docs(thesis): add lit review to introduction

docs/thesis/glossary.tex

@@ -27,6 +27,12 @@
     description={User Interface},
     first={User Interface (GUI)}
 }
+\newglossaryentry{iot}{
+    type=\acronymtype,
+    name={IoT},
+    description={Internet of Things},
+    first={Internet of Things (IoT)}
+}
 \newglossaryentry{spo2}{
     type=\acronymtype,
     name={SPO\textsubscript{2}},

docs/thesis/thesis.tex

@@ -123,8 +123,6 @@ A summary must be written in both English and German.
 
 \subsection{Background}
 
-% TODO add full lit review
-
 Clinical \gls{deterioration} is a critical concern in healthcare, particularly for vulnerable populations such as the elderly and chronically
 ill patients. It refers to a decline in a patient's health status and may lead to adverse outcomes, including hospitalization,
 longer stays in intensive care units, and increased healthcare costs.
@@ -183,30 +181,349 @@ With hospitals facing overwhelming patient load during the SARS-CoV-2 pandemic,
 and \Gls{news2} emerged as an effective tool for predicting severe infection outcomes\cite{filho_iot-based_2021, gidari_predictive_2020, otoom_iot-based_2020, carr_evaluation_2021}
 while reducing person-to-person contact during patient monitoring.
 
