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Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR Vision (2305.09675v2)

Published 9 May 2023 in cs.CY

Abstract: We consider the problem of reducing the time needed by healthcare professionals to understand patient medical history via the next generation of biomedical decision support. This problem is societally important because it has the potential to improve healthcare quality and patient outcomes. However, navigating electronic health records is challenging due to the high patient-doctor ratios, potentially long medical histories, the urgency of treatment for some medical conditions, and patient variability. The current electronic health record systems provides only a longitudinal view of patient medical history, which is time-consuming to browse, and doctors often need to engage nurses, residents, and others for initial analysis. To overcome this limitation, we envision an alternative spatial representation of patients' histories (e.g., electronic health records (EHRs)) and other biomedical data in the form of Atlas-EHR. Just like Google Maps allows a global, national, regional, and local view, the Atlas-EHR may start with an overview of the patient's anatomy and history before drilling down to spatially anatomical sub-systems, their individual components, or sub-components. Atlas-EHR presents a compelling opportunity for spatial computing since healthcare is almost a fifth of the US economy. However, the traditional spatial computing designed for geographic use cases (e.g., navigation, land-surveys, mapping) faces many hurdles in the biomedical domain. This paper presents a number of open research questions under this theme in five broad areas of spatial computing.

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References (154)
  1. Russell L Ackoff. 1971. Towards a system of systems concepts. Management science 17, 11 (1971), 661–671.
  2. The role of the Internet of Things in healthcare: Future trends and challenges. Computer methods and programs in biomedicine 199 (2021), 105903.
  3. Big data for health. IEEE journal of biomedical and health informatics 19, 4 (2015), 1193–1208.
  4. Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11, 5 (2021), e1424.
  5. Human Cell Atlas. 2024. What is the human cell type atlas? https://www.humancellatlas.org/learn-more/
  6. Spatio-temporal data mining: A survey of problems and methods. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–41.
  7. Spatial interaction of tumor cells and regulatory T cells correlates with survival in non-small cell lung cancer. Lung Cancer 117 (2018), 73–79.
  8. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nature Reviews Cardiology 18, 8 (2021), 581–599.
  9. Geoglam: A Geo Initiative on Global Agricultural Monitoring. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, July 22-27, 2018. IEEE, 8155–8157. https://doi.org/10.1109/IGARSS.2018.8517575
  10. Big data analytics in healthcare. BioMed research international 2015 (2015).
  11. Deep learning for AI. Commun. ACM 64, 7 (2021), 58–65. https://doi.org/10.1145/3448250
  12. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science 372, 6547 (2021), eaba2609.
  13. BioDigital. 2023. The BioDigital Human, An interactive 3D software platform for visualizing anatomy, disease, and treatments within the human body. https://www.biodigital.com/product/the-biodigital-human [Accessed: March 20, 2023].
  14. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).
  15. The dawn of spatial omics. Science 381, 6657 (2023), eabq4964.
  16. C Brunsdon et al. 1999. Some notes on parametric significance tests for geographically weighted regression. Journal of regional science 39, 3 (1999), 497–524.
  17. Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician) 47, 3 (1998), 431–443.
  18. A human cell atlas of fetal gene expression. Science 370, 6518 (2020), eaba7721.
  19. An overview on edge computing research. IEEE access 8 (2020), 85714–85728.
  20. The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Annals of gastroenterology: quarterly publication of the Hellenic Society of Gastroenterology 28, 2 (2015), 203.
  21. John M Carroll. 1997. Human-computer interaction: psychology as a science of design. Annual review of psychology 48, 1 (1997), 61–83.
  22. Grape detection with convolutional neural networks. Expert Systems with Applications 159 (2020), 113588.
  23. The rise of deep learning in drug discovery. Drug discovery today 23, 6 (2018), 1241–1250.
  24. Jiasi Chen and Xukan Ran. 2019. Deep learning with edge computing: A review. Proc. IEEE 107, 8 (2019), 1655–1674.
  25. A historical perspective of explainable Artificial Intelligence. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11, 1 (2021), e1391.
