A Focused Review of Deep Phenotyping with Examples from Neurology
Keywords:
Phenotype, ontology, precision medicine, disease, disease variant, patient similarity, patient distance, high-throughput phenotyping, electronic health records, deep phenotyping
Abstract
A deep phenotype is the detailed description of the observable signs and symptoms, the mode of onset, the clinical course, and the response to treatment that characterizes a human disease. With advances in highthroughput phenotyping based on natural language processing and other automated algorithms, it is possible to calculate the distances between cohorts of patients and calculate the distances between individual patients. Vector representations of phenotypes allows the quantitative characterization of disease phenotypes, helps to identify phenotypic features with the highest diagnostic value, facilitate the recognition of disease variants, and supports progress towards precision medicine. This focused review introduces the underlying concepts of deep phenotype, ontology, disease repository, disease mimic, disease chameleon, patient distance, computable phenotype, and phenomics and provides illustrative examples from neurology. In closing, future advances in the application of deep phenotyping will depend on improved methods for the high-throughput phenotyping of large numbers of patients based on the unstructured text that is held in electronic health records.
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References
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32. Jin, L. (2021). Welcome to the Phenomics journal. Phenomics, 1, 1-2. Retrieved from https://doi.org/10.1007/s43657-020-00009-4
33. Khachaturian, Z. S. (2011). Revised criteria for diagnosis of Alzheimer's disease: National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 3(7), 253-256.
34. Khare, S., & Seth, D. (2015). Lhermitte's sign: the current status. Annals of Indian Academy of Neurology, 18(2), 154.
35. Kuhn, M., & Johnson, K. (2019). Feature engineering and selection: A practical approach for predictive models. CRC Press.
36. Kohler, S., Schulz, M. H., Krawitz, P., Bauer, S., D¨olken, S., Ott, C. E., et al. (2009). Clinical diagnostics in human genetics with semantic similarity searches in ontologies. The American Journal of Human Genetics, 85(4), 457–464. Retrieved from https://dx.doi.org/10.1016/j.ajhg.2009.09.003
DOI: 10.1016/j.ajhg.2009.09.003
37. Laforce Jr, R. (2013). Behavioral and language variants of frontotemporal dementia: a review of key symptoms. Clinical Neurology and Neurosurgery, 115(12), 2405–2410.
38. Liu, M., Cohen, E. J., Brewer, G. J., & Laibson, P. R. (2002). Kayser-Fleischer ring as the presenting sign of Wilson disease. American Journal of Ophthalmology, 133(6), 832–834.
39. Magid, C. S. (2001). Developing tolerance for ambiguity. JAMA, 285(1), 88–88.
40. Maiella, S., Rath, A., Angin, C., Mousson, F., & Kremp, O. (2013). Orphanet and its consortium: where to find expert-validated information on rare diseases. Revue neurologique, 169, S3-8.
41. McKeith, I. G., Boeve, B. F., Dickson, D. W., Halliday, G., Taylor, J.-P., Weintraub, D., et al. (2017). Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB consortium. Neurology, 89(1), 88–100.
42. McKhann, G. M., Albert, M. S., Grossman, M., Miller, B., Dickson, D., & Trojanowski, J. Q. (2001). Clinical and pathological diagnosis of frontotemporal dementia: report of the workgroup on frontotemporal dementia and pick's disease. Archives of neurology, 58(11), 1803–1809.
43. McKusick, V. A. (2007). Mendelian Inheritance in Man and its online version, OMIM. American Journal of Human Genetics, 80(4), 588.
44. MedicineNet. (2021). Pathognomonic.
https://www.medicinenet.com/pathognomonic/definition.htm.
45. Parimbelli, E., Marini, S., Sacchi, L., & Bellazzi, R. (2018). Patient similarity for precision medicine: A systematic review. Journal of biomedical informatics, 83, 87–96.
46. Robinson, P. N. (2012). Deep phenotyping for precision medicine. Human Mutation, 33(5), 777–780.
47. Robinson, P. N., & Mundlos, S. (2010). The human phenotype ontology. Clinical Genetics, 77(6), 525–534.
48. SNOMED CT. (2021). NCBO BioPortal. https://bioportal.bioontology.org/ontologies/SNOMEDCT.
49. Sommer, N., Melms, A., Weller, M., & Dichgans, J. (1993). Ocular myasthenia gravis. Documenta Ophthalmologica, 84(4), 309–333.
