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===Related work===
===Related work===
We begin by analyzing the existing systems that simply generate alerts every time one or more sensors cross the abnormality thresholds. Due to the sheer volume of such alerts, they are difficult to manage, even in the case of hospital in-patient settings, let alone for a much larger number of remotely monitored patients. Starting from some of the initial attempts reported by Anliker ''et al.''<ref name="AnlikerAMON04">{{cite journal |title=AMON: A wearable multiparameter medical monitoring and alert system |journal=IEEE Transactions on Information Technology in Biomedicine |author=Anliker, U.; Ward, J.A.; Lukowicz, P. |volume=8 |issue=4 |pages=415–27 |year=2004 |pmid=15615032}}</ref>, to more recent works from various researchers<ref name="BaigReal14">{{cite journal |title=Real-time vital signs monitoring and interpretation system for early detection of multiple physical signs in older adults |journal=Proceeding from the IEEE-EMBS International Conference on Biomedical and Health Informatics |author=Baig, M.M.; GholamHosseini, H.; Connolly, M.J. et al. |pages=355–8 |year=2014 |doi=10.1109/BHI.2014.6864376}}</ref><ref name="RajevencelthaImprov16">{{cite journal |title=Improving the performance of multi-parameter patient monitors using feature mapping and decision fusion |journal=Proceedings from the 2016 IEEE Region 10 Conference |author=Rajevenceltha, J.; Kumar, C.S.; Kimar, A.A. |pages=1515–8 |year=2016 |doi=10.1109/TENCON.2016.7848268}}</ref><ref name="SreejithAReal15">{{cite journal |title=A Real Time Patient Monitoring System for Heart Disease Prediction Using Random Forest Algorithm |journal=Advances in Signal Processing and Intelligent Recognition Systems |author=Sreejith, S.; Rahul, S.; Jisha, R.C. |volume=425 |pages=485–500 |year=2016 |doi=10.1007/978-3-319-28658-7_41}}</ref><ref name="SkubicAuto15">{{cite journal |title=Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment |journal=IEEE Journal of Translational Engineering in Health and Medicine |author=Skubic, M.; Guevara, R.D.; Rantz, M. |volume=3 |pages=1–11 |year=2015 |doi=10.1109/JTEHM.2015.2421499}}</ref>, the severity detection and alert generation is typically based either on predefined thresholds, or based on training of thresholds using machine learning followed by online classification of multi-sensor data. Very similar techniques of machine learning have also been used in fall detection [7, 8]. Hristoskova ''et al.'' [9] propose another system wherein patient conditions are mapped to medical conditions using ontology-driven methods, and alerts are generated based on corresponding risk stratification.  
We begin by analyzing the existing systems that simply generate alerts every time one or more sensors cross the abnormality thresholds. Due to the sheer volume of such alerts, they are difficult to manage, even in the case of hospital in-patient settings, let alone for a much larger number of remotely monitored patients. Starting from some of the initial attempts reported by Anliker ''et al.''<ref name="AnlikerAMON04">{{cite journal |title=AMON: A wearable multiparameter medical monitoring and alert system |journal=IEEE Transactions on Information Technology in Biomedicine |author=Anliker, U.; Ward, J.A.; Lukowicz, P. |volume=8 |issue=4 |pages=415–27 |year=2004 |pmid=15615032}}</ref>, to more recent works from various researchers<ref name="BaigReal14">{{cite journal |title=Real-time vital signs monitoring and interpretation system for early detection of multiple physical signs in older adults |journal=Proceeding from the IEEE-EMBS International Conference on Biomedical and Health Informatics |author=Baig, M.M.; GholamHosseini, H.; Connolly, M.J. et al. |pages=355–8 |year=2014 |doi=10.1109/BHI.2014.6864376}}</ref><ref name="RajevencelthaImprov16">{{cite journal |title=Improving the performance of multi-parameter patient monitors using feature mapping and decision fusion |journal=Proceedings from the 2016 IEEE Region 10 Conference |author=Rajevenceltha, J.; Kumar, C.S.; Kimar, A.A. |pages=1515–8 |year=2016 |doi=10.1109/TENCON.2016.7848268}}</ref><ref name="SreejithAReal15">{{cite journal |title=A Real Time Patient Monitoring System for Heart Disease Prediction Using Random Forest Algorithm |journal=Advances in Signal Processing and Intelligent Recognition Systems |author=Sreejith, S.; Rahul, S.; Jisha, R.C. |volume=425 |pages=485–500 |year=2016 |doi=10.1007/978-3-319-28658-7_41}}</ref><ref name="SkubicAuto15">{{cite journal |title=Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment |journal=IEEE Journal of Translational Engineering in Health and Medicine |author=Skubic, M.; Guevara, R.D.; Rantz, M. |volume=3 |pages=1–11 |year=2015 |doi=10.1109/JTEHM.2015.2421499}}</ref>, the severity detection and alert generation is typically based either on predefined thresholds, or based on training of thresholds using machine learning followed by online classification of multi-sensor data. Very similar techniques of machine learning have also been used in fall detection.<ref name="LopesTowards13">{{cite journal |title=Towards an autonomous fall detection and alerting system on a mobile and pervasive environment |journal=Telecommunications Systems |author=Lopes. I.C.; Vaidya, B.; Rodrigues, J.J.P.C. |volume=52 |issue=4 |pages=2299–310 |year=2013 |doi=10.1007/s11235-011-9534-0}}</ref><ref name="BalasubramanianDisco16">{{cite journal |title=Discovering Multidimensional Motifs in Physiological Signals for Personalized Healthcare |journal=EEE Journal of Selected Topics in Signal Processing |author=Balasubramanian, A.; Wang, J.; Prabhakaran, B. |volume=10 |issue=5 |pages=832–41 |year=2016 |doi=10.1109/JSTSP.2016.2543679}}</ref> Hristoskova ''et al.''<ref name="HristoskovaOnto14">{{cite journal |title=Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert |journal=Sensors |author=Hristoskova, A.; Sakkalis, V.; Zacharioudakis, G. et al. |volume=14 |issue=1 |pages=1598-628 |year=2014 |doi=10.3390/s140101598 |pmid=24445411 |pmc=PMC3926628}}</ref> propose another system wherein patient conditions are mapped to medical conditions using ontology-driven methods, and alerts are generated based on corresponding risk stratification.  


