P2DS: A Holistic Approach to Psychiatric Disease Detection in

Community Pharmacies

André Dias

1,2 a

, Tiago Dias

1,2 b

, Eva Maia

1,2 c

and Isabel Praça

1,2 d

School of Engineering, Polytechnic of Porto, (ISEP/IPP), Porto, Portugal

Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD),

Porto, Portugal

Keywords: Post-Traumatic Stress Disorder, Depression, Burnout, Smart Wearables, Emotion Recognition, Artificial

Intelligence.

Abstract: Health workers appear to have an increased risk of developing psychiatric diseases, namely Post-traumatic

stress disorder (PTSD), Depression and Burnout, due to the nature of their job. In recent years, several

approaches based on artificial intelligence have emerged, using facial expression, audio, text and

physiological features to detect depression, stress and burnout. However, most of these solutions have

limitations in their capacity to simultaneously detect multiple diseases, are not widely implemented in

healthcare settings, and, in some cases, lack explainability. To address this challenge, we propose Psychiatric

Disease Detection System (P2DS), a holistic rule-based system capable of detecting PTSD, Depression and

Burnout in community pharmacists, combining emotion recognition, physiological and performance-related

features. The set of rules developed to detect each disease is based on the most objective medical literature

available, making the system explainable and suitable for healthcare environments.

1 INTRODUCTION

The World Health Organization (WHO) defines

health workers as people whose work is destined to

improve health. Those include doctors, nurses,

pharmacists and technicians (World Health

Organization, 2022). There is increasing evidence

that health workers have an elevated risk of

developing psychiatric illnesses, mainly Depression

and Post-Traumatic Stress Disorder (PTSD), and

Burnout syndrome, in great part due to the multiple

risk factors present in their job (Hill et al., 2022; Razu

et al., 2021; World Health Organization, 2022). These

include increasing workload, long shifts, an

accelerated pace of work and lack of support (Søvold

et al., 2021). The presence of the aforementioned

diseases imposes serious consequences, namely

poorer patient care, increased work-related mistakes,

increased absenteeism and greater patient

https://orcid.org/0000-0001-5194-2784

https://orcid.org/0000-0002-1693-7872

https://orcid.org/0000-0002-8075-531X

https://orcid.org/0000-0002-2519-9859

dissatisfaction (Gregório et al., 2017; Samir AlKudsi

et al., 2022; Søvold et al., 2021).

In past years, researchers have developed multiple

systems to detect or predict psychiatric diseases.

These systems use physiological, audio and image

data, combined or separately, which is then run

through machine learning models to predict diseases

(Saganowski et al., 2023). Literature indicates that

these systems have revolved around pre-established

datasets and single disease detection. They have been

tested in patients, students, nurses, and corporate staff

(Chikersal et al., 2021; Eom et al., 2023; Francese &

Attanasio, 2022; Otsuka et al., 2023a; Yang et al.,

2020). Therefore, a gap in testing is noted in a

community pharmacy setting.

Hence, we propose Psychiatric Disease Detection

System (P2DS), a holistic system capable of detecting

and alerting for the presence PTSD, Major depressive

episode and Burnout syndrome. This can be achieved

through the detection of emotion and collection of

768

Dias, A., Dias, T., Maia, E. and Praça, I.

P2DS: A Holistic Approach to Psychiatric Disease Detection in Community Pharmacies.

DOI: 10.5220/0012568200003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 768-775

ISBN: 978-989-758-688-0; ISSN: 2184-4305

physiological and performance information. All this

data is then correlated using a rule-based system,

grounded in the most objective medical literature,

leading to the detection of the most common

psychiatric diseases in pharmacists and the creation

of an alert for the need to seek medical care. We

expect that this system could help on early detection

of psychiatric diseases, preventing health issues for

community pharmacists and improving patient safety.

2 STATE OF THE ART

Health workers have a high prevalence of psychiatric

diseases, especially after the COVID-19 pandemic

(Braquehais & Vargas-Cáceres, 2023). According to

recent literature, 100% of health workers reported

Burnout and, regarding psychiatric diseases, 21,7%

reported PTSD, 16,1% Anxiety and 13,3%

Depression (Hill et al., 2022; Jakovljevic et al., 2021).

When focusing on pharmacists, these numbers can go

up to 50% for Anxiety and 44% for Depression, and

about 60% for Burnout syndrome (Dee et al., 2022;

Samir AlKudsi et al., 2022; Weichel et al., 2021).