-\subsection{Motivation}
+\subsection{Review of existing literature}
+
+In order to examine the current state of scientific knowledge about the use of wearable devices for automated \Gls{ews} monitoring of
+patients at home, a comprehensive review of the existing literature was conducted.
+By systematically examining and synthesizing the current body of knowledge, this review identified a variety of approaches for
+utilizing smart medical devices in post-discharge patient care, as well as existing limitations and challenges in future research
+in this rapidly evolving field.
+
+\subsubsection{Search strategy}
+
+A systematic search strategy was implemented on the Scopus database, aimed to encompass a broad spectrum of literature relevant
+to the use of smart medical devices for automated early warning score monitoring of patients dismissed from ambulant or hospital care.
+The search focused on topics related to the research area, encompassing the examination of \Gls{ews}, hospital admission, care escalation,
+and medical emergencies in combination with IT automation, medical wearables and \Gls{iot}.
+The Scopus database was chosen for its extensive coverage of scholarly literature across multiple disciplines.
+
+For the search strategy, the following inclusion and exclusion criteria were employed to select relevant articles:
+
+Inclusion criteria:
+
+\begin{itemize}
+    \item Articles focusing on the utilization of medical wearable devices for remote patient monitoring
+    \item Articles addressing the automated calculation of early warning scores
+    \item Articles discussing the application of early warning scores outside of medical care facilities
+\end{itemize}
+
+Exclusion criteria:
+
+\begin{itemize}
+    \item Non-English language articles
+    \item Publications for which full-text access was not available
+    \item Duplicate articles
+    \item Articles outside of the \enquote{Computer Science} subject area
+\end{itemize}
+
+The following Scopus query was used to identify relevant literature:
+
+\begin{tcolorbox}[enhanced, center, width=0.95\linewidth, rounded corners=all, colframe=black!75!white, boxrule=0.5pt, colback=black!5!white]
+\begin{lstlisting}[language=SQL]
+TITLE-ABS-KEY(("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency")) OR ("early warning" OR "early warning score" OR "warning" OR "score*" OR "EWS") AND TITLE-ABS-KEY("system" OR "automat*" OR "smart*" OR "wearable*" OR "internet of thing*" OR "iot" OR "digital" OR "sensor*" OR "signal" OR "intelligen*" OR "predict*" OR "monitor*" OR "sreen*" OR "remote" OR "it" OR "comput*" OR "mobile" OR "5G" OR "network" (("vital*" OR "bio*") AND ("marker*" OR "sign*" OR "monitor*"))) AND TITLE-ABS-KEY("home" OR "domestic" OR "community" OR "remote" OR "longterm" OR "nursing" OR "rehabilitation" OR "outof*hospital" OR "telemedicine" OR "ehealth" OR "mhealth")
+\end{lstlisting}
+\end{tcolorbox}
+
+\subsubsection{Results}
+
+\begin{figure}[h]
+    \begin{center}
+        \includegraphics[width=.5\textwidth]{./figures/prisma-flowchart.png}
+        \caption{\label{prisma-flowchart}PRISMA flowchart showing screening and assessment of identified literature}
+    \end{center}
+\end{figure}
+
+An initial query on Scopus yielded a total of $N=1997$ records.
+After removing duplicates, $N=952$ records were excluded, resulting in $N=1045$ unique records.
+Upon screening the titles and abstracts, $N=963$ records did not meet the inclusion criteria, leaving $N=82$ articles to be assessed for
+eligibility in full text.
+Finally, after a thorough evaluation, $N=45$ articles were included for the literature review, providing insight into the current state of
+research on the use of smart medical devices for automated early warning score monitoring in patients transitioning from ambulant or
+hospital care.
+Figure \ref{prisma-flowchart} shows the literature assessment process. 
+The list of reviewed literature is shown in Tables \ref{tab:inclusion-table-1}, \ref{tab:inclusion-table-2} and \ref{tab:inclusion-table-3}.
+
+\begin{table}[!ht]
+    \centering
+    \begin{tcolorbox}[
+            enhanced, width=\linewidth, boxrule=2pt, arc=4pt,
+            tabularx={
+                >{\footnotesize}r
+                >{\footnotesize}X
+                >{\footnotesize}l
+            }
+        ]
+        \textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
+        \specialrule{2pt}{0em}{0em}
+        1 &
+        Internet of things enabled in-home health monitoring system using early warning score\cite{anzanpour_internet_2015} &
+        Anzanpour 2015 \\
+        \hline
+        2 &
+        Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things\cite{anzanpour_context-aware_2016} &
+        Anzanpour 2016 \\
+        \hline
+        3 &
+        An IoT based system for remote patient monitoring\cite{archip_iot_2016} &
+        Archip 2016 \\
+        \hline
+        4 &
+        Wireless sensor network-based smart room system for healthcare monitoring\cite{arnil_wireless_2011} &
+        Arnil 2011 \\
+        \hline
+        5 &
+        Design and Development of IOT Based Multi-Parameter Patient Monitoring System\cite{athira_design_2020} &
+        Athira 2020 \\
+        \hline
+        6 &
+        Medical warning system based on Internet of Things using fog computing\cite{azimi_medical_2016} &
+        Azimi 2016 \\
+        \hline
+        7 &
+        Self-aware early warning score system for IoT-based personalized healthcare\cite{azimi_self-aware_2017} &
+        Azimi 2017 \\
+        \hline
+        8 &
+        Review on IoT based Healthcare systems\cite{b_v_review_2022} &
+        Krishna 2022 \\
+        \hline
+        9 &
+        Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review\cite{burgos-esteban_effectiveness_2022} &
+        Burgos-Esteban 2022 \\
+        \hline
+        10 &
+        A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices\cite{chen_qrs_2017} &
+        Chen 2017 \\
+        \hline
+        11 &
+        Adopting the Internet of Things technologies in health care systems\cite{chiuchisan_adopting_2014} &
+        Chiuchisan 2014 \\
+        \hline
+        12 &
+        An Efficient Wireless Health Monitoring System\cite{chowdary_efficient_2018} &
+        Chowdary 2018 \\
+        \hline
+        13 &
+        DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration\cite{da_silva_deepsigns_2021} &
+        da Silva 2021 \\
+        \hline
+        14 &
+        Use of ultra-low cost fitness trackers as clinical monitors in low resource emergency departments\cite{dagan_use_2020} &
+        Dagan 2020 \\
+        \hline
+        15 &
+        A data fusion algorithm for clinically relevant anomaly detection in remote health monitoring\cite{de_mello_dantas_data_2020} &
+        de Mello Dantas 2020 \\
+        \hline
+        16 &
+        Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study\cite{downey_strengths_2017} &
+        Downey 2018 \\
+        \hline
+        17 &
+        Developing a real-time detection tool and an early warning score using a continuous wearable multi-parameter monitor\cite{eisenkraft_developing_2023} &
+        Eisenkraft 2023 \\
+        \hline
+        18 &
+        An IoT-Based Healthcare Platform for Patients in ICU Beds During the COVID-19 Outbreak\cite{filho_iot-based_2021} &
+        Filho 2021 \\
+    \end{tcolorbox}
+    \caption{\label{tab:inclusion-table-1}List of reviewed articles \textit{(Part 1 of 3)}}
+\end{table}
+
+\begin{table}[!ht]
+    \centering
+    \begin{tcolorbox}[
+            enhanced, width=\linewidth, boxrule=2pt, arc=4pt,
+            tabularx={
+                >{\footnotesize}r
+                >{\footnotesize}X
+                >{\footnotesize}l
+            }
+        ]
+        \textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
+        \specialrule{2pt}{0em}{0em}
+        19 &
+        Patient Monitoring System Based on Internet of Things\cite{gomez_patient_2016} &
+        Gomez 2016 \\
+        \hline
+        20 &
+        Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19\cite{gronbaek_continuous_2023} &
+        Gronbaek 2023 \\
+        \hline
+        21 &
+        Secure and lightweight privacy preserving Internet of things integration for remote patient monitoring\cite{imtyaz_ahmed_secure_2022} &
+        Imtyaz 2022 \\
+        \hline
+        22 &
+        Remote Continuous Health Monitoring System for Patients\cite{jagadish_remote_2018} &
+        Jagadish 2018 \\
+        \hline
+        23 &
+        Cost utility analysis of continuous and intermittent versus intermittent vital signs monitoring in patients admitted to surgical wards\cite{javanbakht_cost_2020} &
+        Javanbakht 2020 \\
+        \hline
+        24 &
+        Wearable sensors to improve detection of patient deterioration\cite{joshi_wearable_2019} &
+        Joshi 2019 \\
+        \hline
+        25 &
+        Intelligent Healthcare\cite{kale_intelligent_2021} &
+        Kale 2021 \\
+        \hline
+        26 &
+        A Hospital Healthcare Monitoring System Using Internet of Things Technologies\cite{karvounis_hospital_2021} &
+        Karvounis 2021 \\
+        \hline
+        27 &
+        All-day mobile healthcare monitoring system based on heterogeneous stretchable sensors for medical emergency\cite{lee_all-day_2020} &
+        Lee 2020 \\
+        \hline
+        28 &
+        Analysis of the early warning score to detect critical or high-risk patients in the prehospital setting\cite{martin-rodriguez_analysis_2019} &
+        Martin-Rodriguez 2019 \\
+        \hline
+        29 &
+        An IoT-based framework for early identification and monitoring of COVID-19 cases\cite{otoom_iot-based_2020} &
+        Otoom 2020 \\
+        \hline
+        30 &
+        A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home\cite{paganelli_conceptual_2022} &
+        Paganelli 2022 \\
+        \hline
+        31 &
+        Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges\cite{pahlevanynejad_personalized_2023} &
+        Pahlevanynejad 2023 \\
+        \hline
+        32 &
+        CuraBand: Health Monitoring and Warning System\cite{phaltankar_curaband_2021} &
+        Phaltankar 2021 \\
+        \hline
+        33 &
+        Internet of Things in Healthcare, A Literature Review\cite{quraishi_internet_2021} &
+        Quraishi 2021 \\
+        \hline
+        34 &
+        Vital Sign Monitoring System for Healthcare Through IoT Based Personal Service Application\cite{sahu_vital_2022} &
+        Sahu 2022 \\
+        \hline
+        35 &
+        Internet-of-Things-Enabled Early Warning Score System for Patient Monitoring\cite{sahu_internet--things-enabled_2022} &
+        Sahu 2022 \\
+        \hline
+        36 &
+        Cloud-Based Remote Patient Monitoring System with Abnormality Detection and Alert Notification\cite{sahu_cloud-based_2022} &
+        Sahu 2022 \\
+    \end{tcolorbox}
+    \caption{\label{tab:inclusion-table-2}List of reviewed articles \textit{(Part 2 of 3)}}
+\end{table}
+
+\begin{table}[!ht]
+    \centering
+    \begin{tcolorbox}[
+            enhanced, width=\linewidth, boxrule=2pt, arc=4pt,
+            tabularx={
+                >{\footnotesize}r
+                >{\footnotesize}X
+                >{\footnotesize}l
+            }
+        ]
+        \textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
+        \specialrule{2pt}{0em}{0em}
+        37 &
+        Remote patient monitoring using artificial intelligence: Current state, applications, and challenges\cite{shaik_remote_2023} &
+        Shaik 2023 \\
+        \hline
+        38 &
+        Prototype development of continuous remote monitoring of ICU patients at home\cite{thippeswamy_prototype_2021} &
+        Thippeswamy 2021 \\
+        \hline
+        39 &
+        IoT based Smart Healthcare Monitoring Systems: A Review\cite{tiwari_iot_2021} &
+        Tiwari 2021 \\
+        \hline
+        40 &
+        Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients\cite{un_observational_2021} &
+        Un 2021 \\
+        \hline
+        41 &
+        Adaptive threshold-based alarm strategies for continuous vital signs monitoring\cite{van_rossum_adaptive_2022} &
+        van Rossum 2022 \\
+        \hline
+        42 &
+        A retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach\cite{wu_predicting_2021} &
+        Wu 2021 \\
+        \hline
+        43 &
+        IoT based Real Time Health Monitoring\cite{yeri_iot_2020} &
+        Yeri 2020 \\
+        \hline
+        44 &
+        Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology\cite{youssef_ali_amer_vital_2020} &
+        Youssef Ali Amer 2020 \\
+        \hline
+        45 &
+        Features of electronic Early Warning systems which impact clinical decision making\cite{zarabzadeh_features_2012} &
+        Zarabzadeh 2012 \\
+    \end{tcolorbox}
+    \caption{\label{tab:inclusion-table-3}List of reviewed articles \textit{(Part 3 of 3)}}
+\end{table}
+
+% TODO for all outcomes, present and compare the findings of each study
+
+\subsubsection{Discussion}
 