Even though there has been noticeable success in detection and diagnosis of specific disease conditions, most of these works have not explored the opportunity for personalized and precision diagnosis. In an extensive review of Big Data for Health, Andreu-Perez ''et al.'' [10] specifically emphasize the opportunity for stratified patient management and personalized health diagnostics, citing examples of customized blood pressure management [11]. More specifically, Bates ''et al.'' [12] discuss the utility of using analytics to predict adverse events, which could reduce the associated morbidity and mortality rates. Furthermore, Bates ''et al.'' [12] argue that patient data analytics based on early [[information]] supplied to the hospital prior to admission can result in better management of staffing and other hospital resources. One of the recent works in personalized criticality detection is reported in [13], which propose an analytical unit in which the Improved Particle Swarm Optimization (IPSO) algorithm is used to arrive at patient-specific threat ranges.  
Even though there has been noticeable success in detection and diagnosis of specific disease conditions, most of these works have not explored the opportunity for personalized and precision diagnosis. In an extensive review of Big Data for Health, Andreu-Perez ''et al.''<ref name="Andreu-PerezBigData15">{{cite journal |title=Big data for health |journal=IEEE Journal of Biomedical and Health Informatics |author=Andreu-Perez, J.; Poon, C.C.; Merrifield, R.D. et al. |volume=19 |issue=4 |pages=1193-208 |year=2015 |doi=10.1109/JBHI.2015.2450362 |pmid=26173222}}</ref> specifically emphasize the opportunity for stratified patient management and personalized health diagnostics, citing examples of customized blood pressure management.<ref name="LiuAttenu14">{{cite journal |title=Attenuation of systolic blood pressure and pulse transit time hysteresis during exercise and recovery in cardiovascular patients |journal=IEEE Transactions on Bio-medical engineering |author=Liu, Q.; Yan, B.P.; Yu, C.M. et al. |volume=61 |issue=2 |pages=346–52 |year=2014 |doi=10.1109/TBME.2013.2286998 |pmid=24158470}}</ref> More specifically, Bates ''et al.''<ref name="BatesBigData14">{{cite journal |title=Big data in health care: using analytics to identify and manage high-risk and high-cost patients |journal=Health Affairs |author=Bates, D.W.; Saria, S.; Ohno-Machado, L. et al. |volume=33 |issue=7 |pages=1123-31 |year=2014 |doi=10.1377/hlthaff.2014.0041 |pmid=25006137}}</ref> discuss the utility of using analytics to predict adverse events, which could reduce the associated morbidity and mortality rates. The authors further argue that patient data analytics based on early [[information]] supplied to the hospital prior to admission can result in better management of staffing and other hospital resources.<ref name="BatesBigData14" /> One of the recent works in personalized criticality detection is reported by Sung ''et al.''<ref name="SungMobile14">{{cite journal |title=Mobile Physiological Measurement Platform With Cloud and Analysis Functions Implemented via IPSO |journal=IEEE Sensors Journal |author=Sung, W.-T.; Chen, J.-H.; Chang, K.-W. |volume=14 |issue=1 |pages=111–23 |year=2014 |doi=10.1109/JSEN.2013.2280398}}</ref>, who propose an analytical unit in which the Improved Particle Swarm Optimization (IPSO) algorithm is used to arrive at patient-specific threat ranges.  


