Intelligent Decision Support System for Precision Medicine:

Time Series Multi-variable Approach for Data Processing

Nasim Sadat Mosavi

and Manuel Filipe Santos

Algoritmi Research Centre, University of Minho, Guimaraes, Portugal

Keywords: Intelligent Decision Support, Precision Medicine, Optimization, Data Processing, Intensive Care Unit.

Abstract: This study has introduced a new approach to clinical data processing. Clinical data is unstructured,

heterogeneous, and comes from various resources. Although, the challenges associated with processing such

data have been discussed widely in literature, addressing those aspects is fragmented and case-based. This

paper presents the initial outcome of applying the Time series Multi-Variables model (TsMV) to 12 different

datasets from Intensive Care Units (ICU), medications, and laboratories. TsMV supports the development of

an Intelligent Decision Support System for PM (IDSS4PM) by preparing effective data. Moreover, the

CRISP-DM methodology was employed, and based on the proposed solution, we have adjusted the significant

steps to CRISP-DM, where those extra phases are essential for taking future works.

1 INTRODUCTION

While, growing the aging population,

consumerism, increasing the availability of patient

data and limited human-cognitive for timely

decision-making, in addition to the economic

pressure have challenged the old model of clinical

decision-making, big data, and analytics have

provided the opportunity for developing Precision

Medicine (PM). Although there is various

definition of PM,

the US National Library of

Medicine, referred it to as “an as emerging approach

for disease treatment and prevention that takes into

account individual variability in genes, environment,

and lifestyle for each person” (Hulsen et al.,

2019),(Y. Zhang, Silvers, & Randolph, 2007).

Hence, not only biological factors are taken into

the consideration, but also environmental,

lifestyle, and patient’s condition and preferences

are important for releasing the best possible

treatment (Fenning, Smith, & Calderwood,

2019)

,(Williams et al., 2018).

The advantages of Artificial Intelligence (AI) and

analytics to reduce medical errors and increase the

performance of clinical decision-making are

extensively highlighted in the literature (Jiang et al.,

https://orcid.org/0000-0002-6153-2524

https://orcid.org/0000-0002-5441-3316

2017). While descriptive, diagnosis and predictive

analytics discover insights from data, prescriptive

analytics focuses on optimal decision-making

(Mosavi & Santos, 2020). Even though such

promising technologies are advanced to extract, and

interpret meaningful information from raw data, there

are major challenges and limitations associated with

data acquisition and processing in the context of the

adoption of PM. Based on that, current studies have

not demonstrated practical and valid frameworks to

address the limitations in processing diverse and

complex data (McPadden et al., 2019). This paper

aims to present TsMV approach for filling the

identified gaps in clinical data processing which is

essential for the development of IDSS4PM.

IDSS4PM is a framework from the concept of

“Decision Support Systems”, where AI and analytics

pioneer the development of PM to propose an optimal

outcome for clinical decision-making.

The Time series Multiple-Variables Approach for

Precision Medicine (TsMVs4PM) is designed to

address the challenges such as infrequent registration

of data, dimensionality, variety, velocity, and

integration aspects.

From a general point of view, the key limitations

and restrictions that have challenged the adoption of

PM are classified into three categories: “Definition of

Mosavi, N. and Santos, M.

Intelligent Decision Support System for Precision Medicine; Time Series Multi-variable Approach for Data Processing.

DOI: 10.5220/0011590600003335

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS, pages 231-238

ISBN: 978-989-758-614-9; ISSN: 2184-3228

231

PM”, “Data source/Data Management” and

“Validity/Reliability of clinical practice”. Although

there are wide contributions to explaining PM, the

terms have evolved and it requires time and practice

to emerge with the best possible performance.

Moreover, issues related to the variety and types of

data that come from diverse resources resulted in

limitations in data integration. Even though there are

defined standards for data exchange, stakeholders and

potential adopters need to cooperate to employ the

available policies (Sadat Mosavi & Filipe Santos,

2021). In addition, dealing with data quality, privacy,

dimensionality, integration, and interoperability have

provided remarkable research opportunities under the

category of “Data Management” (Liu, Luo, Jiang, &

Zhao, 2019). Finally, the shift from the traditional

protocol of clinical decision-making to the new one

needs valid and reliable practices in the area of AI and

Machine Learning (ML). This effort mostly depends

on overcoming the limitations of data sources,

handling big data, and data processing. However,

policies and regulations of data privacy, cost of

project development, multidisciplinary cooperation,

and technology acceptance by the professionals must

be considered to facilitate the fusion of PM (Sadat

Mosavi & Filipe Santos, 2021).