Among the possible causes, the high community

pharmacists’ workload has been a major concern

(Gregório et al., 2017; Samir AlKudsi et al., 2022).

2.1 Psychiatric Diseases and Burnout

Anxiety disorders are a large group of psychiatric

diseases. According to the Diagnostic and Statistical

Manual of Mental Disorders (DSM-V), examples of

Anxiety disorders are Generalized Anxiety Disorder,

Panic Disorder and Phobias (American Psychiatric

Association, 2013). Clinically, Anxiety can be

experienced through psychological and physical

symptoms. Psychological symptoms include fearful

anticipation, which is the sensation of fear before a

specific (phobia) or non-specific (generalized) event,

worrying thoughts, irritability, restlessness and poor

concentration. Physical symptoms can range from

palpitations (tachycardia) or tachypnea to increased

urinary frequency and mydriasis. Sleep disturbances,

namely initial insomnia, are also common. It is

defined by a Wake After Sleep Onset (WASO) or

Sleep Onset Latency (SOL) superior or equal to 30

minutes, at least three nights per week (Craske et al.,

2017; Harrison et al., 2017; Lichstein et al., 2003).

According to DSM-V, PTSD is allocated to the stress-

related disorders group. To diagnose this condition, a

preceding traumatic event (witnessed or experienced)

is necessary. In response to the causative trauma, the

patient develops intrusive ideas (for example, re-

experiencing the traumatic event or having

flashbacks), leading to the avoidance of triggering

stimuli, which are usually related to the traumatic

event. Negative alterations in cognition and mood are

present, for example amnesia, self-negative beliefs

(guilt), and a negative emotional state (that includes

fear or anger). Other symptoms that may be present

are Anxiety-related, namely poor concentration,

insomnia, and irritability. To establish a diagnosis,

these symptoms must be present for over one month

and cause functional impairment (American

Psychiatric Association, 2013; Sareen, 2023).

Depressive disorders are characterised by

depressed mood and lack of willingness and joy in

activities that were once enjoyable (Harrison et al.,

2017). Sadness is typically associated with Depression.

However, for a depressive episode to occur, sadness

must be present every day, for the most part of the day

and lasting a minimum of two weeks. Other symptoms

that help establishing the diagnosis are insomnia, early

awakenings, fatigue, diminished efficiency, loss of

weight (at least 5% body weight in 1 month) and

anorexia. There are also feelings of worthlessness and

guilt, which are usually excessive or inappropriate. In

milder or atypical forms of the disease, symptoms that

overlap with Anxiety disorders can occur, for example,

irritability and hypersomnia (American Psychiatric

Association, 2013; Bains & Abdijadid, 2023; Harrison

et al., 2017).

Burnout syndrome is a response to chronic work

stress and can be defined by emotional exhaustion,

depersonalization and lack of personal achievement.

Emotional exhaustion typically manifests with

tiredness and fatigue, leading to increased difficulty

adapting to the work environment, as the person stops

having the necessary emotional energy to cope with

work. Depersonalization is described as a detachment

or indifference towards co-workers, sometimes

leading to negative attitudes due to increased

irritability. At last, people experiencing lack of

personal achievement typically refer a feeling of

worthlessness, of not being good enough or feeling

that they are underperforming at work. Furthermore,

burnout syndrome can be contagious. This

phenomenon is called emotional contagion and is

particularly common in health settings. It can be

detected if other coworkers start experiencing the

same symptoms and emotions (Edú‐valsania et al.,

2022; Nápoles, 2022). A person with burnout

syndrome expresses typical emotions that,

accordingly to Otsuka et. al, are increased surprise

and sadness, and decreased happiness, for more than

one month. However, the authors only highlight the

P2DS: A Holistic Approach to Psychiatric Disease Detection in Community Pharmacies

769

role of decreased happiness as a predictive factor for

burnout syndrome (Otsuka et al., 2023b).

There are numerous consequences to the

aforementioned diseases. In the case of Depression

and Anxiety, examples include an increased risk for

coronary artery disease (Ayers & de Visser, 2021).

Depression alone increases the risk of type 2 Diabetes

Mellitus and Parkinson disease and ultimately, if

untreated, can lead to death by suicide (American

Psychiatric Association, 2013; Ayers & de Visser,

2021). Furthermore, there is an increased risk for

substance abuse and social isolation, leading to

functional decline. In terms of work-related

consequences, PTSD, Depression and Burnout lead to

increased absenteeism, work-related mistakes and

diminished productivity (American Psychiatric

Association, 2013; Craske et al., 2017; Edú‐valsania

et al., 2022; Lerner et al., 2010; Vignoli et al., 2017).