To improve precision in diagnosis we also need to arrive at a balance between a completely automated system on one hand, and physician assist systems on the other. Celler ''et al.'' [14] propose a balanced approach wherein sophisticated analytics are presented to physicians, who in turn identify the changes and decide on the diagnosis. This is also supported by many results, including that reported in [6], wherein domain knowledge-based methods performed as well as other trained machine learning models. These arguments and results provide further impetus for personalized, precision, and preventive diagnostic techniques that are amenable to physician interventions.
To improve precision in diagnosis we also need to arrive at a balance between a completely automated system on one hand, and physician assist systems on the other. Celler ''et al.''<ref name="CellerHome15">{{cite journal |title=Home telemonitoring of vital signs--technical challenges and future directions |journal=IEEE Journal of Biomedical and Health Informatics |author=Celler, B.G.; Sparks, R.S. |volume=19 |issue=1 |pages=82–91 |year=2015 |doi=10.1109/JBHI.2014.2351413 |pmid=25163076}}</ref> propose a balanced approach wherein sophisticated analytics are presented to physicians, who in turn identify the changes and decide on the diagnosis. This is also supported by many results, including those reported by Skubic ''et al.''<ref name="SkubicAuto15" />, wherein domain knowledge-based methods performed as well as other trained machine learning models. These arguments and results provide further impetus for personalized, precision, and preventive diagnostic techniques that are amenable to physician interventions.


==References==
==References==

Revision as of 23:31, 4 February 2019

Full article title Data to diagnosis in global health: A 3P approach
Journal BMC Medical Informatics and Decision Making
Author(s) Pathinarupothi, Rahul Krishnan; Durga, P.; Rangan, Ekanath Srihari
Author affiliation(s) Amrita School of Engineering, Amrita Institute of Medical Science
Primary contact Email: rahulkrishnan @ am dot amrita dot edu
Year published 2018
Volume and issue 18
Page(s) 78
DOI 10.1186/s12911-018-0658-y
ISSN 1472-6947
Distribution license Creative Commons Attribution 4.0 International
Website https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0658-y
Download https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-018-0658-y (PDF)
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Abstract

Background: With connected medical devices fast becoming ubiquitous in healthcare monitoring, there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge.

Methods: To address this challenge, we present a "3P" approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is "physician assist filters" (PAF) that 1. transform unwieldy multi-sensor time series data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for a patient’s personalized condition, and 2. help in identifying and subsequently predictively alerting the onset of critical conditions. The output of PAFs is a clinically useful, yet extremely succinct summary of a patient’s medical condition, represented as a motif, which could be sent to remote doctors even over SMS, reducing the need for data bandwidths. We evaluate the clinical validity of these techniques using support-vector machine (SVM) learning models measuring both the predictive power and its ability to classify disease condition. We used more than 16,000 minutes of patient data (N=70) from the openly available MIMIC II database for conducting these experiments. Furthermore, we also report the clinical utility of the system through doctor feedback from a large super-speciality hospital in India.

Results: The results show that the RASPRO motifs perform as well as (and in many cases better than) raw time series data. In addition, we also see improvement in diagnostic performance using optimized sensor severity threshold ranges set using the personalization PAF severity quantizer.