50. Tasker, R. C. (2017). Why everyone should care about "computable phenotypes." Pediatric critical care medicine: a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 18(5), 489.
51. Turner, M. R., & Talbot, K. (2013). Mimics and chameleons in motor neurone disease. Practical neurology, 13(3), 153-164.
52. Weng, C., Shah, N. H., & Hripcsak, G. (2020). Deep phenotyping: embracing complexity and temporality—towards scalability, portability, and interoperability. Journal of biomedical informatics, 105, 103433.
53. Wunsch, D. C., & Hier, D. B. (2021). Subsumption reduces dataset dimensionality without decreasing the performance of a machine learning classifier. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021, 1622-1625.
54. Yang, H., Robinson, P. N., & Wang, K. (2015). Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nature Methods, 12(9), 841–843.
55. Yehia, L., & Eng, C. (2019). Largescale population genomics versus deep phenotyping: brute force or elegant pragmatism towards precision medicine. Nature Publishing Group.
56. Yu, S., Liao, K. P., Shaw, S. Y., Gainer, V. S., Churchill, S. E., Szolovits, P., Cai, T. (2015). Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. Journal of the American Medical Informatics Association, 22(5), 993–1000.
57. Zheng, N. S., Feng, Q., Kerchberger, V. E., Zhao, J., Edwards, T. L., Cox, N. J., Wei, W.-Q. (2020). Phemap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records. Journal of the American Medical Informatics Association, 27(11), 1675–1687.
2. Andrews, J. E., Richesson, R. L., & Krischer, J. (2007). Variation of SNOMED CT coding of clinical research concepts among coding experts. Journal of the American Medical Informatics Association, 14 (4), 497–506.
3. Aronowitz, R. A. (2001). When do symptoms become a disease? Annals of Internal Medicine, 134 (9), 803–808.
4. Azhagusundari, B., Thanamani, A. S., et al. (2013). Feature selection based on information gain. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2 (2), 18–21.
5. Barrows, H. S., & Bennett, K. (1972). The neurologist's diagnostic (problem-solving) skill: experimental studies and their implications for neurological training. Archives of Neurology, 26, 273-277.
6. Bodenreider, O. (2004). The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Research, 32 (suppl 1), D267–D270.
7. Brown, S.-A. (2016). Patient similarity: emerging concepts in systems and precision medicine. Frontiers in physiology, 7, 561.
8. Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., . . . others (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562 (7726), 203–209.
9. Caplan, L. (2021). Caplan-Fisher rules. Stroke, 52 (5), e155–e159.
10. Caplan, L. R. (1982). Fisher's rules. Arch Neurol, 39 (7), 389–390.
11. Chen, Y., Carroll, R. J., Hinz, E. R. M., Shah, A., Eyler, A. E., Denny, J. C., & Xu, H. (2013). Applying active learning to high-throughput phenotyping algorithms for electronic health records data. Journal of the American Medical Informatics Association, 20 (e2), e253–e259.
12. Chiang, M. F., Hwang, J. C., Alexander, C. Y., Casper, D. S., Cimino, J. J., & Starren, J. (2006). Reliability of SNOMED-CT coding by three physicians using two terminology browsers. In Amia annual symposium proceedings (Vol. 2006, p. 131).
13. Choi, S.-S., Cha, S.-H., & Tappert, C. C. (2010). A survey of binary similarity and distance measures. Journal of systemics, cybernetics, and informatics, 8 (1), 43–48.
14. Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372 (9), 793–795.