The TsMV method supports the development of

an analytical dashboard to monitor and analyze all the

clinical transactions from the time of admission to

discharge. This solution addresses infrequent data

registration and provides an integrated/unique

platform for the decision maker to analyze the clinical

background. Where It facilitates future works

(clustering prediction and optimization).

Furthermore, since the objective is to maximize

the rationality of clinical decision-making via

adopting analytics, we have employed Simon's model

of decision-making as the theoretical foundation.

Intelligence-Design-choice, introduced by Simon has

been identified as the most common framework in

decision making where the “Intelligence” phase is

about identifying the problem, searching and

collecting relevant information, and “Design” is

associated with generating alternatives and

developing possible courses of action. Furthermore,

evaluating the consequences of each option and

choosing the optimal performance are related in the

“Choice” phase (Mosavi & Santos, 2021).

Simon in “Bounded Rationality” identified that

the decision-maker chooses the first attention which

is good enough without evaluating alternatives, but

the optimum option cannot be the best decision as

there is a difference between decision making and

searching for the best (Barros, 2010),(Gigerenzer,

2001). Following the assumptions to decide

rationally, the decision-maker should know all the

alternatives as well as the consequence of each

alternative. In addition, the decision-maker should be

able to compute with perfect accuracy. Hence,

optimization is one step closer to normative decision-

making. In other words, optimization identifies the

best course of action; maximizing the value between

alternatives(Hertog, 2015),(Delen, 2020).

This paper has employed the CRISP-DM

methodology, so it is organized based on the six

phases of this methodology to present the initial result

in the data processing phase. Therefore, the first

three-phased are explained and the last three are

planned as future work for developing the final

framework of IDSS4PM.

2 CRISPS_DM METHODOLOGY

CRoss-Industry Standard Process for Data Mining

refers to the process of applying intelligent techniques

to data to extract patterns and identify valid and useful

information (S. Zhang, Zhang, & Yang, 2003). It is a

multidisciplinary subject, leveraging various

techniques such as ML, statistics, and data analytics

(Leprince, Miller, & Zeiler, 2021). Whereas Fayyad

considers DM as one of the phases in the Knowledge

Discovery from Database (KDD) process for

searching and discovering patterns(Fayyad &

Uthurusamy, 1996), CRISP-DM guides people to

know how DM can be applied in practice in real

systems (Ipp, Azevedo, & Santos, 2004). This is a

standard methodology used to support translating

business problems or application requirements and

objectives into data mining projects. Regardless of

the type of industry, CRISP-DM helps the

effectiveness of the outcome by extracting knowledge

from the raw data (Pete et al., 2000). This

methodology was introduced in the late 90s for

Knowledge Discovery from Database (KDD) (Grady,

2016) and was developed by the means of the effort

of a consortium initially composed of Daimler-

Chrysler, SPSS, and NCR. The six phases of CRISP-

DM 0.1 include “Business/Application

Understanding”, to identify problems or to define

objectives. This phare requires domain knowledge

and consists of various tasks and reports. “Data

Understanding” includes activities such as data

collection, exploration, and quality verification. The

third phase, “Data Preparation”, includes different

activities to prepare data for modelling. Moreover, in

“Modelling”, the most promising and potential ML

algorithms will be applied. In addition, “Evaluation”,

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

232

uses techniques to assess the accuracy of the result,

and finally, in “Deployment”, the most suitable

algorithm will be selected for practical use (Pete et

al., 2000). This paper presents the result of the first

three steps of developing IDSS4PM. This road map is

adjustable to analytics where Simon’s model of

decision-making is taken into consideration.

According to table 1, the first step is “Business

Understanding”; identifying scientific gaps and

defining objectives. Moreover, data collection and

analysis are associated with “Data Understanding”. In

addition, major activities are required to prepare

effective data such as data aggregation, feature

engineering, transformation, and cleaning.

Furthermore, TsMV as an integrated approach is

necessary for performing the discovery and

prediction. Accordingly, prediction and evaluation

are associated with “Modelling” and “Evaluation”.

