative Cardiovascular and Thromboembolic
Complications. Appl Clin Inform 2015;6(3):565-576 [FREE Full text] [doi: 10.4338/ACI-2015-03-RA-0026] [Medline:
26448798]
Lamer A, Jeanne M, Marcilly R, Kipnis E, Schiro J, Logier R, et al. Methodology to automatically detect abnormal valuesof vital parameters in anesthesia time-series: Proposal for an adaptable algorithm. Comput Methods Programs Biomed 2016
Jun;129:160-171. [doi: 10.1016/j.cmpb.2016.01.004] [Medline: 26817405]
Gabel E, Hofer IS, Satou N, Grogan T, Shemin R, Mahajan A, et al. Creation and Validation of an Automated Algorithm
to Determine Postoperative Ventilator Requirements After Cardiac Surgery. Anesth Analg 2017 May;124(5):1423-1430.
[doi: 10.1213/ANE.0000000000001997] [Medline: 28431419]
Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Magazine
1996;17(3):37-54 [FREE Full text] [doi: 10.1609/aimag.v17i3.1230]
Lamer A, Jeanne M, Ficheur G, Marcilly R. Automated Data Aggregation for Time-Series Analysis: Study Case on
Anaesthesia Data Warehouse. Stud Health Technol Inform 2016;221:102-106. [Medline: 27071886]
Price A, Caciula A, Guo C, Lee B, Morrison J, Rasmussen A, et al. DEvis: an R package for aggregation and visualizationof differential expression data. BMC Bioinformatics 2019 Mar 04;20(1):110 [FREE Full text] [doi:10.1186/s12859-019-2702-z] [Medline: 30832568]
Chazard E, Ficheur G, Caron A, Lamer A, Labreuche J, Cuggia M, et al. Secondary Use of Healthcare Structured Data:
The Challenge of Domain-Knowledge Based Extraction of Features. Stud Health Technol Inform 2018;255:15-19. [Medline:
30306898]
Pasma W, Peelen LM, van Buuren S, van Klei WA, de Graaff JC. Artifact Processing Methods Influence on Intraoperative
Hypotension Quantification and Outcome Effect Estimates. Anesthesiology 2020 Apr;132(4):723-737 [FREE Full text][doi: 10.1097/ALN.0000000000003131] [Medline: 32022770]
Breil B, Kenneweg J, Fritz F, Bruland P, Doods D, Trinczek B, et al. Multilingual Medical Data Models in ODM Format:
A Novel Form-based Approach to Semantic Interoperability between Routine Healthcare and Clinical Research. Appl Clin
Inform 2012;3(3):276-289 [FREE Full text] [doi: 10.4338/ACI-2012-03-RA-0011] [Medline: 23620720]
Pivovarov R, Albers DJ, Sepulveda JL, Elhadad N. Identifying and mitigating biases in EHR laboratory tests. J Biomed
Inform 2014 Oct;51:24-34 [FREE Full text] [doi: 10.1016/j.jbi.2014.03.016] [Medline: 24727481]
Krumm R, Semjonow A, Tio J, Duhme H, Bürkle T, Haier J, et al. The need for harmonized structured documentation andchances of secondary use - results of a systematic analysis with automated form comparison for prostate and breast cancer.
J Biomed Inform 2014 Oct;51:86-99 [FREE Full text] [doi: 10.1016/j.jbi.2014.04.008] [Medline: 24747879]
Dhombres F, Bodenreider O. Interoperability between phenotypes in research and healthcare terminologies--Investigating
partial mappings between HPO and SNOMED CT. J Biomed Semantics 2016;7:3 [FREE Full text] [doi:10.1186/s13326-016-0047-3] [Medline: 26865946]
Dugas M, Neuhaus P, Meidt A, Doods J, Storck M, Bruland P, et al. Portal of medical data models: information infrastructure
for medical research and healthcare. Database (Oxford) 2016;2016:bav121 [FREE Full text] [doi: 10.1093/database/bav121][Medline: 26868052]
Xu Y, Zhou X, Suehs BT, Hartzema AG, Kahn MG, Moride Y, et al. A Comparative Assessment of Observational Medical
Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety
Surveillance. Drug Saf 2015 Aug;38(8):749-765. [doi: 10.1007/s40264-015-0297-5] [Medline: 26055920]
Garza M, Del Fiol G, Tenenbaum J, Walden A, Zozus MN. Evaluating common data models for use with a longitudinal
community registry. J Biomed Inform 2016 Dec;64:333-341 [FREE Full text] [doi: 10.1016/j.jbi.2016.10.016] [Medline:
27989817]
Klann JG, Abend A, Raghavan VA, Mandl KD, Murphy SN. Data interchange using i2b2. J Am Med Inform Assoc 2016
Sep;23(5):909-915 [FREE Full text] [doi: 10.1093/jamia/ocv188] [Medline: 26911824]
Hume S, Aerts J, Sarnikar S, Huser V. Current applications and future directions for the CDISC Operational Data Model
standard: A methodological review. J Biomed Inform 2016 Apr;60:352-362 [FREE Full text] [doi: 10.1016/j.jbi.2016.02.016][Medline: 26944737]

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

Lamer et al

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 15(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
38.