  26. Epic Systems Corporation. 2023. About Epic. https://www.epic.com/about [Accessed: March 20, 2023].
  27. Electronic health records to facilitate clinical research. Clinical Research in Cardiology 106 (2017), 1–9.
  28. Jeremy W Crampton and Jonh Krygier. 2018. An introduction to critical cartography. (2018).
  29. Generative adversarial networks: An overview. IEEE signal processing magazine 35, 1 (2018), 53–65.
  30. Big data in healthcare: management, analysis and future prospects. Journal of big data 6, 1 (2019), 1–25.
  31. Large scale distributed deep networks. Advances in neural information processing systems 25 (2012).
  32. Philip Dixon. 2001. Ripley’s K function.
  33. Explainable artificial intelligence: A survey. In 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, 0210–0215.
  34. Nathan Eddy. Nov 25, 2020. VR Surgery Helps Surgeons Train & Adapt. https://healthtechmagazine.net/article/2020/11/how-surgeons-use-vr-technology-train-and-adapt
  35. The nexus of food, energy, and water resources: Visions and challenges in spatial computing. In Advances in Geocomputation: Geocomputation 2015–The 13th International Conference. Springer, 5–20.
  36. Ring-shaped hotspot detection: A summary of results. In 2014 IEEE International Conference on Data Mining. IEEE, 815–820.
  37. Ahmed Eldawy and Mohamed F Mokbel. 2015a. The era of big spatial data: Challenges and opportunities. In 2015 16th IEEE International Conference on Mobile Data Management, Vol. 2. IEEE, 7–10.
  38. Ahmed Eldawy and Mohamed F Mokbel. 2015b. Spatialhadoop: A mapreduce framework for spatial data. In 2015 IEEE 31st international conference on Data Engineering. IEEE, 1352–1363.
  39. Esri. 2024. About Esri — The Science of Where. https://www.esri.com/en-us/about/about-esri/overview
  40. SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 2860–2870. https://doi.org/10.1145/3534678.3539168
  41. Towards Spatially Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data.. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM). SIAM.
  42. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. The Journal of Machine Learning Research 23, 1 (2022), 5232–5270.
  43. Stephen Few and Perceptual Edge. 2007. Data visualization: past, present, and future. IBM Cognos Innovation Center (2007), 1–12.
  44. High-resolution intersubject averaging and a coordinate system for the cortical surface. Human brain mapping 8, 4 (1999), 272–284.
  45. Spatio-temporal indexing in non-relational distributed databases. In 2013 IEEE International Conference on Big Data. IEEE, 291–299.
  46. Parul Gandhi and Jyoti Pruthi. 2020. Data visualization techniques: traditional data to big data. Data Visualization: Trends and Challenges Toward Multidisciplinary Perception (2020), 53–74.
  47. Ken Gardels. 1996. The Open GIS approach to distributed geodata and geoprocessing. In Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM. 21–25.
  48. DM Gavrila. 1994. R-tree index optimization. University of Maryland, Center for Automation Research, Computer Vision ….
  49. National Geographic. 2024. The underappreciated Man Behind the ”Best Graphic Ever Produced”. https://www.nationalgeographic.com/culture/article/charles-minard-cartography-infographics-history
  50. 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging. Communications Biology 6, 1 (2023), 718.
  51. Towards geographically robust statistically significant regional colocation pattern detection. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2022, Seattle, Washington, 1 November 2022, Taylor Anderson, Alexander Hohl, Joon-Seok Kim, and Ashwin Shashidharan (Eds.). ACM, 11–20. https://doi.org/10.1145/3557989.3566158
  52. An introduction to spatial data mining. UCGIS. (2020).
  53. Cloud computing and education: A state-of-the-art survey. Computers & Education 80 (2015), 132–151.
  54. GovInfo. 2024. Public Law 111 - 148 - Patient Protection and Affordable Care Act. https://www.govinfo.gov/app/details/PLAW-111publ148
  55. Inside Story: Indoor Air Quality. (2004).
  56. A spatially resolved timeline of the human maternal–fetal interface. Nature 619, 7970 (2023), 595–605.
  57. Edward S Grood and Wilfredo J Suntay. 1983. A joint coordinate system for the clinical description of three-dimensional motions: application to the knee. (1983).