In conclusion, there is a growing urgency to address

the emotional, physical and mental exhaustion of

health workers (Mollica & Fricchione, 2021).

2.2 Related Work

In the field of psychiatric condition detection,

multiple approaches have surfaced for the detection

of Depression and stress, using facial expression,

speech, and physiological data as the main features

analysed (Saganowski et al., 2023).

Concerning PTSD, multiple works have emerged

for stress detection. Grupta et. al developed a system

based in the Wearable Stress and Affect Detection

(WESAD) dataset, containing real-world data from

corporate employees. Physiological data (ECG,

temperature, respiratory rate, electrodermal activity

and electromyogram) was collected through smart

bands, allowing the identification of employees with

abnormal levels of stress (Gupta et al., 2022). A

similar approach has been used on real-word

healthcare data, in an experimental fashion. Eom et.

al used a dataset containing over 1000 hours of

physiological data captured by an Empatica E4 band,

used by hospital nurses during the COVID-19

pandemic. The nurses were also instructed to answer

a stress-related survey to help validating the acquired

data, which was then processed in a multimodal

Convolutional Neural Network (CNN) model to

predict stress. Although real-world data was used, the

model was not implemented on the field (Eom et al.,

2023). Muñoz and Iglesias tried a different approach,

using text. They used the datasets Dreaddit, Natural

Stress Emotion and TensiStrenght, which contain text

data from Reddit, real world interviews and Twitter,

respectively. Data was processed in a lexicon-based

framework for the detection of stress (Muñoz &

Iglesias, 2022). Keystroke analysis has also been used

to this end by Bakkialakshmi and Sudalaimuthu. The

authors collected typing data with the help of 200

volunteers which was posteriorly run through a

Dynamic Cat-Boost algorithm to detect stress. (G.L.

Bajaj Institute of Technology & Management, 2022).

Rodrigues and Correia combined physiology and

image features of 28 healthy volunteers of an

insurance company. They collected heart rate and eye

closure from users and validated data with stress-

related questionnaires, serving as input for training

machine learning models capable of predicting stress

(Rodrigues & Correia, 2023). Singh et. al collected

video and audio data from students using cameras and

smartphone microphones, which was then run though

machine learning models to detect stress (Singh et al.,

2022). Lastly, Dogan and Akbulut combined

physiological, audio and visual data from the

WorkStress3D dataset for stress detection, by

analysing facial expressions, physiological and audio

data (Dogan & Akbulut, 2023). Although stress

relates to PTSD, to the best of the author’s

knowledge, there are no systems for PTSD detection.

Concerning Depression, Francese et. al used a

system that combined speech and facial recognition

to increase the accuracy of the Beck Depression

Inventory II (BDI-II) questionnaire, a screening tool

for Depression. The combination between facial

expression, speech and the answers provided in BDI-

II impressed both clinicians and patients, who

highlighted it as helpful in decision making (Francese

& Attanasio, 2022). Yoon et al. explored the dataset

D-Vlog, consisting of 160 hours of Youtube Vlog

video. Speech and facial expressions were extracted,

processed and combined for the detection of

depression (Yoon et al., 2022). Anshul et. al tried a

different approach, using text, image and account

details from Twitter users. The collected data was

used to train three different machine learning models

to identify Depression (Anshul et al., 2023). Yang et.

al used a similar methodology, retrieving Facebook

data (text and account details) from the dataset

“myPersonality” and Thorstad and Wolff used online

text from Reddit to predict user Depression (Thorstad

& Wolff, 2019; Yang et al., 2020). Park and Moon

collected speech and text data from the DAIC-WOZ

Dataset, which were processed separately and then

fused by a multimodal fusion model to recognise

Depression (Park & Moon, 2022). Chikersal et al

used data from smartphones and fitness trackers to

identify Depression on campus students. The

collected data included number of calls, GPS

location, phone usage, steps, sleep and behaviour.

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Comparatively to similar works, this had the most

participants and variety of devices and data sources

(Chikersal et al., 2021).

Regarding Burnout, to the best of the authors

knowledge, there are no detection systems that

comprise wearables, facial expression, or audio.

