Download the datasets
Select a dataset to download or view documentation.
Sepsis
This dataset comprises vital signs, lab tests, administered fluid boluses and vasopressors for 2,164 patients with sepsis in the intensive care unit.
Acute Hypotension
This dataset comprises vital signs, lab tests, administered fluid boluses and vasopressors for 3,910 patients with acute hypotension in the intensive care unit.
Antiretroviral Therapy in HIV
This dataset comprises viral loads, CD4 counts, and drug regimen information for 8,916 patients with Human Immunodeficiency Virus (HIV).
RL in Healthcare
There is now huge interest in the application of RL algorithms to solve real world problems. In the healthcare domain, clinicians treating individuals with chronic disorders (e.g. epilepsy, mental illness, HIV infection) or with potentially life-threatening conditions (e.g. sepsis) often prescribe a series of treatments to maximize the chances of a favourable outcome.
This generally requires modifying the duration, dose or type of treatment over time, and is challenging due to patient heterogeneity in response to treatment, potential relapse and side-effects. Clinicians often rely on clinical judgement and instinct, rather than formal evidence-based processes, to optimize sequences of treatments. Thus, there is vast potential for the application of RL algorithms for adaptive personalisation of treatment regimens, as shown by early research on optimizing antiretroviral therapy in HIV, radiotherapy planning in lung cancer, and the management of sepsis [1]Yu C, Dong Y, Liu J, Ren G. Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV. BMC medical informatics and decision making. 2019;19(2):60. [2]Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, Naqa IE. Deep reinforcement learning for automated radiation adaptation in lung cancer. Medical physics. 2017;44(12):6690-705. [3]Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine. 2018;24(11):1716..
Nonetheless, some authors have highlighted the lack of reproducibility and potential for patient harm inherent in these methods [4]Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28(3):231-7.. In particular, recommendations made by RL algorithms may not be safe if the training data omit variables that influence clinical decision making, or if the effective sample size is small [5]Gotesman O, Johansson F, Komorowski M, Faisal A, Sontag D, Doshi-Velez F, Celi LA. Guidelines for reinforcement learning in healthcare. Nat Med. 2019;25(1):16-8..
This generally requires modifying the duration, dose or type of treatment over time, and is challenging due to patient heterogeneity in response to treatment, potential relapse and side-effects. Clinicians often rely on clinical judgement and instinct, rather than formal evidence-based processes, to optimize sequences of treatments. Thus, there is vast potential for the application of RL algorithms for adaptive personalisation of treatment regimens, as shown by early research on optimizing antiretroviral therapy in HIV, radiotherapy planning in lung cancer, and the management of sepsis [1]Yu C, Dong Y, Liu J, Ren G. Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV. BMC medical informatics and decision making. 2019;19(2):60. [2]Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, Naqa IE. Deep reinforcement learning for automated radiation adaptation in lung cancer. Medical physics. 2017;44(12):6690-705. [3]Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine. 2018;24(11):1716..
Nonetheless, some authors have highlighted the lack of reproducibility and potential for patient harm inherent in these methods [4]Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28(3):231-7.. In particular, recommendations made by RL algorithms may not be safe if the training data omit variables that influence clinical decision making, or if the effective sample size is small [5]Gotesman O, Johansson F, Komorowski M, Faisal A, Sontag D, Doshi-Velez F, Celi LA. Guidelines for reinforcement learning in healthcare. Nat Med. 2019;25(1):16-8..
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