Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019
Computational psychiatry is an emerging field that examines phenomena in mental illness using formal techniques from computational neuroscience, mathematical psychology, and machine learning. These techniques can be used in a theory-driven manner to gain insight into neural or cognitive processes an...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2020
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| _version_ | 1797086609579966464 |
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| author | Browning, M Carter, C Chatham, C Den Ouden, H Gillan, C Baker, J Chekroud, A Cools, R Dayan, P Gold, J Goldstein, R Hartley, C Kepecs, A Lawson, R Mourao-Miranda, J Philips, M Pizzagalli, D Powers, A Rindskopf, D Roiser, J Schmack, K Schiller, D Sebold, M Stephan, K Frank, M Huys, Q Paulus, M |
| author_facet | Browning, M Carter, C Chatham, C Den Ouden, H Gillan, C Baker, J Chekroud, A Cools, R Dayan, P Gold, J Goldstein, R Hartley, C Kepecs, A Lawson, R Mourao-Miranda, J Philips, M Pizzagalli, D Powers, A Rindskopf, D Roiser, J Schmack, K Schiller, D Sebold, M Stephan, K Frank, M Huys, Q Paulus, M |
| author_sort | Browning, M |
| collection | OXFORD |
| description | Computational psychiatry is an emerging field that examines phenomena in mental illness using formal techniques from computational neuroscience, mathematical psychology, and machine learning. These techniques can be used in a theory-driven manner to gain insight into neural or cognitive processes and in a data-driven way to identify predictive and explanatory relationships in complex datasets. The approaches complement each other: theory-driven models can be used to infer mechanisms, and the resulting measurements can be used in data-driven approaches for prediction. Recent computational studies have successfully described and measured novel mechanisms in a range of disorders, have framed disorders in new and informative ways, and have identified predictors of treatment response. These methods hold the potential to improve identification of relevant clinical variables and could be superior to classification based on traditional behavioral or neural data alone. However, these promising results have been slow to influence clinical practice or to improve patient outcomes. |
| first_indexed | 2024-03-07T02:24:19Z |
| format | Journal article |
| id | oxford-uuid:a511cdd6-371f-4aeb-943f-a1d4e5c5ff92 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:24:19Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:a511cdd6-371f-4aeb-943f-a1d4e5c5ff922022-03-27T02:37:58ZRealizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a511cdd6-371f-4aeb-943f-a1d4e5c5ff92EnglishSymplectic Elements at OxfordElsevier2020Browning, MCarter, CChatham, CDen Ouden, HGillan, CBaker, JChekroud, ACools, RDayan, PGold, JGoldstein, RHartley, CKepecs, ALawson, RMourao-Miranda, JPhilips, MPizzagalli, DPowers, ARindskopf, DRoiser, JSchmack, KSchiller, DSebold, MStephan, KFrank, MHuys, QPaulus, MComputational psychiatry is an emerging field that examines phenomena in mental illness using formal techniques from computational neuroscience, mathematical psychology, and machine learning. These techniques can be used in a theory-driven manner to gain insight into neural or cognitive processes and in a data-driven way to identify predictive and explanatory relationships in complex datasets. The approaches complement each other: theory-driven models can be used to infer mechanisms, and the resulting measurements can be used in data-driven approaches for prediction. Recent computational studies have successfully described and measured novel mechanisms in a range of disorders, have framed disorders in new and informative ways, and have identified predictors of treatment response. These methods hold the potential to improve identification of relevant clinical variables and could be superior to classification based on traditional behavioral or neural data alone. However, these promising results have been slow to influence clinical practice or to improve patient outcomes. |
| spellingShingle | Browning, M Carter, C Chatham, C Den Ouden, H Gillan, C Baker, J Chekroud, A Cools, R Dayan, P Gold, J Goldstein, R Hartley, C Kepecs, A Lawson, R Mourao-Miranda, J Philips, M Pizzagalli, D Powers, A Rindskopf, D Roiser, J Schmack, K Schiller, D Sebold, M Stephan, K Frank, M Huys, Q Paulus, M Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019 |
| title | Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019 |
| title_full | Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019 |
| title_fullStr | Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019 |
| title_full_unstemmed | Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019 |
| title_short | Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019 |
| title_sort | realizing the clinical potential of computational psychiatry report from the banbury center meeting february 2019 |
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