Despite this, Otsuka et. al studied the relation

between emotions and Burnout by detecting the facial

expression of the participants. They used a “Face

Recognition and Attendance App” for facial

recognition and the questionnaire BAT-J (Japanese

version of the Burnout Assessment Scale) for Burnout

screening (Otsuka et al., 2023a).

Table 1 encompasses the various approaches

previously described. Notably, only the study by Eom

et al. uses data from health workers, although the

system was not tested in a healthcare setting. In

contrast, Francese et al.'s work on Depression took

place on a healthcare setting but targeted patients. No

studies were found for the detection of a combination

of diseases.

3 PSYCHIATRIC DISEASES

DETECTION SYSTEM

The analysis of medical literature related to the three

psychiatric diseases under analysis, PTSD, Major

Depressive Episode and Burnout Syndrome,

permitted the identification of the key characteristics

present on each condition. We have aggregated these

into three different domains: (i) human emotion, (ii)

physiological signs and (iii) performance metrics.

Considering the holistic setting in which these

diseases can be predicted, we propose P2DS, a

psychiatric diseases detection system composed of

three alert-generation modules, one for each domain,

and a rule-based inference engine capable of

correlating the alerts generated via classification

rules. Figure 1 describes a general overview of P2DS.

In this architecture, the modules defined as Emotion

Recognizer, Physiology Monitor and Performance

Collector are constant observers of the pharmacists’

emotions, physiological signs and performance

metrics in the context of the pharmacy, outputting a

great amount of data in real-time. The correlation

Table 1. Detection of Stress, Depression and Burnout Approaches Summary.

Author and year Psychiatric condition Field-tested Features

(Francese & Attanasio, 2022) Depression (aid only) Yes Speech + Facial recognition

(Yoon et al., 2022) Depression No Speech + Facial recognition

(Anshul et al., 2023) Depression No

Text + Online image + Account

details

(Yang et al., 2020) Depression No Text + Account details

(Thorstad & Wolff, 2019) Depression No Text

(Park & Moon, 2022) Depression No Speech + Text

(Chikersal et al., 2021) Depression Yes

Physiologic signs + GPS location

+ calls numbe

(Gupta et al., 2022) Stress No Physiologic signs

(Eom et al., 2023) Stress No Physiologic signs + Questionnaire

(Muñoz & Iglesias, 2022) Stress No Text

(G.L. Bajaj Institute of Technology

& Mana

ement, 2022)

Stress Yes Typing patterns

(Rodrigues & Correia, 2023) Stress Yes

Physiologic signs + Eye closure +

Questionnaire

(Singh et al., 2022) Stress No Speech + Facial recognition

(Dogan & Akbulut, 2023) Stress No

Physiologic signs + Speech +

Facial reco

nition

(Otsuka et al., 2023a) Burnout Yes Facial recognition + Questionnaire

P2DS: A Holistic Approach to Psychiatric Disease Detection in Community Pharmacies

771

engine ingests this information and processes it using

the classification rules defined in its knowledge base

to predict the presence of psychiatric diseases, raising

alerts for the need to seek medical attention. The

following subsections describe in greater detail the

intricacies and characteristics of each subsystem.

Figure 1: General overview of P2DS.

3.1 Emotion Recogniser

Emotion is a broad term that includes our affect,

mood, and impulses. It is generally divided into three

separate components: a cognitive one, relative to how

our interpretation of a situation modulates emotion; a

physiological one, related to the physiologic changes

in response to emotions; and lastly a behavioural one,

which includes our facial expressions (Ayers & de

Visser, 2021). Technological progress has allowed

the identification of human emotions through these

components, with most recent approaches using

Machine Learning (Cai et al., 2023; Pan et al., 2023).

The Emotion Recognizer is intended to perform

emotion classification using Machine Learning

models and relying on image and audio data, as these

would be the expected means of communication

utilized in a pharmacy. These classifiers could be

trained effectively utilizing pre-existing datasets,

providing that they follow Paul Ekman’s model of

basic emotions, a standard, cross-cultural, recognized

model defining 6 universal emotions: sadness,

happiness, disgust, anger, surprise and fear, plus

neutral (Ayers & de Visser, 2021).

Regarding the data to train these classification

models, there is a wide range of datasets for emotion

detection, which have been collected in natural and

induced environments. However, literature shows

that models trained with data captured in a controlled

environment tend to be less applicable in a real

context with real conditions (Aguilera et al., 2023).