Finally, we have justified “Optimization” as the part

of “Modelling “and “Evaluation” phases for

obtaining the sub-optimal result.

Table 1: Workplan According to CRISP-DM.

As table 2 presents, phases 1,2,3, and 4 which are

informative, are adjustable with the “Intelligence”

phase in Simon’s framework. Moreover, phases 1,2,

and 3 are based on “Descriptive” analytics. phase 4 is

associated with “Design” since in “Modelling” and

“Evaluation”, various algorithms will be generated,

and different scenarios will be analysed and assessed.

Finally, based on objectives and the outcome of the

evaluation, the most potentials and suitable

performance (algorithm) will be selected Hence, this

step is about decision making and performance

selection and it is related to “choice and prescriptive

analytics.

Table 2: Towards optimal clinical decision making.

Tasks CRISP_DM Simon’s

Framewor

Analytics

i Phase (1) Intelligence None

i, iii Phase (2) Intelligence Descriptive

iv,v,vi, vii Phase

(

)

Intelli

ence Descri

tive

viii, ix NEW Intelli

ence Descri

tive

x, xi Phase (4,5) Design,

Intelligence

Predictive

xii Phase

(

4,5,6

)

Choice Prescri

tive

2.1 Application| Business

Understanding

2.1.1 Precision Medicine

PM as a new approach in medical decision-making

has been motivated by major opportunities and

challenges such as the failed business model of “one

size fits all” (releasing similar treatments for patients

with similar symptoms) (Barros, 2010) which

resulted in less effective medical performance. In

addition, the cost of overtreatment, less balance

between patient expectations and the quality of

services, the global aging population growth, and an

increasing number of new chronic diseases, need a

supply of advanced scientific and medical

commitment and technologies (C. Kennedy & Turley,

201AD). Besides, the availability of healthcare big

data and advanced technologies such as AI, cloud

computing, the Internet of Things (IoT), and analytics

provide actionable and useful information for

decision-making (Wu et al., 2017). Thus, it is

expected, that by 2050, because of healthcare

digitization under the influence of technological

advancement, using patients’ biological data for

clinical decision-making will pioneer the adoption of

PM (Khadanga, Aggarwal, Joty, & Srivastava, 2019).

This shift from the traditional approach to the new

way of clinical decision-making will effectively

change the quality of treatment, especially for PM

where IoT facilitates customized data collection for

individual patients by considering the influence of

heterogeneous data (Brown, 2016).

2.1.2 Scientific Gaps in Data Processing

Phase

“As ever, where new technology promises “Big

Advances,” significant challenges remain” (Hulsen et

al., 2019).

As it was mentioned above, whereas aspects

related to data processing such as “data source and

management” are identified as the major limitations

xii. Optimization

xi. Evaluation

x. Prediction

ix. Discovery (2);Clustering

viii. Discovery (1)- Analytical Dashboards

vii. TsMV(transformation, visualization, integration)

vi. Cleaning

v. Feature selection ¦ construction| exteraction

iv. Aggregation

iii. Data Analysis

ii. Data Collection

i. Identify Gaps, Define objectives

Intelligent Decision Support System for Precision Medicine; Time Series Multi-variable Approach for Data Processing

233

for standardizing the fusion of the “one-size-fits-all”

model, other problems are related to terminology

itself, and existing clinical practices. Although there

is, a definition of PM released by the U.S national

library of medicine, since literature is heterogeneous

in terms of using the terminology of PM, there has not

been a standard paradigm used to develop a data-

driven DSS to conduct the protocol of PM (Sadat

Mosavi & Filipe Santos, 2021). For example, some

research works have considered genetic profiles as

necessary data for obtaining precise treatment

pathways, and others have defined PM as a process

that needs to be evolved and completed over time.

Therefore, by considering the general purpose of

emerging PM in healthcare which is accepted as a

process to develop the treatment pathway with more

accuracy and transparency based on patient profile,

research works and study designs strongly depend on

the particular research questions (Hulsen et al., 2019).