Conclusion: The RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications, especially in remote patient monitoring. The personalization, precision, and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of the 3Ps, thereby providing the advantages of "3As": availability, affordability, and accessibility in the global health scenario.

Keywords: precision medicine, medical informatics, personalized healthcare, motif summarization

Background

Precision medicine and personalized healthcare are quickly gaining wide research interest as well as initial acceptance among the medical community. This is facilitated by the availability of ubiquitous data sources such as wearable sensors, smartphones, and internet of things (IoT) devices, along with machine learning and large-scale data analytics tools, resulting in promising outcomes in some of the niche medical domains. Our research particularly focuses on introducing the three Ps: precision, personalization, and preventive diagnosis in remote healthcare monitoring of patients, especially in a global health scenario. In our system, patients in remote areas use wearable devices to capture their vital parameters such as blood pressure (BP), blood glucose, oxygen saturation (SpO2), electro cardiographs (ECG) etc., and transmit them to doctors in tertiary care hospitals, who in turn are expected to suggest suitably needed timely interventions. While deploying our system in the highly populous region of southern India, we found that although this promises to provide hitherto unavailable healthcare services to a critically ill and aging population, particularly in the developing world, there are significant roadblocks in our expectation that doctors embrace this new paradigm in handling patients. The doctors, who are already overloaded, feel even more overwhelmed by the voluminous data flooding in from remote patients’ sensors. Furthermore, interpreting such incoming multi-parameter data simultaneously from a multitude of remote patients is time-consuming and soon transforms into an unmanageable deluge.

Approach

In this paper, we propose novel approaches to transform data into diagnosis. As a collaborative work between our researchers and clinicians in one of the largest super-specialty hospitals in India (Amrita Institute of Medical Sciences - AIMS), we developed physician assist filters (PAFs) that are designed to transform unwieldy time series sensor data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for patient’s personalized condition at hand, and help in identifying and subsequently predictively alerting the onset of critical conditions. Together with the communication network and data transmission architecture, this new framework that we have designed, developed, and successfully deployed is called RASPRO (Rapid Active Summarization for effective PROgnosis) and was first introduced in 2016 IEEE Wireless Health.[1]

Related work

We begin by analyzing the existing systems that simply generate alerts every time one or more sensors cross the abnormality thresholds. Due to the sheer volume of such alerts, they are difficult to manage, even in the case of hospital in-patient settings, let alone for a much larger number of remotely monitored patients. Starting from some of the initial attempts reported by Anliker et al.[2], to more recent works from various researchers[3][4][5][6], the severity detection and alert generation is typically based either on predefined thresholds, or based on training of thresholds using machine learning followed by online classification of multi-sensor data. Very similar techniques of machine learning have also been used in fall detection.[7][8] Hristoskova et al.[9] propose another system wherein patient conditions are mapped to medical conditions using ontology-driven methods, and alerts are generated based on corresponding risk stratification.

Even though there has been noticeable success in detection and diagnosis of specific disease conditions, most of these works have not explored the opportunity for personalized and precision diagnosis. In an extensive review of Big Data for Health, Andreu-Perez et al.[10] specifically emphasize the opportunity for stratified patient management and personalized health diagnostics, citing examples of customized blood pressure management.[11] More specifically, Bates et al.[12] discuss the utility of using analytics to predict adverse events, which could reduce the associated morbidity and mortality rates. The authors further argue that patient data analytics based on early information supplied to the hospital prior to admission can result in better management of staffing and other hospital resources.[12] One of the recent works in personalized criticality detection is reported by Sung et al.[13], who propose an analytical unit in which the Improved Particle Swarm Optimization (IPSO) algorithm is used to arrive at patient-specific threat ranges.

To improve precision in diagnosis we also need to arrive at a balance between a completely automated system on one hand, and physician assist systems on the other. Celler et al.[14] propose a balanced approach wherein sophisticated analytics are presented to physicians, who in turn identify the changes and decide on the diagnosis. This is also supported by many results, including those reported by Skubic et al.[6], wherein domain knowledge-based methods performed as well as other trained machine learning models. These arguments and results provide further impetus for personalized, precision, and preventive diagnostic techniques that are amenable to physician interventions.