Since this module constitutes multimodality, a

dataset like MELD or RECOLA would be fit for

training the model (Poria et al., n.d.; Ringeval et al.,

2013). Nonetheless, the importance of unimodal

datasets, such as IFEED, cannot be overlooked, as

these could provide valuable data to increase the

model’s performance and generalizability (T. Dias et

al., 2023).

3.2 Physiology Monitor

The popularity of wearable health devices has

increased in the recent years, as reflected by an

increasing market value over the years, with more

units sold (D. Dias & Cunha, 2018; Escobar-Linero et

al., 2023; Lu et al., 2020). These devices allow for the

continuous monitorization of a person’s vital signs,

providing a stream of real-time data. They have the

advantage of being non-obtrusive and are readily

accessible. As so, similarly to other studies cited

above, the Physiology Monitor uses wearable health

devices, such as smartwatches, for collecting sleep

data (SOL, WASO and awakening time), cardiac and

respiratory rate, tremor (via electromyogram) and

sweating (via electrodermal activity).

Weight changes can be monitored by establishing

the initial weight and subsequently conducting

monthly re-evaluations. The use of a Bluetooth scale

obviates the necessity for manual weight input.

Afterwards, weight variation would be the difference

between the previous and the current weight,

subsequently turned into a percentage.

3.3 Performance Collector

Psychiatric diseases imply the existence of functional

impairment, which may manifest by loss of perfor-

mance (Telles-Correia et al., 2018). To measure this,

we propose the Performance Collector, an information

system designed to gather data from the software

systems of pharmacies concerning the productivity,

work-related errors, and absenteeism of pharmacists.

The standard values for these parameters should be

based on the pharmacy’s estimates, as different

pharmacies have different numbers of employees, sales

volumes and working hours (Gregório et al., 2017).

Values that are significantly different to those defined

could pose a red flag for all conditions.

3.4 Correlation Engine

The Correlation Engine is a rule-based inference

engine that leverages expert knowledge in the form of

classification rules to perform inference. This module

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aggregates evidence from all three subsystems,

leveraging rules to analyse the data and infer alert-

raising conclusions. These rules include objective

emotions, signs and other information related to each

psychiatric disease.

We propose two sets of medical literature-based

rules, complementary to this system, which are

subdivided into main and adjuvant. The main rules

include information regarding emotion. The adjuvant

rules allow for a more fine-grained detection of each

disease and comprise physiological and performance

information. Since these are grounded on medical

literature, our system achieves a greater degree of

explainability in comparison to existing alternatives.

In PTSD, we defined persistent fear (for over 1

month) as a necessary condition. The adjuvant

conditions are tachycardia, tachypnea, increased

sweating, tremor, insomnia, increased absenteeism,

work-related mistakes, diminished productivity, and

irritability. As irritability is nonspecific, we considered

it an adjuvant condition rather than a main one.

For Major depressive episode, daily sadness, for

the most part of the day and during at least two weeks

is a main condition. The adjuvants are insomnia (as

defined earlier), early awakenings (compared to the

normal personal awakening time), weight loss

superior to 5% of total body weight in one month,

increased absenteeism, work-related mistakes and

diminished productivity. To cover for atypical

Depression symptoms, hypersomnia (defined by

delayed awakening when compared to normal) and

irritability are also adjuvant conditions.

For Burnout syndrome the main condition is

decreased happiness for more than one month, based

on the work of Otsuka et.al (Otsuka et al., 2023a).

Adjuvant conditions are increased absenteeism,

work-related mistakes, diminished productivity,

irritability and emotional contagion.

To increase accuracy, we propose the application

of standardized score-based questionnaires, namely

Primary Care PTSD Screening questionnaire (PC-

PTSD-5) for PTSD, Patient Health Questionnaire-9

(PHQ-9) for Depression and Burnout Assessment

Tool (BAT) for Burnout. (Bains & Abdijadid, 2023;

Edú‐valsania et al., 2022; Sareen, 2023; Sinval et al.,

2022). Table 2 summarizes all these rules.

4 CONCLUSIONS

This article presents a novel solution for the detection

of the most common psychiatric diseases in healthcare

workers, particularly community pharmacists.

Comparatively to other existing systems, our proposal

detects three different conditions: PTSD, Major

depressive episode and Burnout syndrome, which, to

the best of the authors knowledge, has not been done.