39.

40.

41.

42.
43.
44.

45.

46.

47.

48.

49.

50.
51.

52.
53.

54.
55.
56.

57.

58.
59.

Liyanage H, Liaw S, Jonnagaddala J, Hinton W, de Lusignan S. Common Data Models (CDMs) to Enhance International
Big Data Analytics: A Diabetes Use Case to Compare Three CDMs. Stud Health Technol Inform 2018;255:60-64. [Medline:
30306907]
Stang PE, Ryan PB, Racoosin JA, Overhage JM, Hartzema AG, Reich C, et al. Advancing the science for active surveillance:
rationale and design for the Observational Medical Outcomes Partnership. Ann Intern Med 2010 Nov 02;153(9):600-606.
[doi: 10.7326/0003-4819-153-9-201011020-00010] [Medline: 21041580]
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance
research. J Am Med Inform Assoc 2012;19(1):54-60 [FREE Full text] [doi: 10.1136/amiajnl-2011-000376] [Medline:
22037893]
Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, et al. Observational Health Data Sciences and Informatics(OHDSI): Opportunities for Observational Researchers. Stud Health Technol Inform 2015;216:574-578 [FREE Full text][Medline: 26262116]
Observational Health Data Sciences and Informatics. URL: https://www.ohdsi.org/ [accessed 2019-05-03]
Areas of Focus. Observational Health Data Sciences and Informatics. URL: https://www.ohdsi.org/who-we-are/areas-of-focus/[accessed 2022-07-07]
Suchard MA, Schuemie MJ, Krumholz HM, You SC, Chen R, Pratt N, et al. Comprehensive comparative effectivenessand safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet 2019 Nov
16;394(10211):1816-1826 [FREE Full text] [doi: 10.1016/S0140-6736(19)32317-7] [Medline: 31668726]
Burn E, You SC, Sena AG, Kostka K, Abedtash H, Abrahão MTF, et al. Deep phenotyping of 34,128 adult patients
hospitalised with COVID-19 in an international network study. Nat Commun 2020 Oct 06;11(1):5009 [FREE Full text][doi: 10.1038/s41467-020-18849-z] [Medline: 33024121]
Belenkaya R, Gurley MJ, Golozar A, Dymshyts D, Miller RT, Williams AE, et al. Extending the OMOP Common Data
Model and Standardized Vocabularies to Support Observational Cancer Research. JCO Clin Cancer Inform 2021 Jan;5:12-20[FREE Full text] [doi: 10.1200/CCI.20.00079] [Medline: 33411620]
Conversion of Diagnosis and Chemotherapy Data in Electronic Health Records to Episode-based Oncology Extension of
OMOP-CDM. Observational Health Data Sciences and Informatics. URL: https://www.ohdsi.org/2019-us-symposiumshowcase-12/ [accessed 2021-10-22]
Warner JL, Dymshyts D, Reich CG, Gurley MJ, Hochheiser H, Moldwin ZH, et al. HemOnc: A new standard vocabulary
for chemotherapy regimen representation in the OMOP common data model. J Biomed Inform 2019 Aug;96:103239 [FREEFull text] [doi: 10.1016/j.jbi.2019.103239] [Medline: 31238109]
Jeon H, You SC, Kang SY, Seo SI, Warner JL, Belenkaya R, et al. Characterizing the Anticancer Treatment Trajectoryand Pattern in Patients Receiving Chemotherapy for Cancer Using Harmonized Observational Databases: Retrospective
Study. JMIR Med Inform 2021 Apr 06;9(4):e25035 [FREE Full text] [doi: 10.2196/25035] [Medline: 33720842]
Boudemaghe T, Belhadj I. Data Resource Profile: The French National Uniform Hospital Discharge Data Set Database(PMSI). Int J Epidemiol 2017 Apr 01;46(2):392-392d. [doi: 10.1093/ije/dyw359] [Medline: 28168290]
Scailteux L, Droitcourt C, Balusson F, Nowak E, Kerbrat S, Dupuy A, et al. French administrative health care database(SNDS): The value of its enrichment. Therapie 2019 Apr;74(2):215-223. [doi: 10.1016/j.therap.2018.09.072] [Medline:
30392702]
Lamer A, Jeanne M, Vallet B, Ditilyeu G, Delaby F, Tavernier B, et al. Development of an anesthesia data warehouse:
Preliminary results. IRBM 2013 Dec;34(6):376-378. [doi: 10.1016/j.irbm.2013.09.005]
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet:
components of a new research resource for complex physiologic signals. Circulation 2000 Jun 13;101(23):E215-E220.
[doi: 10.1161/01.cir.101.23.e215] [Medline: 10851218]
Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care
database. Sci Data 2016 May 24;3:160035 [FREE Full text] [doi: 10.1038/sdata.2016.35] [Medline: 27219127]
French A, Taylor LK, Lemke MR. Chapter 6 - Task analysis. In: Privitera MB, editor. Applied Human Factors in Medical
Device Design. Cambridge, MA: Academic Press; 2019:63-81.
Popoff B, Besnier E, Dureuil B, Veber B, Clavier T. Effect of early hyperoxemia on mortality in mechanically ventilated
septic shock patients according to Sepsis-3 criteria: analysis of the MIMIC-III database. Eur J Emerg Med 2021 Dec
01;28(6):469-475. [doi: 10.1097/MEJ.0000000000000854] [Medline: 34285171]
Laurent G, Moussa MD, Cirenei C, Tavernier B, Marcilly R, Lamer A. Development, implementation and preliminary
evaluation of clinical dashboards in a department of anesthesia. J Clin Monit Comput 2021 May;35(3):617-626 [FREE Fulltext] [doi: 10.1007/s10877-020-00522-x] [Medline: 32418147]
Laroche M, Charmes J, Merle L. Potentially inappropriate medications in the elderly: a French consensus panel list. Eur J
Clin Pharmacol 2007 Aug;63(8):725-731. [doi: 10.1007/s00228-007-0324-2] [Medline: 17554532]
Chazard E, Boudry A, Beeler PE, Dalleur O, Hubert H, Tréhou E, et al. Towards The Automated, Empirical Filtering ofDrug-Drug Interaction Alerts in Clinical Decision Support Systems: Historical Cohort Study of Vitamin K Antagonists.
JMIR Med Inform 2021 Jan 20;9(1):e20862 [FREE Full text] [doi: 10.2196/20862] [Medline: 33470938]