 While the application of \Glspl{ews} in ambulant care facilities and hospitals has been thoroughly investigated,
 very little research has been done to assess their practicability for remote monitoring of at-risk patients at home.
+Furthermore, it was observed that previous research on the use of \Gls{iot}-devices for this purpose was largely conducted in
+experimental settings, limiting the generalizability of the results.
 Some studies have examined monitoring vital signs of at-home-patients for abnormalities,
-however in most of them, no automated \Gls{ews} calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
+however in most of them, no automated EWS calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
 In 2015, Anzanpour et al. developed a monitoring system which collects vitals data and calculates an \Gls{ews}, however due to limited or nonexistent
 availability of wireless sensors for all relevant vital signs, the work was limited to using a laboratory prototype
 and required manual interaction in transferring vitals data\cite{anzanpour_internet_2015}.
 Sahu et al. documented their development of an \Gls{ews}-supported digital early warning system using the PM6750\cite{sahu_internet--things-enabled_2022},
 an experimental vitals data monitoring device capable of taking continuous measurements in a laboratory setting\cite{noauthor_pm6750_nodate}.
-However, the methodology they used to calculate an \Gls{ews} in real-time with laboratory data is both inconsistent and weak.
+However, the methodology they used to calculate the \Gls{ews} in real-time with laboratory data is both inconsistent and weak.
 