In addition, we designed this system to be as holistic as

possible, respecting objective medical literature

available, and combining image, audio, physiological

and performance data. In comparison to other works

that also combine multiple modalities, ours is

explainable, as each correlation can be explained by the

classification rules defined. In addition to the field of

psychiatric disease detection, we also define, design

and architect a system purposefully built to be

implemented in the context of community pharmacies.

Data collection in this setting is as non-obtrusive as

possible, requiring only the use of a wristband,

monthly weight determinations and image and audio

capturing. Furthermore, this work contributes to the

knowledge of psychiatric disease detection, by

analysing several sources of available medical

literature, using the most objective criteria possible to

design the rules.

Table 2: Correlation Rules for each disease.

Conditions PTSD Ma

or De

ressive E

isode Burnout S

ndrome

Main condition

Increase in fear for more

than one month

Daily sadness for at least two weeks

Decreased happiness for

more than one month

Adjuvant

conditions

Tachycardia

Weight loss superior to 5% in one

month

Emotional contagion

Tachypnea Early awakenings

Increased sweatin

Insomnia Increased tremo

Insomnia

Increased absenteeis

Decreased productivit

Increased wor

-related mistakes

Irritabilit

Questionnaire PC-PTSD-5 PHQ-9 BAT

P2DS: A Holistic Approach to Psychiatric Disease Detection in Community Pharmacies

773

ACKNOWLEDGEMENTS

This work has received funding from the project

ForPharmacy (P2020-COMPETE-FEDER number

070053). This work has also received funding from

projects UIDB/00760/2020 and UIDP/00760/2020.

REFERENCES

Aguilera, A., Mellado, D., & Rojas, F. (2023). An

Assessment of In-the-Wild Datasets for Multimodal

Emotion Recognition. Sensors, 23(11).

American Psychiatric Association. (2013). Diagnostic and

statistical manual of mental disorders (5th ed.).

Anshul, A., Pranav, G. S., Rehman, M. Z. U., & Kumar, N.

(2023). A Multimodal Framework for Depression

Detection During COVID-19 via Harvesting Social

Media. IEEE Transactions on Computational Social

Systems.

Ayers, S., & de Visser, R. (2021). Psychology for Medicine

& Healthcare (3rd Edition) (Sage Publishing).

Bains, N., & Abdijadid, S. (2023, April 10). Major

Depressive Disorder. StatPeals.

https://www.ncbi.nlm.nih.gov/books/NBK559078/

Braquehais, M. D., & Vargas-Cáceres, S. (2023).

Psychiatric Issues Among Health Professionals. In

Medical Clinics of North America (Vol. 107, Issue 1,

pp. 131–142). W.B. Saunders.

Cai, Y., Li, X., & Li, J. (2023). Emotion Recognition Using

Different Sensors, Emotion Models, Methods and

Datasets: A Comprehensive Review. In Sensors (Vol.

23, Issue 5). MDPI.

Chikersal, P., Doryab, A., Tumminia, M., Villalba, D. K.,

Dutcher, J. M., Liu, X., Cohen, S., Creswell, K. G.,

Mankoff, J., David Creswell, J., Goel, M., & Dey, A.

K. (2021). Detecting depression and predicting its onset

using longitudinal symptoms captured by passive

sensing: A machine learning approach with robust

feature selection. ACM Transactions on Computer-

Human Interaction, 28(1).

Craske, M. G., Stein, M. B., Eley, T. C., Milad, M. R.,

Holmes, A., Rapee, R. M., & Wittchen, H. U. (2017).

Anxiety disorders. Nature Reviews Disease Primers, 3.

Dee, J., Dhuhaibawi, N., & Hayden, J. C. (2022). A

systematic review and pooled prevalence of burnout in

pharmacists. In International Journal of Clinical

Pharmacy. Springer Science and Business Media

Deutschland GmbH.

Dias, D., & Cunha, J. P. S. (2018). Wearable health

devices—vital sign monitoring, systems and

technologies. In Sensors (Switzerland) (Vol. 18, Issue

8). MDPI AG.

Dias, T., Vitorino, J., Oliveira, J., Oliveira, N., Maia, E., &

Praça, I. (2023). IFEED: Interactive Facial Expression

and Emotion Detection Dataset. Zenodo.

Dogan, G., & Akbulut, F. P. (2023). Multi-modal fusion

learning through biosignal, audio, and visual content for

detection of mental stress. Neural Computing and

Applications, 35(34), 24435–24454.

Edú‐valsania, S., Laguía, A., & Moriano, J. A. (2022).