On the one hand, features (heterogeneous,

diverted, and unstructured) carried by clinical data

have resulted in poor synchronization, particularly in

data acquisition and integration phases (Y. Zhang et

al., 2007). On the other hand, the lack of an integrated

platform for considering multi-variable data

paradigm caused useful data and trend information

not able to be incorporated into a single model for

further decision-making (C. E. Kennedy & Turley,

2011). One considerable reason is the diverse

frequency of data registration from various resources.

In addition, the different time granularity of data

collection can result in ambiguous data correlation(Y.

Zhang et al., 2007). For example, data from bedside

monitoring has generally high frequency while

clinical sampling and lab tests might be taken

irregularly. Therefore, aspects such as frequency and

regulations of data generation strongly influence the

performance of the data processing phase (Wu et al.,

2017). In addition, verification of data quality is a

critical step in data processing. Data quality

considerably depends on major factors such as the

assessment of a patient’s condition by the clinical

team, misinterpretation of the original document,

and mistakes in data entry(Brown, 2016). Also,

Medical Waveforms (MW) such as

electrocardiograms and electroencephalograms,

which are widely utilized in physiological

examination, might caries random noise and

gaps(Khadanga et al., 2019).Therefore, deal with

missing values is another point needs to be addressed

in data cleaning and preparation (Adiba, Sharwardy,

& Rahman, 2021).Finally, the validity and reliability

of existing clinical practices in this area need

maturity, and new policies, regulations, and

cooperation pipelines between stakeholders to speed

up the emergence of PM. In other words, successful

and valid projects in scale affect positively the quality

of performance in general and indirectly best

practices boost problem solving associated with

technical areas such as data processing aspects

(Blasimme, Fadda, Schneider, & Vayena, 2018).

One common situation in digitized healthcare

platforms is where various physiological variables of

patients are continuously monitored and stored

resulting in huge amounts of data collected. Whereas

the integration of data collected at the bedside is

required to study associated with other data generated

during the patient’s involvement with treatment, and

other clinical aspects, outliers, and abnormal data

present bias, and related data must be ignored in

modelling and many cases data has to be filtered from

the study(Seyhan & Carini, 2019). Hence, despite the

promising start of Big Data analysis, manipulation,

and interpretation in clinical research, which has seen

a rising number of peer-reviewed articles, very

limited applications have been used to overcome

those aspects. A close future effort should be done to

validate the knowledge extracted from clinical Big

Data and implement it in clinical practice(Carra,

Salluh, da Silva Ramos, & Meyfroidt, 2020).

Major studies that contributed to offering a

promising framework have focused on time series

data and addressed limitations in data preparation.

One example is the “attention scores” technique for

feature importance in time series clinical data. This

method is complex and applicable for nonlinear

(Johnson, Parbhoo, Ross, & Doshi-Velez, 2021).

Another research works used summaries of patient

time series data for 24-72 hour from ICU to examine

the early prediction of in-hospital mortality. In this

study, static observations and physiological data

including labs and vital signs extracted based on

hourly circumstances. This approach limited the data

to vital signs and lab results and considered data

extraction and integration of time series clinical data

in the context of data aggregation (Johnson et al.,

2021). Another limitation related to data

management, and processing is storage and

computing. Especially for handling data that is

created with high frequency such as physiological

indicators. Although this data is valuable for

analyses, storing and managing such records needs

high computation and storage facilities. The

“Electron” framework is a solution offered to store

and analysed longitudinal physiologic monitoring

data (McPadden et al., 2019). Furthermore, the TDA

approach is an effective way for large-scale datasets

and employs algebraic topology to analysed big data

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

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by reducing the dimension, particularly for geometric

representations to extract patterns and obtain insight

into them. In addition, to deal with data velocity, the

“anytime algorithm “to learn from data streaming has

been introduced as a useful approach for time series

data which copies its growth over time. The

effectiveness of this method depends on the amount

of computation they were able to perform. Moreover,

to deal with heterogeneous data (variety), although

GNMTF is an efficient data integration framework,

subject to the number of data types to be integrated

the competencies complexity increased (Gligorijević,

Malod-Dognin, & Pržulj, 2016)

therefore, existing

approaches have offered solutions to manage data

such as time-series and case-based challenges such as

feature importance, feature selection, dimensionally

reduction, and velocity.