-The availability of comprehensive, mobile vital signs monitoring equipment has seen a significant increase, especially in the wake of the SARS-CoV-2
+Recent studies indicate a growing trend towards investigating automated \Gls{ews} calculations in real-world scenarios\cite{downey_strengths_2017, karvounis_hospital_2021, b_v_review_2022, dagan_use_2020}.
+Notably, the availability of comprehensive, mobile vital signs monitoring equipment has seen a significant increase, especially in the wake of the SARS-CoV-2
 pandemic\cite{paganelli_conceptual_2022, filho_iot-based_2021, otoom_iot-based_2020, gronbaek_continuous_2023}.
-Since then, a variety of wearable medical sensors capable of continuously recording vital parameters have been developed and are
-commercially available\cite{noauthor_visi_nodate, noauthor_equivital_nodate, noauthor_vitls_nodate, noauthor_caretaker_nodate, noauthor_medtronic_nodate, noauthor_bpm_nodate, noauthor_worlds_nodate, noauthor_smart_nodate}.
 This surge in accessibility has paved the way for more extensive and continuous monitoring of patients in non-medical care settings.
-This demonstrates the evolving landscape of \Gls{rpm}, aiming to improve clinical outcomes and alleviate the burden on hospital wards.
+Moreover, there is a growing interest in incorporating machine learning algorithms to enhance the predictive capabilities of
+deterioration detection\cite{un_observational_2021, da_silva_deepsigns_2021, de_mello_dantas_data_2020}.
+This demonstrates the evolving landscape of remote patient monitoring, aiming to improve clinical outcomes and alleviate the
+burden on hospital wards.
 