15. Curcin, V. (2020). Why does human phenomics matter today? Learning Health Systems, 4 (4).
16. Delude, C. M. (2015). Deep phenotyping: the details of the disease. Nature, 527 (7576), S14–S15.
17. Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., & Zupan, B. (2013). Orange: data mining toolbox in Python. The Journal of Machine Learning research, 14(1), 2349-2353. Retrieved from http://jmlr.org/papers/v14/demsar13a.html
18. Fellner, A., Ruhrman-Shahar, N., Orenstein, N., Lidzbarsky, G., Shuldiner, A. R.,
19. Gonzaga-Jauregui, C., Basel-Salmon, L. (2021). The role of phenotype-based search approaches using public online databases in diagnostics of mendelian disorders. Genetics in Medicine, 23 (6), 1095–1100.
20. Fu, S., Chen, D., He, H., Liu, S., Moon, S., Peterson, K. J., and others (2020). Clinical concept extraction: a methodology review. Journal of Biomedical Informatics, 103526.
21. Gehrmann, S., Dernoncourt, F., Li, Y., Carlson, E. T., Wu, J. T., Welt, J. (2018). Comparing deep learning and concept extraction-based methods for patient phenotyping from clinical narratives. PloS one, 13 (2), e0192360.
22. Girdea, M., Dumitriu, S., Fiume, M., Bowdin, S., Boycott, K. M., Chenier, S., et al. (2013). Phenotips: Patient phenotyping software for clinical and research use. Human Mutation, 34 (8), 1057–1065.
23. Grad, L. I., Rouleau, G. A., Ravits, J., & Cashman, N. R. (2017). Clinical spectrum of amyotrophic lateral sclerosis (ALS). Cold Spring Harbor Perspectives in Medicine, 7 (8), a024117.
24. Groza, T., Kohler, S., Moldenhauer, D., Vasilevsky, N., Baynam, G., Zemojtel, T., . . . others (2015). The human phenotype ontology: semantic unification of common and rare disease. The American Journal of Human Genetics, 97 (1), 111–124.
25. Gruber, T. (2018). Ontology. https://queksiewkhoon.tripod.com/ontology01.pdf.
26. Hier, D. B., & Brint, S. U. (2020). A neuro-ontology for the neurological examination. BMC Medical Informatics and Decision Making, 20(1), 1–9.
27. Hier, D. B., Kopel, J., Brint, S. U., Wunsch, D. C., Olbricht, G. R., Azizi, S., & Allen, B. (2020). Evaluation of standard and semantically-augmented distance metrics for neurology patients. BMC Medical Informatics and Decision Making, 20(1), 1–15.
28. Houle, D., Govindaraju, D. R., & Omholt, S. (2010). Phenomics: the next challenge. Nature Reviews Genetics, 11(12), 855–866.
29. Hucklenbroich, P. (2014). "Disease entity" as the key theoretical concept of medicine. In The Journal of medicine and philosophy: A forum for bioethics and philosophy of medicine (Vol. 39, p. 609-633).
30. Janeway, E. G. (1884, 08). Limitations of Pathognomonic signs and symptoms. Journal of the American Medical Association, III(5), 116-120. Retrieved from
https://doi.org/10.1001/jama.1884.02390540004001a
DOI: 10.1001/jama.1884.02390540004001a
31. Jepsen, T. C. (2009). Just what is an ontology, anyway? IT Professional Magazine, 11(5), 22.
32. Jin, L. (2021). Welcome to the Phenomics journal. Phenomics, 1, 1-2. Retrieved from https://doi.org/10.1007/s43657-020-00009-4
33. Khachaturian, Z. S. (2011). Revised criteria for diagnosis of Alzheimer's disease: National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 3(7), 253-256.
34. Khare, S., & Seth, D. (2015). Lhermitte's sign: the current status. Annals of Indian Academy of Neurology, 18(2), 154.
35. Kuhn, M., & Johnson, K. (2019). Feature engineering and selection: A practical approach for predictive models. CRC Press.
36. Kohler, S., Schulz, M. H., Krawitz, P., Bauer, S., D¨olken, S., Ott, C. E., et al. (2009). Clinical diagnostics in human genetics with semantic similarity searches in ontologies. The American Journal of Human Genetics, 85(4), 457–464. Retrieved from https://dx.doi.org/10.1016/j.ajhg.2009.09.003
DOI: 10.1016/j.ajhg.2009.09.003
37. Laforce Jr, R. (2013). Behavioral and language variants of frontotemporal dementia: a review of key symptoms. Clinical Neurology and Neurosurgery, 115(12), 2405–2410.