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

Lamer et al

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 16(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
60.
61.
62.
63.
64.
65.
66.
67.

Lamer et al

EPISODE. Observational Health Data Sciences and Informatics. URL: https://ohdsi.github.io/CommonDataModel/cdm54.
html#EPISODE [accessed 2021-10-21]
Liang H, Sun X, Sun Y, Gao Y. Text feature extraction based on deep learning: a review. EURASIP J Wirel Commun
Netw 2017;2017(1):211 [FREE Full text] [doi: 10.1186/s13638-017-0993-1] [Medline: 29263717]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015 May 28;521(7553):436-444. [doi: 10.1038/nature14539][Medline: 26017442]
Peters M, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, et al. Deep contextualized word representations. arXiv. 2018.
URL: https://arxiv.org/abs/1802.05365 [accessed 2022-09-24]
Devlin J, Chang M, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding. arXiv. 2019. URL: https://arxiv.org/abs/1810.04805 [accessed 2022-09-24]
Brown T, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language Models are Few-Shot Learners. arXiv.
2020. URL: https://arxiv.org/abs/2005.14165 [accessed 2022-09-24]
Alsentzer E, Murphy J, Boag W, Weng W, Jin D, Naumann T, et al. Publicly Available Clinical BERT Embeddings. arXiv.
2019. URL: https://arxiv.org/abs/1904.03323 [accessed 2022-09-24]
Copara J, Knafou J, Naderi N, Moro C, Ruch P, Teodoro D. Contextualized French Language Models for Biomedical
Named Entity Recognition. HAL Archives. URL: https://hal.archives-ouvertes.fr/hal-02784740 [accessed 2022-07-18]

Abbreviations
ATC: Anatomical Therapeutic Chemical
CDM: common data model
HAT: hierarchical analysis of the task
ICD10: International Statistical Classification of Diseases and Related Health Problems, 10th Revision
OHDSI: Observational Health Data Sciences and Informatics
OMOP: Observational Medical Outcomes Partnership
SNDS: Système National des Données de Santé (French national claims database)
SNOMED CT: Systematized Nomenclature of Medicine - Clinical Terms

Edited by C Lovis, J Hefner; submitted 22.04.22; peer-reviewed by M Sedlmayr, FM Calisto, E Sylvestre; comments to author 13.06.22;
revised version received 19.07.22; accepted 11.08.22; published 17.10.22
Please cite as:
Lamer A, Fruchart M, Paris N, Popoff B, Payen A, Balcaen T, Gacquer W, Bouzillé G, Cuggia M, Doutreligne M, Chazard E
Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data
Reuse: Consensus StudyJMIR Med Inform 2022;10(10):e38936
URL: https://medinform.jmir.org/2022/10/e38936
doi: 10.2196/38936
PMID:©Antoine Lamer, Mathilde Fruchart, Nicolas Paris, Benjamin Popoff, Anaïs Payen, Thibaut Balcaen, William Gacquer, Guillaume
Bouzillé, Marc Cuggia, Matthieu Doutreligne, Emmanuel Chazard. Originally published in JMIR Medical Informatics(https://medinform.jmir.org), 17.10.2022. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete
bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license
information must be included.

https://medinform.jmir.org/2022/10/e38936

XSL• FO
RenderX

JMIR Med Inform 2022 | vol. 10 | iss. 10 | e38936 | p. 17(page number not for citation purposes)