+Despite the wealth of literature reviewed, no existing empirical studies evaluating the use of early warning scores for
+patients at home were identified.
+This highlights a crucial research gap and prompts the need for further investigation in this area, potentially warranting the development
+of an \Gls{ews} specialized for use outside of medical care facilities.
+
+\subsubsection{Interpretation of Results}
+
+Based on the findings, several key implications can be drawn.
+Firstly, the improved availability of smart sensors and the demonstrated effectiveness of \Glspl{ews} in predicting deterioration in direct
+medical care settings warrant research into their utilization at home.
 By remotely monitoring patients, it may be possible to identify early signs of deterioration, enabling earlier dismissal from
 hospital care and thereby freeing up valuable resources.
 Additionally, this approach holds the potential to reduce mortality rates and minimize the frequency of adverse clinical outcomes.
 
+However, it is important to acknowledge the lack of research on the use of \Glspl{ews} at home, which calls for a feasibility study in this
+specific context.
+This study would need to address challenges such as the frequency of measurements required and the absence of immediate diagnosis
+from qualified medical staff.
+Overcoming these obstacles is essential to ensure the safety and efficacy of automated remote patient monitoring in home-based settings.
+
+In conclusion, the literature review highlights the increasing interest in using smart medical devices and EWS for remote patient
+monitoring, particularly in real-world scenarios.
+The absence of studies evaluating the application of \Glspl{ews} for patients at home underscores the need for further investigation in this area.
+Conducting a feasibility study to explore the practicality and challenges of implementing \Glspl{ews} in home-based care would contribute
+significantly to the existing body of knowledge and help advance the field of automated early warning score monitoring in
+non-medical care settings.
+
+\subsection{Motivation}
+
+% TODO EWS makes prediction value better than monitoring abnormalities in single vital signs
 Installing and operating traditional continuous monitoring systems, like the vital sign monitors used in medical facilities, demands
 specialized equipment and technical expertise.
 Furthermore, these systems are cumbersome for patients, as they involve connecting patient and sensor device with numerous electrodes
@@ -215,16 +532,16 @@ to a single location.
 Conversely, battery-powered, wireless vitals monitoring devices, such as wearable armbands or smartwatches, can combine several
 biometric sensors into one device, allowing for a much higher degree of patient mobility, faster deployment and better
 scalability\cite{un_observational_2021}.
+Therefore, utilizing such devices for \Gls{rpm} is a suitable approach.
 
-In summary, with the current availability of wearable, networked biosensors and the validated effectiveness of \Glspl{ews} in medical facilities,
+In summary, with the current availability of wearable, networked biosensors and the validated effectiveness of EWS in medical facilities,
 combining both aspects presents an important and interesting research opportunity which could help reduce mortality and improve clinical
 outcomes for patients at risk of deterioration, both in their homes and on the go.
-Conducting a feasibility study to explore the practicality and challenges of implementing a system capable of remote \Gls{ews} calculation for mobile patients
-would contribute significantly to the existing body of knowledge and help advance the field of automated early warning score monitoring in
-non-medical care settings.
 
 \subsection{State of the problem}
 
+% Merge with Motivation?
+
 % There is a lack of software calculating MEWS with RPM
 
 The rapid advancements in wearable, networked biosensors have expanded the horizons of \Gls{rpm}.