2.2 Data Understanding

According to table 2, there are 12 datasets collected

as excel files. The “vital sign” includes 439025

records with 108 features and many of them came

from biological sensors in ICU. Furthermore, “Lab

Result” is a dataset that includes outcomes of

laboratory exams. This table has113320 records with

9 features. “Procedure”, with 911 records, and 6

features consisting of raw data associated with an

action prescribed by the doctors. In addition, “SOAP”

with 2435 records and 8 features, keeps key data

about the SOAP framework (Subject, Object,

Assessment, Plan). Gravity score or “saps” presents

data about the level of gravity where it has 176

records and 6 features. Moreover, “Glasgow” carries

861 records and 6 features. The Glasgow table has

data about the consciousness status of each patient.

The “diagnosis” table with 124 records and 9 features

is about signs, symptoms, and laboratory findings.

While “prescription of medicine” addresses key

information about medications prescribed by the

clinician, “administration of medicine”, with 993496

records and 17 features is associated with drug

administration. The tenth table shows the intervention

data of each patient. Finally, an “admin-discharge”

dataset includes data on admission and discharge

from ICU. In addition, there is a reference dataset that

includes episode/process number exist in eleven

datasets. Those two variables are key to linking

datasets and are patient identifications.

The only dataset including time-series data is a

“vital sign” marked by #. Moreover, tables such as

“vital sign”, “procedure”, “SOAP” and “diagnosis” are

arked by | including time or date of admission. Based

on that, others specified by ||, have both time and date

(admission). In addition, tables marked by * consist of

data from ICU. R means the number of records and F

means the number of features. Also, two features

include distinct values whether “Process Number”

(DP) or “Episode Number” (DE). The “procedures”

and “SOAP” include DP (distinct process number) and

other tables have DE (distinct episode number).

Table 3: Data Collection/Initial Analysis.

2.3 Data Preparation

According to the CRISP_DM methodology, “Data

Preparation” consists of activities to prepare data for

the modelling phase. Since analytical dashboards and

clustering (discovery) require integrated clinical data,

TsMV performs to solve the limitation in the data

processing. Thus, In addition to the initial data

manipulation, TsMV method was applied. In this

case, we have modified the CRISP-DM methodology

by adding TsMV approach in data preparation and an

extra step before modelling which is discovery.

Figure 1: CRISP-DM - Adjusted for IDSS4PM.

Patient Data

# *| vital sign 70DE, 439025R, 108F

|| lab result 69DE,113320R, 9F

*| procedure 63DP,911R, 6F

*| SOAP 70DEP, 2435R,8F

*|| saps 17DE,176R,6F

*|| galgw 49DE, 861R,6F

*| diagnosis 67DE,124R,9F

|| med prescription 70DE,35422R, 39F

|| med administration 70DE, 993496R, 17F

*|| intervention 70DE,18674R, 4F

*admin-discharge -ICU

70DE, R,2F

process-episod number 70DEP,70R, 2F

1.Business

Understanding

2.Data

Understanding

3. Data Preparation

TsMV

4.Discovery

5.Modelling

6.Evaluation

7. Deployment

Intelligent Decision Support System for Precision Medicine; Time Series Multi-variable Approach for Data Processing

235

2.3.1 Data Preparation- TsMV

As it was mentioned above, In addition to the initial

data manipulation (feature engineering, data

cleaning, data extraction, etc.,), specific data

processing works were performed.

To solve the limitation associated with irregular data

acquisition and synchronization, we aggregated

physiological data (vital sign dataset) based on hourly

circumstances. This solution addressed the diverse

frequency of time series data registered by different

sensors. In addition, because data generation from

ICU (monitoring) has a high frequency compared

with other data resources (medications, procedures,

SAP, Glasgow, and intervention), therefore, to deal

with fragmented and infrequent data generation, we

performed a specific data transformation. where data

has been transformed from the time/date dimension

into a sequence dimension we called “Time Slot”

(TS). According to the figure2, each patient has a

Time Slot TS1 to TSn where the TS1 to TS23

includes one day of clinical transactions. In other

words, each (TS+1)-TS= 1 hour Therefore, each

patient with different episode numbers has TS from 1

to n where TS1 to TS23 shows transactions on the

first day of admission and TS24 to TS71 is associated

with the second day.

Figure 2: Timeline platform using TsMV model.

Data preparation is summarised in table 3 including

transformation to TS, aggregation, feature

engineering, selection, and data cleaning.

