Near-optimal insulin treatment for diabetes patients: A machine learning approach

Shifrin, Mark; Siegelmann, Hava T.

doi:10.1016/j.artmed.2020.101917

Cited by 20 publications

(28 citation statements)

References 23 publications

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“…Researchers have applied MDP models to optimize initial treatment selection [25,26] and the timing of transplantation [27,28], to compare the effectiveness of different combinations of treatment [29], to optimize screening policy [30], and to prevent disease-related complications [31]. However, 16 studies concern the optimization of treatment decisions [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Five studies use the MDP to optimize treatment decisions for cancer [30,35,39,42,43], five focus on optimizing the treatment of diabetes mellitus [31][32][33][34]41], and the remaining (N = 13) studies are concerned with liver diseases [27,28], high blood pressure/hypertension [37,40], hepatitis C [44], atherosclerotic cardiovascular disease [45], ischemic heart disease [29,36], atrial fibrillation [38], anemia [47], tuberculosis [46...…”

Section: Overview Of Existing Applications Of Mdp In Treatment Of Dis...mentioning

confidence: 99%

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

Jörg

et al. 2022

PharmacoEconomics

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The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.

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Section: Overview Of Existing Applications Of Mdp In Treatment Of Dis...mentioning

confidence: 99%

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

Jörg

et al. 2022

PharmacoEconomics

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“…Other works such as Dave et al [52], Shifrin et al [56], and Cappon et al [66] used BG along with insulin and CHO for prediction purposes. Aiello et al [67] and Oviedo et al [53] both aimed at postprandial hypoglycemia prediction by utilizing BG data combined with insulin and CHO data.…”

Section: Bg Combined With Other Types Of Datamentioning

confidence: 99%

“…[80] 90 min [60] 120 min [56] 210 min [49,53,68] 240 min Long-Term Prediction [41,42,[47][48][49]69] 360 min [55] 420 min [44] 540 min [61] 1 Week [66,67,72] Meal Duration…”

Section: Medium-term Predictionmentioning

confidence: 99%

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Mujahid

Contreras

Vehı́

2021

Sensors

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(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

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“…Time is of the essence in many clinical domains, necessitating its modelling in decision support systems for such domains. Four contributions of the Special Issue involve time considerations, namely the contributions by Bernardinia et al [1], by Pokharel et al [2], by Shifrin and Siegelmann [3] and by Bhatia et al [4]. Bernardinia et al's work [1] deals with the domain of Type 2 Diabetes (T2D), where early prediction of a high risk for developing this life-threatening ailment is critically important.…”

Section: Summary Of Selected Papersmentioning

confidence: 99%

“…In working towards the management of diabetes, a personalised treatment scheme is proposed by Shifrin and Siegelmann [3] for patients with diabetes, which is based on a stochastic modelling of blood glucose level process and sta-tistical learning of possible reactions to various treatment actions. In particular, the reaction to insulin treatment is formulated as a Markov decision process, which is then learned by reinforcement learning.…”

Section: Summary Of Selected Papersmentioning

confidence: 99%

Medical analytics for healthcare intelligence – Recent advances and future directions

Chen

Keravnou-Papailiou

Antoniou

2021

Artificial Intelligence in Medicine

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Near-optimal insulin treatment for diabetes patients: A machine learning approach

Cited by 20 publications

References 23 publications

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Medical analytics for healthcare intelligence – Recent advances and future directions

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