  58. Deep learning for visual understanding: A review. Neurocomputing 187 (2016), 27–48.
  59. Spatial variability aware deep neural networks (SVANN): a general approach. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 6 (2021), 1–21.
  60. Construction of a human cell landscape at single-cell level. Nature 581, 7808 (2020), 303–309.
  61. A roadmap for the human developmental cell atlas. Nature 597, 7875 (2021), 196–205.
  62. Richard I Hartley and Peter Sturm. 1997. Triangulation. Computer vision and image understanding 68, 2 (1997), 146–157.
  63. K He et al. 2016. Deep residual learning for image recognition. In IEEE CVPR. 770–778.
  64. Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine 13 (2020), 69–76.
  65. Multiplexed imaging mass cytometry of the chemokine milieus in melanoma characterizes features of the response to immunotherapy. Science immunology 7, 70 (2022), eabk1692.
  66. Harry Hochheiser and Rupa S Valdez. 2020. Human-computer interaction, ethics, and biomedical informatics. Yearbook of Medical Informatics 29, 01 (2020), 093–098.
  67. Why is the electronic health record so challenging for research and clinical care? Methods of information in medicine 60, 01/02 (2021), 032–048.
  68. Wayne Horowitz. 1988. The Babylonian map of the world. Iraq 50 (1988), 147–165.
  69. HTAN. 2024. Human Tumor Atlas Network Overview. https://humantumoratlas.org/overview
  70. Discovering colocation patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and data engineering 16, 12 (2004), 1472–1485.
  71. IMDB. 2023. About Esri — The Science of Where. https://www.imdb.com/title/tt0060397/ [Accessed: March 20, 2023].
  72. National Cancer Institute. 2015. Immune checkpoint inhibitors. https://www.cancer.gov/about-cancer/treatment/types/immunotherapy/checkpoint-inhibitors. Immuno-Oncology 42 (2015), 55–66.
  73. Michael C Jackson and Paul Keys. 1984. Towards a system of systems methodologies. Journal of the operational research society 35, 6 (1984), 473–486.
  74. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nature cell biology 25, 8 (2023), 1089–1100.
  75. Prediction of protein–protein interaction using graph neural networks. Scientific Reports 12, 1 (2022), 8360.
  76. Focal-Test-Based Spatial Decision Tree Learning: A Summary of Results. In 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013, Hui Xiong, George Karypis, Bhavani Thuraisingham, Diane J. Cook, and Xindong Wu (Eds.). IEEE Computer Society, 320–329. https://doi.org/10.1109/ICDM.2013.96
  77. Using ArcGIS geostatistical analyst. Vol. 380. Esri Redlands.
  78. Abraham J Koster and Judith Klumperman. 2003. Electron microscopy in cell biology: integrating structure and function. Nature Reviews Molecular Cell Biology 4, 9; SUPP (2003), SS6–SS9.
  79. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012).
  80. Challenges and opportunities of big data in health care: a systematic review. JMIR medical informatics 4, 4 (2016), e5359.
  81. Machine learning for large-scale wearable sensor data in Parkinson’s disease: Concepts, promises, pitfalls, and futures. Movement disorders 31, 9 (2016), 1314–1326.
  82. An atlas of healthy and injured cell states and niches in the human kidney. bioRxiv. Preprint] 10, 2021.07 (2021), 28–454201.
  83. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 7777 (2019), 187–192.
  84. Deep learning. nature 521, 7553 (2015), 436–444.
  85. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278–2324.
  86. Spatial omics and multiplexed imaging to explore cancer biology. Nature methods 18, 9 (2021), 997–1012.
  87. The internet of things: a survey. Information systems frontiers 17 (2015), 243–259.
  88. SRNet: A spatial-relationship aware point-set classification method for multiplexed pathology images. 10 (2021).
  89. CSCD: towards spatially resolving the heterogeneous landscape of MxIF oncology data. In Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. 36–46.