38. Liu, M., Cohen, E. J., Brewer, G. J., & Laibson, P. R. (2002). Kayser-Fleischer ring as the presenting sign of Wilson disease. American Journal of Ophthalmology, 133(6), 832–834.
39. Magid, C. S. (2001). Developing tolerance for ambiguity. JAMA, 285(1), 88–88.
40. Maiella, S., Rath, A., Angin, C., Mousson, F., & Kremp, O. (2013). Orphanet and its consortium: where to find expert-validated information on rare diseases. Revue neurologique, 169, S3-8.
41. McKeith, I. G., Boeve, B. F., Dickson, D. W., Halliday, G., Taylor, J.-P., Weintraub, D., et al. (2017). Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB consortium. Neurology, 89(1), 88–100.
42. McKhann, G. M., Albert, M. S., Grossman, M., Miller, B., Dickson, D., & Trojanowski, J. Q. (2001). Clinical and pathological diagnosis of frontotemporal dementia: report of the workgroup on frontotemporal dementia and pick's disease. Archives of neurology, 58(11), 1803–1809.
43. McKusick, V. A. (2007). Mendelian Inheritance in Man and its online version, OMIM. American Journal of Human Genetics, 80(4), 588.
44. MedicineNet. (2021). Pathognomonic.
https://www.medicinenet.com/pathognomonic/definition.htm.
45. Parimbelli, E., Marini, S., Sacchi, L., & Bellazzi, R. (2018). Patient similarity for precision medicine: A systematic review. Journal of biomedical informatics, 83, 87–96.
46. Robinson, P. N. (2012). Deep phenotyping for precision medicine. Human Mutation, 33(5), 777–780.
47. Robinson, P. N., & Mundlos, S. (2010). The human phenotype ontology. Clinical Genetics, 77(6), 525–534.
48. SNOMED CT. (2021). NCBO BioPortal. https://bioportal.bioontology.org/ontologies/SNOMEDCT.
49. Sommer, N., Melms, A., Weller, M., & Dichgans, J. (1993). Ocular myasthenia gravis. Documenta Ophthalmologica, 84(4), 309–333.
50. Tasker, R. C. (2017). Why everyone should care about "computable phenotypes." Pediatric critical care medicine: a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 18(5), 489.
51. Turner, M. R., & Talbot, K. (2013). Mimics and chameleons in motor neurone disease. Practical neurology, 13(3), 153-164.
52. Weng, C., Shah, N. H., & Hripcsak, G. (2020). Deep phenotyping: embracing complexity and temporality—towards scalability, portability, and interoperability. Journal of biomedical informatics, 105, 103433.
53. Wunsch, D. C., & Hier, D. B. (2021). Subsumption reduces dataset dimensionality without decreasing the performance of a machine learning classifier. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021, 1622-1625.
54. Yang, H., Robinson, P. N., & Wang, K. (2015). Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nature Methods, 12(9), 841–843.
55. Yehia, L., & Eng, C. (2019). Largescale population genomics versus deep phenotyping: brute force or elegant pragmatism towards precision medicine. Nature Publishing Group.
56. Yu, S., Liao, K. P., Shaw, S. Y., Gainer, V. S., Churchill, S. E., Szolovits, P., Cai, T. (2015). Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. Journal of the American Medical Informatics Association, 22(5), 993–1000.
57. Zheng, N. S., Feng, Q., Kerchberger, V. E., Zhao, J., Edwards, T. L., Cox, N. J., Wei, W.-Q. (2020). Phemap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records. Journal of the American Medical Informatics Association, 27(11), 1675–1687.
Published
2022-02-08
How to Cite
Hier, D. B., Yelugam, R., Azizi, S., & Wunsch III, D. C. (2022). A Focused Review of Deep Phenotyping with Examples from Neurology. European Scientific Journal, ESJ, 18(4), 4. https://doi.org/10.19044/esj.2022.v18n4p4
Section
Special Editions
Copyright (c) 2022 Daniel B. Hier, Raghu Yelugam, Sima Azizi, Donald C. Wunsch III
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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