Transformation to TS: Each data set includes the

time/date of admission. Hence, in the transformation

from time/date to TS, each episode number has TS (1

to n).

Feature Engineering | selection and Extractions:

all tables had admission time in the format of seconds

and data was extracted in the form of the hour to

support TsMV solution.

The “vital sign” data set consists of time series

data from ICU, considering data quality analysis, and

based on studying the domain|literature, significant

physiological features were selected for hourly

aggregation. Hence, out of 108 features, specific

biological indicators (pulse rate, temperature,

oxygen, saturation, and heart rate) were chosen. In

addition, a new conditional column was constructed

where oxygen saturation value resulted in four

categories: “actual danger to life”, “critical-refer to a

specialist”, “decrease-insufficient”, and “serve

hypoxia hospitalization”. This new feature will

support clustering performance and the same type of

constructed feature will be applied to other biological

indicators too.

Lab result dataset includes features such as exam

classification, detailed exam, and references

associated with the value of the exam. This feature

was split into min and max references to support the

clustering phase. Moreover, result_status was created

as a conditional column where it compares the result

of the exam with the min-reference and max_refence,

to show if the result is below the minimum or more

than the maximum values. This new feature will be

used in the clustering phase.

The “procedures” table includes “zona” (specific area

of the patient body) and “DNOME” (in the category

of ZONA). Furthermore, the “SOAP” table includes

the subject, assessment, and plan.

The “saps”, has a feature (valor) to show the value

of the patient's gravity. Furthermore, in “Glasgow”,

The status of the patient’s consciousness level is

presented in a feature called valor.

In the “diagnosis” dataset, out of 124 features, 4

are selected where “SERVICE” shows the type of

diagnosis, and “DIAG1” addresses the description of

diagnosis for each episode number.

In addition, “medical administration” consists of

features such as the dose of a drug used by the nurse,

recommended dose, and the code of the drug.

Of the “medical prescription” with 14, out of 39

features, 10 effective ones were selected. Where the

code of medication, prescriptions, dose, and unit of

medicine are the essential ones. we have constructed

a new feature: “Period of Stay” to analyse the period

that a patient starts medication and finishes. This new

feature will be used in clustering.

Finally, the “intervention” like other datasets has

time|date of admission and episode number.

Moreover, intervention service shows the type of

service.

In terms of handling missing cells, in some tables

such as “vital sign,” we filled null cells with the

average of previous and next values and in some other

lab result

procedure

glasgw

TS1

TS2

TS24

TSn

SOAP

vital sign

saps

diognosis

med_admin

Interventinon

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

236

datasets, we deleted null cells and marked some other

null cells to decide on the modelling phase. In “lab

result” we constructed three columns to mark null

cells with zero where this method will be used in the

modeling phase. In addition, in “SOAP” we deleted

variables with the majority of missing values and

marked missing cells associated with main features.

As it was discussed, all datasets include

episid|process number, so this feature is used to link

each table to the look-up table (the table consisting of

the episode and process number was considered as the

look-up).

Besides, features such as age, gender, and status

(pre-operation, post-operation) are excluded to a new

table called demographics.

Table 4: Summary of data preparation.

Dataset

Time/Date

to TS

Aggregation

Feature

Engineering|

Selection

Missing

Cells

Vital sign Time hourly X Fille

Lab result Time - X Marke

rocedures Date - X Delete

SOAP Date - X Marke

oth - X Delete

Glas

oth - X Delete

Diagnosis Time - X Delete

Med_admin

oth - X Delete

Med

res

oth - X Delete

intervention

oth - X Delete

3 CONCLUSIONS

This paper presents the initial result of TsMV

approach to address the limitations and challenges

identified in the clinical data processing phase.

12 datasets have been used under the guidance of

CRISP_DM methodology to develop the framework

of IDSS4PM.

The current study considered the literature gaps in

integrating time series data from ICU and other

clinical data resources which are multi variables.

They proposed a solution by transforming time-

dependent data to TS (independent of time). This

solution not only provided a unique time sequence

platform for analysing the whole clinical background

from admission to discharge but also can solve

challenges highlighted in literature such as

infrequence data registration. In addition, having sync

data in a unique platform will facilitate the clustering

phase to classify similar patients by various indicators

(medications, period of stay, laboratory results, vital

signs, and SOAP). In addition, the modelling phase

will be performed based on the outcome of the

preparation phase. Based on that, the first three stages

of CRISP-DM are discussed, and this methodology

was modified by adding extra steps (TsMV,

discovery).