  90. Yan Li and Shashi Shekhar. 2018. Local Co-location Pattern Detection: A Summary of Results. In 10th International Conference on Geographic Information Science, GIScience 2018, August 28-31, 2018, Melbourne, Australia (LIPIcs, Vol. 114). Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 10:1–10:15. https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.10
  91. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nature communications 6, 1 (2015), 8390.
  92. Task-adaptive meta-learning framework for advancing spatial generalizability. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 14365–14373.
  93. 3D digitization and prototyping of the skull for practical use in the teaching of human anatomy. Journal of medical systems 41 (2017), 1–5.
  94. Alexander Selvikvåg Lundervold and Arvid Lundervold. 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29, 2 (2019), 102–127.
  95. Big data application in biomedical research and health care: a literature review. Biomedical informatics insights 8 (2016), BII–S31559.
  96. Alan M MacEachren. 2004. How maps work: representation, visualization, and design. Guilford Press.
  97. Alan M MacEachren and Menno-Jan Kraak. 1997. Exploratory cartographic visualization: advancing the agenda. , 335–343 pages.
  98. Alan M MacEachren and DR Fraser Taylor. 2013. Visualization in modern cartography. Elsevier.
  99. I Scott MacKenzie. 2024. Human-computer interaction: An empirical research perspective. (2024).
  100. Towards a foundation model for geospatial artificial intelligence (vision paper). In Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 1–4.
  101. On the opportunities and challenges of foundation models for geospatial artificial intelligence. arXiv preprint arXiv:2304.06798 (2023).
  102. Donna Marbury. Nov 23, 2020. What Does the Future Hold for AR and VR in Healthcare? https://healthtechmagazine.net/article/2020/11/what-does-future-hold-ar-and-vr-healthcare
  103. Donna Marbury. Oct 4, 2022. From VR to XR: Advancements in Patient Care. https://healthtechmagazine.net/article/2022/10/vr-xr-advancements-patient-care
  104. Dan C Marinescu. 2022. Cloud computing: theory and practice. Morgan Kaufmann.
  105. Resolving the heterogeneous tumor-centric cellular neighborhood through multiplexed, spatial paracrine interactions in the setting of immune checkpoint blockade. Cancer research communications 2, 2 (2022), 78–89.
  106. Multiplexed imaging of human tuberculosis granulomas uncovers immunoregulatory features conserved across tissue and blood. Biorxiv (2020), 2020–06.
  107. Harvey J Miller. 2005. A measurement theory for time geography. Geographical analysis 37, 1 (2005), 17–45.
  108. Sparsh Mittal and Jeffrey S Vetter. 2015. A survey of CPU-GPU heterogeneous computing techniques. ACM Computing Surveys (CSUR) 47, 4 (2015), 1–35.
  109. Overview on wearable sensors for the management of Parkinson’s disease. npj Parkinson’s Disease 9, 1 (2023), 153.
  110. Human primary auditory cortex: cytoarchitectonic subdivisions and mapping into a spatial reference system. Neuroimage 13, 4 (2001), 684–701.
  111. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. Sensors 23, 2 (2023), 828.
  112. Brad A Myers. 1998. A brief history of human-computer interaction technology. interactions 5, 2 (1998), 44–54.
  113. S Nawaz and Y Yuan. 2016. Computational pathology: Exploring the spatial dimension of tumor ecology. Cancer letters 380, 1 (2016), 296–303.
  114. Cathy Newman. 2018. Digital cadavers are replacing real ones. But should they? https://www.nationalgeographic.com/science/article/digital-cadavers-are-replacing-real-ones-but-should-they-future-medicine [Accessed: March 20, 2023].
  115. Light-sheet microscopy: a tutorial. Advances in Optics and Photonics 10, 1 (2018), 111–179.
  116. Dev Oliver and Daniel J Steinberger. 2011. From geography to medicine: exploring innerspace via spatial and temporal databases. In Advances in Spatial and Temporal Databases: 12th International Symposium, SSTD 2011, Minneapolis, MN, USA, August 24-26, 2011, Proceedings 12. Springer, 467–470.