References

  1. ↑ Pathinarupothi, R.K.; Rangan, E.S.; Alangot, B. et al. (2016). "RASPRO: Rapid summarization for effective prognosis in wireless remote health monitoring". 2016 IEEE Wireless Health: 1–6. doi:10.1109/WH.2016.7764566. 
  2. ↑ Anliker, U.; Ward, J.A.; Lukowicz, P. (2004). "AMON: A wearable multiparameter medical monitoring and alert system". IEEE Transactions on Information Technology in Biomedicine 8 (4): 415–27. PMID 15615032. 
  3. ↑ Baig, M.M.; GholamHosseini, H.; Connolly, M.J. et al. (2014). "Real-time vital signs monitoring and interpretation system for early detection of multiple physical signs in older adults". Proceeding from the IEEE-EMBS International Conference on Biomedical and Health Informatics: 355–8. doi:10.1109/BHI.2014.6864376. 
  4. ↑ Rajevenceltha, J.; Kumar, C.S.; Kimar, A.A. (2016). "Improving the performance of multi-parameter patient monitors using feature mapping and decision fusion". Proceedings from the 2016 IEEE Region 10 Conference: 1515–8. doi:10.1109/TENCON.2016.7848268. 
  5. ↑ Sreejith, S.; Rahul, S.; Jisha, R.C. (2016). "A Real Time Patient Monitoring System for Heart Disease Prediction Using Random Forest Algorithm". Advances in Signal Processing and Intelligent Recognition Systems 425: 485–500. doi:10.1007/978-3-319-28658-7_41. 
  6. ↑ 6.0 6.1 Skubic, M.; Guevara, R.D.; Rantz, M. (2015). "Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment". IEEE Journal of Translational Engineering in Health and Medicine 3: 1–11. doi:10.1109/JTEHM.2015.2421499. 
  7. ↑ Lopes. I.C.; Vaidya, B.; Rodrigues, J.J.P.C. (2013). "Towards an autonomous fall detection and alerting system on a mobile and pervasive environment". Telecommunications Systems 52 (4): 2299–310. doi:10.1007/s11235-011-9534-0. 
  8. ↑ Balasubramanian, A.; Wang, J.; Prabhakaran, B. (2016). "Discovering Multidimensional Motifs in Physiological Signals for Personalized Healthcare". EEE Journal of Selected Topics in Signal Processing 10 (5): 832–41. doi:10.1109/JSTSP.2016.2543679. 
  9. ↑ Hristoskova, A.; Sakkalis, V.; Zacharioudakis, G. et al. (2014). "Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert". Sensors 14 (1): 1598-628. doi:10.3390/s140101598. PMC PMC3926628. PMID 24445411. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3926628. 
  10. ↑ Andreu-Perez, J.; Poon, C.C.; Merrifield, R.D. et al. (2015). "Big data for health". IEEE Journal of Biomedical and Health Informatics 19 (4): 1193-208. doi:10.1109/JBHI.2015.2450362. PMID 26173222. 
  11. ↑ Liu, Q.; Yan, B.P.; Yu, C.M. et al. (2014). "Attenuation of systolic blood pressure and pulse transit time hysteresis during exercise and recovery in cardiovascular patients". IEEE Transactions on Bio-medical engineering 61 (2): 346–52. doi:10.1109/TBME.2013.2286998. PMID 24158470. 
  12. ↑ 12.0 12.1 Bates, D.W.; Saria, S.; Ohno-Machado, L. et al. (2014). "Big data in health care: using analytics to identify and manage high-risk and high-cost patients". Health Affairs 33 (7): 1123-31. doi:10.1377/hlthaff.2014.0041. PMID 25006137. 
  13. ↑ Sung, W.-T.; Chen, J.-H.; Chang, K.-W. (2014). "Mobile Physiological Measurement Platform With Cloud and Analysis Functions Implemented via IPSO". IEEE Sensors Journal 14 (1): 111–23. doi:10.1109/JSEN.2013.2280398. 
  14. ↑ Celler, B.G.; Sparks, R.S. (2015). "Home telemonitoring of vital signs--technical challenges and future directions". IEEE Journal of Biomedical and Health Informatics 19 (1): 82–91. doi:10.1109/JBHI.2014.2351413. PMID 25163076. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. Grammar and punctuation was edited to American English, and in some cases additional context was added to text when necessary. In some cases important information was missing from the references, and that information was added.

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