Future work will be developed using the outcome

of the fusion of TsMV for discovery phases

(analytical dashboards and clustering) and predicting

the best treatment pathway. Where optimization will

present the sub-optimal outcome by considering the

clinical objectives. Hence, “Discovery”,

“Modelling”, “Evaluation” and “Optimization” will

be performed as further steps to introduce an

IDSS4PM.

ACKNOWLEDGMENTS

The work has been supported by FCT – Fundação

para a Ciência e Tecnologia within the Projects

Scope: DSAIPA/DS/0084/2018.

REFERENCES

Adiba, F. I., Sharwardy, S. N., & Rahman, M. Z. (2021).

Multivariate time series prediction of pediatric ICU

data using deep learning. 2021 International

Conference on Innovative Trends in Information

Technology, ICITIIT 2021. https://doi.org/10.1109/

ICITIIT51526.2021.9399593

Barros, G. (2010). Herbert A . Simon and the concept of

rationality : Boundaries and procedures. 30(March

2009), 455–472.

Blasimme, A., Fadda, M., Schneider, M., & Vayena, E.

(2018). Data sharing for precision medicine: Policy

lessons and future directions. Health Affairs, 37(5),

702–709. https://doi.org/10.1377/hlthaff.2017.1558

Brown, S. A. (2016). Patient similarity: Emerging concepts

in systems and precision medicine. Frontiers in

Physiology, 7(NOV), 1–6. https://doi.org/10.3389/

fphys.2016.00561

Carra, G., Salluh, J. I. F., da Silva Ramos, F. J., &

Meyfroidt, G. (2020). Data-driven ICU management:

Using Big Data and algorithms to improve outcomes.

Journal of Critical Care, 60, 300–304. https://doi.org/

10.1016/j.jcrc.2020.09.002

Delen, D. (2020). Prescriptive Analytics The Final Frontier

for Evidence-Based Management and Optimal

Decision. In Library. Pearson Education, Inc.

Fayyad, U., & Uthurusamy, R. (1996). Data Mining and

Knowledge Discovery in Databases. Communications

of the ACM, 39(11), 24–26. https://doi.org/10.1145/

240455.240463

Intelligent Decision Support System for Precision Medicine; Time Series Multi-variable Approach for Data Processing

237

Fenning, S. J., Smith, G., & Calderwood, C. (2019).

Realistic Medicine: Changing culture and practice in

the delivery of health and social care. Patient Education

and Counseling, 102(10), 1751–1755.

https://doi.org/10.1016/j.pec.2019.06.024

Gigerenzer, G. (2001). Decision Making : Nonrational

Theories. 5, 3304–3309.

Gligorijević, V., Malod-Dognin, N., & Pržulj, N. (2016).

Integrative methods for analyzing big data in precision

medicine. PROTEOMICS, 16(5), 741–758.

https://doi.org/10.1002/PMIC.201500396

Grady, N. W. (2016). Knowledge Discovery in Data KDD

Meets Big Data. Archives of Civil Engineering, 62(2),

217–228. https://doi.org/10.1515/ace-2015-0076

Hertog, D. Den. (2015). Bridging the gap between

predictive and prescriptive analytics - new optimization

methodology needed. 1–15. Retrieved from

http://www.optimization-

online.org/DB_HTML/2016/12/5779.html

Hulsen, T., Jamuar, S. S., Moody, A. R., Karnes, J. H.,

Varga, O., Hedensted, S., … McKinney, E. F. (2019).

From big data to precision medicine. Frontiers in

Medicine, 6(MAR), 1–14. https://doi.org/10.3389/

fmed.2019.00034

Ipp, C. I., Azevedo, A., & Santos, M. F. (2004). Double-

Coated Bonding Material. Appliance, 61(1), 35.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang,

Y. (2017). Artificial intelligence in healthcare: Past,

present and future. Stroke and Vascular Neurology,

2(4), 230–243. https://doi.org/10.1136/svn-2017-

000101

Johnson, N., Parbhoo, S., Ross, A. S., & Doshi-Velez, F.