  117. Pariwat Ongsulee. 2017. Artificial intelligence, machine learning and deep learning. In 2017 15th international conference on ICT and knowledge engineering (ICT&KE). IEEE, 1–6.
  118. Optimizing multi-GPU parallelization strategies for deep learning training. Ieee Micro 39, 5 (2019), 91–101.
  119. Spatial components of molecular tissue biology. Nature Biotechnology 40, 3 (2022), 308–318.
  120. The transformational role of GPU computing and deep learning in drug discovery. Nature Machine Intelligence 4, 3 (2022), 211–221.
  121. The rise of consumer health wearables: promises and barriers. PLoS medicine 13, 2 (2016), e1001953.
  122. The future of healthcare internet of things: a survey of emerging technologies. IEEE Communications Surveys & Tutorials 22, 2 (2020), 1121–1167.
  123. QGIS. 2024. A Free and Open Source Geographic Information System. https://qgis.org/en/site/
  124. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 652–660.
  125. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017).
  126. GediNET for discovering gene associations across diseases using knowledge based machine learning approach. Scientific reports 12, 1 (2022), 19955.
  127. Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 11, 4 (2020), 193.
  128. The human cell atlas. elife 6 (2017), e27041.
  129. Erik L Ritman. 2011. Current status of developments and applications of micro-CT. Annual review of biomedical engineering 13 (2011), 531–552.
  130. Toward a common coordinate framework for the human body. Cell 179, 7 (2019), 1455–1467.
  131. The internet of things: An overview. The internet society (ISOC) 80 (2015), 1–50.
  132. The human tumor atlas network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 2 (2020), 236–249.
  133. The Human Cell Atlas: from vision to reality. Nature 550, 7677 (2017), 451–453.
  134. Machine learning and artificial intelligence in research and healthcare. Injury 54 (2023), S69–S73.
  135. Machine learning for healthcare wearable devices: the big picture. Journal of Healthcare Engineering 2022 (2022).
  136. The human lung cell atlas: a high-resolution reference map of the human lung in health and disease. American journal of respiratory cell and molecular biology 61, 1 (2019), 31–41.
  137. Lauren M Scott and Mark V Janikas. 2009. Spatial statistics in ArcGIS. In Handbook of applied spatial analysis: Software tools, methods and applications. Springer, 27–41.
  138. The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. Sensors 23, 23 (2023), 9498.
  139. Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, 3 (2011), 193–214.
  140. Spatial computing. Commun. ACM 59, 1 (2015), 72–81.
  141. Shashi Shekhar and Yan Huang. 2001. Discovering spatial co-location patterns: A summary of results. In International symposium on spatial and temporal databases. Springer, 236–256.
  142. Shashi Shekhar and Pamela Vold. 2020. Spatial computing. Mit Press.
  143. Deep learning in medical image analysis. Annual review of biomedical engineering 19 (2017), 221–248.
  144. Edge computing: Vision and challenges. IEEE internet of things journal 3, 5 (2016), 637–646.
  145. Loren Siebert. 2004. Using GIS to map rail network history. The Journal of Transport History 25, 1 (2004), 84–104.
  146. John P Snyder. 1978. The space oblique Mercator projection. Photogramm. Eng. Remote Sensing 44, 585-596 (1978), 140.
  147. The emerging field of mobile health. Science translational medicine 7, 283 (2015), 283rv3–283rv3.
  148. Recommendations for achieving interoperable and shareable medical data in the USA. Communications Medicine 2, 1 (2022), 86.
  149. Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Communications 40, 4 (2020), 135–153.
  150. Significant Linear Hotspot Discovery. IEEE Trans. Big Data 3, 2 (2017), 140–153. https://doi.org/10.1109/TBDATA.2016.2631518
  151. Federico Thomas and Lluís Ros. 2005. Revisiting trilateration for robot localization. IEEE Transactions on robotics 21, 1 (2005), 93–101.
  152. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA cardiology 3, 5 (2018), 409–416.
  153. Multi-UAV Collaborative Absolute Vision Positioning and Navigation: A Survey and Discussion. Drones 7, 4 (2023), 261.
  154. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 7835 (2020), 619–625.
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