Burnout: A Review of Theory and Measurement. In

International Journal of Environmental Research and

Public Health (Vol. 19, Issue 3). MDPI.

Eom, S., Eom, S., & Washington, P. (2023). SIM-CNN:

Self-Supervised Individualized Multimodal Learning

for Stress Prediction on Nurses Using Biosignals.

Escobar-Linero, E., Muñoz-Saavedra, L., Luna-Perejón, F.,

Sevillano, J. L., & Domínguez-Morales, M. (2023).

Wearable Health Devices for Diagnosis Support:

Evolution and Future Tendencies. In Sensors (Vol. 23,

Issue 3). MDPI.

Francese, R., & Attanasio, P. (2022). Emotion detection for

supporting depression screening. Multimedia Tools and

Application.

G.L. Bajaj Institute of Technology & Management. (2022).

2022 International Conference on Computational

Intelligence and Sustainable Engineering Solutions

(CISES) 20-21 May 2022.

Gregório, J., Cavaco, A. M., & Lapão, L. V. (2017). How

to best manage time interaction with patients?

Community pharmacist workload and service provision

analysis. Research in Social and Administrative

Pharmacy, 13(1), 133–147.

Gupta, A., Raut, A., Yadav, R., Kumar, M., & Chaurasiya,

V. K. (2022). A Hybrid Approach based Stress

Monitoring System for Office Environment using IoT.

INDICON 2022 - 2022 IEEE 19th India Council

International Conference.

Harrison, P., Cowen, P., Burns, T., & Fazel, M. (2017).

Shorter Oxford Textbook of Psychiatry. Shorter Oxford

Textbook of Psychiatry.

Hill, J. E., Harris, C., Danielle L., C., Boland, P., Doherty,

A. J., Benedetto, V., Gita, B. E., & Clegg, A. J. (2022).

The prevalence of mental health conditions in

healthcare workers during and after a pandemic:

Systematic review and meta-analysis. In Journal of

Advanced Nursing (Vol. 78, Issue 6, pp. 1551–1573).

John Wiley and Sons Inc.

Jakovljevic, B., Stojanovic, K., Turnic, T. N., &

Jakovljevic, V. L. (2021). Burnout of physicians,

pharmacists and nurses in the course of the covid-19

pandemic: A serbian cross-sectional questionnaire

study. International Journal of Environmental

Research and Public Health, 18(16).

Lerner, D., Adler, D. A., Rogers, W. H., Chang, H.,

Lapitsky, L., McLaughlin, T., & Reed, J. (2010). Work

Performance of Employees With Depression: The

Impact of Work Stressors. American Journal of Health

Promotion : AJHP, 24(3), 205.

Lichstein, K. L., Durrence, H. H., Taylor, D. J., Bush, A. J.,

& Riedel, B. W. (2003). Quantitative criteria for

insomnia. Behaviour Research and Therapy, 41(4),

427–445.

Lu, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye,

Z. (2020). Wearable health devices in health care:

Narrative systematic review. In JMIR mHealth and

uHealth (Vol. 8, Issue 11). JMIR Publications Inc.

HEALTHINF 2024 - 17th International Conference on Health Informatics

774

Mollica, R. F., & Fricchione, G. L. (2021). Mental and

physical exhaustion of health-care practitioners. In The

Lancet (Vol. 398, Issue 10318, pp. 2243–2244).

Elsevier B.V.

Muñoz, S., & Iglesias, C. A. (2022). A text classification

approach to detect psychological stress combining a

lexicon-based feature framework with distributional

representations. Information Processing &

Management, 59(5), 103011.

Nápoles, J. (2022). Burnout: A Review of the Literature.

Update: Applications of Research in Music Education,

40(2), 19–26.

Otsuka, Y., Sagisaka, Y., Nakamura, J., Hara, K., Okada,

M., Takeuchi, Y., Tsuchiya, M., & Monden, Y.

(2023a). Happiness Detected by the Emotion Cognition

System Is Associated with Burnout in an Information

Technology Products and Services Trading Company.

International Journal of Environmental Research and

Public Health, 20(3).

Otsuka, Y., Sagisaka, Y., Nakamura, J., Hara, K., Okada,

M., Takeuchi, Y., Tsuchiya, M., & Monden, Y.

(2023b). Happiness Detected by the Emotion Cognition

System Is Associated with Burnout in an Information

Technology Products and Services Trading Company.

International Journal of Environmental Research and

Public Health, 20(3).

Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023). A review of

multimodal emotion recognition from datasets,

preprocessing, features, and fusion methods.

Neurocomputing, 561, 126866.

Park, J., & Moon, N. (2022). Design and Implementation of

Attention Depression Detection Model Based on

Multimodal Analysis. Sustainability (Switzerland),

14(6).

Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria,

E., & Mihalcea, R. (n.d.). MELD: A Multimodal Multi-

Party Dataset for Emotion Recognition in

Conversations.

Razu, S. R., Yasmin, T., Arif, T. B., Islam, M. S., Islam, S.

M. S., Gesesew, H. A., & Ward, P. (2021). Challenges

Faced by Healthcare Professionals During the COVID-

19 Pandemic: A Qualitative Inquiry From Bangladesh.

Frontiers in Public Health, 9, 1024.

Ringeval, F., Sonderegger, A., Sauer, J., & Lalanne, D.

(2013). Introducing the RECOLA multimodal corpus of

remote collaborative and affective interactions. 2013

10th IEEE International Conference and Workshops on

Automatic Face and Gesture Recognition, FG 2013.

Rodrigues, F., & Correia, H. (2023). Semi-supervised and

ensemble learning to predict work-related stress.

Journal of Intelligent Information Systems.

Saganowski, S., Perz, B., Polak, A. G., & Kazienko, P.

(2023). Emotion Recognition for Everyday Life Using

Physiological Signals From Wearables: A Systematic

Literature Review. In IEEE Transactions on Affective

Computing (Vol. 14, Issue 3, pp. 1876–1897). Institute

of Electrical and Electronics Engineers Inc.

Samir AlKudsi, Z., Hany Kamel, N., El-Awaisi, A., Shraim,

M., & Saffouh El Hajj, M. (2022). Mental health,

burnout and resilience in community pharmacists

during the COVID-19 pandemic: A cross-sectional

study. Saudi Pharmaceutical Journal, 30(7), 1009–

1017.

Sareen, J. (2023). Posttraumatic stress disorder in adults:

Epidemiology, pathophysiology, clinical features,

assessment, and diagnosis. In UpToDate, Post TW,

Wolters Kluwer.

Singh, M., Bharti, S., Kaur, H., Arora, V., Saini, M., Kaur,

M., & Singh, J. (2022). A Facial and Vocal Expression

Based Comprehensive Framework for Real-Time

Student Stress Monitoring in an IoT-Fog-Cloud

Environment. IEEE Access, 10, 63177–63188.

Sinval, J., Vazquez, A. C. S., Hutz, C. S., Schaufeli, W. B.,

& Silva, S. (2022). Burnout Assessment Tool (BAT):

Validity Evidence from Brazil and Portugal.

International Journal of Environmental Research and

Public Health, 19(3).

Søvold, L. E., Naslund, J. A., Kousoulis, A. A., Saxena, S.,

Qoronfleh, M. W., Grobler, C., & Münter, L. (2021).

Prioritizing the Mental Health and Well-Being of

Healthcare Workers: An Urgent Global Public Health

Priority. Frontiers in Public Health, 9, 679397.

Telles-Correia, D., Saraiva, S., & Gonçalves, J. (2018).

Mental disorder-The need for an accurate definition.

Frontiers in Psychiatry, 9(MAR).

Thorstad, R., & Wolff, P. (2019). Predicting future mental

illness from social media: A big-data approach.

Behavior Research Methods, 51(4), 1586–1600.

Vignoli, M., Muschalla, B., & Mariani, M. G. (2017).

Workplace Phobic Anxiety as a Mental Health

Phenomenon in the Job Demands-Resources Model.

BioMed Research International, 2017.

Weichel, C., Lee, J. S., & Lee, J. Y. (2021). Burnout among

Hospital Pharmacists: Prevalence, Self-Awareness, and

Preventive Programs in Pharmacy School Curricula.

Canadian Journal of Hospital Pharmacy, 74(4), 309–

316.

World Health Organization. (2022). Occupational health:

health workers.

Yang, X., McEwen, R., Ong, L. R., & Zihayat, M. (2020).

A big data analytics framework for detecting user-level

depression from social networks. International Journal

of Information Management, 54.

Yoon, J., Kang, C., Kim, S., & Han, J. (2022). D-vlog:

Multimodal Vlog Dataset for Depression Detection.

https://sites.google.com/view/jeewoo-yoon/dataset

P2DS: A Holistic Approach to Psychiatric Disease Detection in Community Pharmacies

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