Improving adherence to heart failure management guidelines via abductive reasoning

Zhuo, Chen; Salazar, Elmer; Marple, Kyle; Gupta, Gopal; Tamil, Lakshman S.; Cheeran, Daniel; Das, Sandeep R; Amin, Alpesh

doi:10.1017/s1471068417000308

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…We are also planning to augment the system in the future with a feature that automatically evaluates the cardiologists’ recommendations and gives real-time clues to help them correct their non-guideline-compliant recommendations. A preliminary study of this feature is presented in our work [13] , where we used abductive reasoning to accomplish this task. The heart failure treatment adviser system that we have developed here can serve as a valuable point-of-care tool for physicians to use during a clinical visit.…”

Section: Discussionmentioning

confidence: 99%

An AI-Based Heart Failure Treatment Adviser System

Chen

Salazar

Marple

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Management of heart failure is a major health care challenge. Healthcare providers are expected to use best practices described in clinical practice guidelines, which typically consist of a long series of complex rules. For heart failure management, the relevant guidelines are nearly 80 pages long. Due to their complexity, the guidelines are often difficult to fully comply with, which can result in suboptimal medical practices. In this paper, we describe a heart failure treatment adviser system that automates the entire set of rules in the guidelines for heart failure management. The system is based on answer set programming, a form of declarative programming suited for simulating human-style reasoning. Given a patient’s information, the system is able to generate a set of guideline-compliant recommendations. We conducted a pilot study of the system on 21 real and 10 simulated patients with heart failure. The results show that the system can give treatment recommendations compliant with the guidelines. Out of 187 total recommendations made by the system, 176 were agreed upon by the expert cardiologists. Also, the system missed eight valid recommendations. The reason for the missed and discordant recommendations seems to be insufficient information, differing style, experience, and knowledge of experts in decision-making that were not captured in the system at this time. The system can serve as a point-of-care tool for clinics. Also, it can be used as an educational tool for training physicians and an assessment tool to measure the quality metrics of heart failure care of an institution.

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Section: Discussionmentioning

confidence: 99%

An AI-Based Heart Failure Treatment Adviser System

Chen

Salazar

Marple

et al. 2018

IEEE J. Transl. Eng. Health Med.

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“…Some studies have been conducted on treatment recommendations for multimorbidity [12][13][14][15][16][17], while others have focused on specific diseases, including sepsis [18][19][20], oncology [21], non-small-cell lung cancer [8,9], breast cancer [22][23][24], cerebral infarction disease [25], diabetes [26,27], hypertension [28], hypercholesterolemia [29], AIDS [30], adolescent depression [31][32][33][34], bipolar disorder [35,36], anxiety disorders [37], paediatric generalized schizophrenia [38], graft versus host disease [39], thrombosis [40], and paediatric cystic fibrosis [41]. Several works have modelled personalized treatment pathways [42,43], built automatic clinical guidelines [44][45][46], and developed optimized exercise prescription systems [47] for cardiovascular diseases. Few works have been conducted on intelligent learning of dynamic treatment strategies for CHD [48], especially dynamic drug recommendations according to the evolving health status of CHD patients.…”

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confidence: 99%

Learning Dynamic Treatment Strategies for Coronary Heart Diseases by Artificial Intelligence: Real-world Data Driven Study (Preprint)

Guo¹,

Li²,

Liu³

et al. 2021

Preprint

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BACKGROUND Coronary heart disease (CHD) had become the world’s leading cause of death, and one of the worldwide most serious epidemic diseases. CHD was characterized by urgency, danger and severity, to make dynamic treatment strategies for CHD patients in the ICUs are of great importance. However, few works had done on this. OBJECTIVE We aimed at building and validating an artificial intelligence (AI) model to learn dynamic treatment strategies for CHD patients with the goal of improving patient outcome and learning the best practice from clinicians at the same time. METHODS We formed the treatment strategy as a sequential decision problem, and applied an AI framework of combining supervised learning and reinforcement learning (SRL) to learn a recommendation model from the real-world EHRs that took the patient’s diagnosis and evolving health status as input, and gave a treatment recommendation in the form of whether or not to take the specific drugs. The experiments were conducted by leveraging a real-world ICU database with 13,762 hospital admissions diagnosed with CHD, and the long-short-term memory (LSTM) was adopted to track the long term observed states of CHD patients. We compared the AI model with its supervised learning (SL) prototype and reinforcement learning (RL) prototype on the performance of reducing estimated in-hospital mortality and the Jaccard similarity with clinician decision by 5-fold cross validation and independent test. We used the random forest algorithm to calculate the feature importance of both the clinician policy and the AI policy, so as to illustrate the interpretability of the AI model. A case study was conducted to see the similarities and differences between the AI recommend treatment actions and the clinician’s actual treatment decisions. RESULTS The AI model with 40% reinforcement learning and 60% supervised learning could help reducing estimated in-hospital mortality by 3.13% and 0.81% on trajectory and state-wise respectively, and ensure the Jaccard similarity (0.3110) close to that of the supervised learning (0.3432). We observed that the smallest treatment action difference between AI policy and clinician decision was associated with the best survival rates, the further the prescribed drugs away from the AI recommended treatment, the worse the outcome. The case study showed that the similarity between clinician decision and AI policy on the survival case was high, while on the expired case was much smaller. CONCLUSIONS We applied an AI model of SRL-LSTM to learn dynamic treatment strategies for CHD patients in ICUs by using real-world EHRs, and it was proved to have the ability to help improving the outcome of CHD patients through its RL part, as well as learning the best practice of the clinicians by the SL part.

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Prolog for Scientific Explanation

Rohner

Kjellerstrand

2023

Prolog: The Next 50 Years

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Improving adherence to heart failure management guidelines via abductive reasoning

Cited by 5 publications

References 18 publications

An AI-Based Heart Failure Treatment Adviser System

An AI-Based Heart Failure Treatment Adviser System

Learning Dynamic Treatment Strategies for Coronary Heart Diseases by Artificial Intelligence: Real-world Data Driven Study (Preprint)

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