(2021). Learning Predictive and Interpretable

Timeseries Summaries from ICU Data. ArXiv.Org.

Retrieved from https://www.proquest.com/working-

papers/learning-predictive-interpretable-

timeseries/docview/2576125188/se-

2%0Ahttp://lenketjener.uit.no/?url_ver=Z39.88-

2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&genr

e=preprint&sid=ProQ:ProQ%3Aengineeringjournals&

atitl

Kennedy, C. E., & Turley, J. P. (2011). Time series analysis

as input for clinical predictive modeling: Modeling

cardiac arrest in a pediatric ICU. Theoretical Biology

and Medical Modelling, Vol. 8. https://doi.org/10.1186/

1742-4682-8-40

Kennedy, C., & Turley, J. (201AD). Time Series Analysis

As Input for Predictive Modeling : Predicting Cardiac

Arrest in a Pediatric Intensive Care Unit. Theoretical

Biology and Medical Modelling, 8(40), 1–25.

Khadanga, S., Aggarwal, K., Joty, S., & Srivastava, J.

(2019).

Using Clinical Notes with Time Series Data for

ICU Management. Retrieved from

http://arxiv.org/abs/1909.09702

Leprince, J., Miller, C., & Zeiler, W. (2021). Data mining

cubes for buildings, a generic framework for

multidimensional analytics of building performance

data. Energy and Buildings, 248, 111195.

https://doi.org/10.1016/j.enbuild.2021.111195

Liu, X., Luo, X., Jiang, C., & Zhao, H. (2019). Difficulties

and challenges in the development of precision

medicine. Clinical Genetics, 95(5), 569–574.

https://doi.org/10.1111/CGE.13511/

McPadden, J., Durant, T. J. S., Bunch, D. R., Coppi, A.,

Price, N., Rodgerson, K., … Schulz, W. L. (2019).

Health care and precision medicine research: Analysis

of a scalable data science platform. Journal of Medical

Internet Research, 21(4), 1–11.

https://doi.org/10.2196/13043

Mosavi, N. S., & Santos, M. F. (2020). How prescriptive

analytics influences decision making in precision

medicine. Procedia Computer Science, 177, 528–533.

https://doi.org/10.1016/j.procs.2020.10.073

Mosavi, N. S., & Santos, M. F. (2021). To what extent

healthcare analytics influences decision making in

precision medicine. Procedia Computer Science,

198(2021), 353–359. https://doi.org/10.1016/j.procs.20

21.12.253

Pete, C., Julian, C., Randy, K., Thomas, K., Thomas, R.,

Colin, S., & Wirth, R. (2000). Crisp-Dm 1.0. CRISP-

DM Consortium, 76.

Sadat Mosavi, N., & Filipe Santos, M. (2021). Adoption of

Precision Medicine; Limitations and Considerations

(David C. Wyld et al. (Eds), Ed.).

https://doi.org/10.5121/csit.2021.110302

Seyhan, A. A., & Carini, C. (2019). Are innovation and new

technologies in precision medicine paving a new era in

patients centric care? Journal of Translational

Medicine, 17(1), 1–28. https://doi.org/10.1186/s12967-

019-1864-9

Williams, A. M., Liu, Y., Regner, K. R., Jotterand, F., Liu,

P., & Liang, M. (2018). Artificial intelligence,

physiological genomics, and precision medicine.

Physiological Genomics, 50(4), 237–243.

https://doi.org/10.1152/PHYSIOLGENOMICS.00119.

2017

Wu, P. Y., Cheng, C. W., Kaddi, C. D., Venugopalan, J.,

Hoffman, R., & Wang, M. D. (2017). -Omic and

Electronic Health Record Big Data Analytics for

Precision Medicine. IEEE Transactions on Biomedical

Engineering, 64(2), 263–273. https://doi.org/10.1109/

TBME.2016.2573285

Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation

for data mining. Applied Artificial Intelligence,

17(5–

6), 375–381. https://doi.org/10.1080/713827180

Zhang, Y., Silvers, C. T., & Randolph, A. G. (2007). Real-

time evaluation of patient monitoring algorithms for

critical care at the bedside. Annual International

Conference of the IEEE Engineering in Medicine and

Biology - Proceedings, 2783–2786. https://doi.org/

10.1109/IEMBS.2007.4352906

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