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Human and AI Collaborative Decision Making in Healthcare
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Human and AI Collaborative Decision Making in Healthcare

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 25734

Special Issue Editors

Dr. Hadi Akbarzadeh Khorshidi

E-Mail Website
Guest Editor
Melbourne School of Population and Global Health, The University of Melbourne, Melbourne 3052, Australia
Interests: system modeling; health informatics; machine learning and health policy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Uwe Aickelin

E-Mail Website
Guest Editor
School of Computing and Information Systems, University of Melbourne, Parkville, VIC 3010, Australia
Interests: artificial intelligence; health informatics; optimization; data mining; medical decision support
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been used frequently in many areas including healthcare and medicine. It has facilitated developing models for decision making due to computers’ processing ability to handle big data and large amount of information. However, humans are better at working with unstructured information and deal with uncommon situations. Therefore, collaborative decision making can provide an opportunity to collect more information sources that improve the accuracy and reliability of prediction and decision-making models. In addition, explainability and transparency are always concerns when AI models make decisions in healthcare. Collaborative decision making by providing interaction between clinicians and AI models can result in more transparent decision making. Thus, intelligent decision making would be better understood by clinicians. From another point of view, decisions made by both clinicians and AI models are associated with uncertainty due to the nature of human thinking and prevalence of uncertainty in data. Collaborative decision making contributes to capture all types of uncertainty as a source of information to make robust decisions. Although there have been some studies that addressed the need for these types of decision making, few studies have worked on developing collaborative decision making systems.

This Special Issue aims to highlight the importance of collaboration between clinicians and AI models and initiate developing collaborative decision-making systems with applications in healthcare decision making. The Special Issue invites submissions from researchers and practitioners who work on group decision making, explainable AI, uncertainty, and human–computer interaction (HCI).

You may choose our Joint Special Issue in Informatics.

Dr. Hadi Akbarzadeh Khorshidi
Prof. Dr. Uwe Aickelin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human and AI collaboration in healthcare decision making
  • collaborative decision-making systems
  • interpretable AI models in collaborative decision making in healthcare
  • group decision making between clinicians and AI
  • human–computer interaction in decision making in healthcare
  • multicriteria decision making (MCDM) in machine learning
  • ensemble learning under uncertainty

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Published Papers (5 papers)

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Research

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14 pages, 964 KiB  
Article
Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning
by José A. González-Nóvoa, Silvia Campanioni, Laura Busto, José Fariña, Juan J. Rodríguez-Andina, Dolores Vila, Andrés Íñiguez and César Veiga
Int. J. Environ. Res. Public Health 2023, 20(4), 3455; https://doi.org/10.3390/ijerph20043455 - 16 Feb 2023
Cited by 6 | Viewed by 2743
Abstract
It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today’s hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher [...] Read more.
It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today’s hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients’ care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking. Full article
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
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17 pages, 3667 KiB  
Article
Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers
by Juan A. Gómez-Pulido, José M. Gómez-Pulido, Diego Rodríguez-Puyol, María-Luz Polo-Luque and Miguel Vargas-Lombardo
Int. J. Environ. Res. Public Health 2021, 18(5), 2364; https://doi.org/10.3390/ijerph18052364 - 28 Feb 2021
Cited by 16 | Viewed by 3776
Abstract
A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can [...] Read more.
A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%. Full article
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
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19 pages, 1232 KiB  
Article
Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention
by Ningrong Lei, Murtadha Kareem, Seung Ki Moon, Edward J. Ciaccio, U Rajendra Acharya and Oliver Faust
Int. J. Environ. Res. Public Health 2021, 18(2), 813; https://doi.org/10.3390/ijerph18020813 - 19 Jan 2021
Cited by 8 | Viewed by 3477
Abstract
In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients [...] Read more.
In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis. Full article
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
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21 pages, 3657 KiB  
Article
Surviving Burn Injury: Drivers of Length of Hospital Stay
by Chimdimma Noelyn Onah, Richard Allmendinger, Julia Handl and Ken W. Dunn
Int. J. Environ. Res. Public Health 2021, 18(2), 761; https://doi.org/10.3390/ijerph18020761 - 18 Jan 2021
Cited by 9 | Viewed by 3469
Abstract
With a reduction in the mortality rate of burn patients, length of stay (LOS) has been increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient’s LOS. However, few have investigated the association between LOS [...] Read more.
With a reduction in the mortality rate of burn patients, length of stay (LOS) has been increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient’s LOS. However, few have investigated the association between LOS and a patient’s mental and socioeconomic status. There is anecdotal evidence for links between these factors; uncovering these will aid in better addressing the specific physical and emotional needs of burn patients and facilitate the planning of scarce hospital resources. Here, we employ machine learning (clustering) and statistical models (regression) to investigate whether segmentation by socioeconomic/mental status can improve the performance and interpretability of an upstream predictive model, relative to a unitary model. Although we found no significant difference in the unitary model’s performance and the segment-specific models, the interpretation of the segment-specific models reveals a reduced impact of burn severity in LOS prediction with increasing adverse socioeconomic and mental status. Furthermore, the socioeconomic segments’ models highlight an increased influence of living circumstances and source of injury on LOS. These findings suggest that in addition to ensuring that patients’ physical needs are met, management of their mental status is crucial for delivering an effective care plan. Full article
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
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Review

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27 pages, 2236 KiB  
Review
A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications
by Mansoureh Maadi, Hadi Akbarzadeh Khorshidi and Uwe Aickelin
Int. J. Environ. Res. Public Health 2021, 18(4), 2121; https://doi.org/10.3390/ijerph18042121 - 22 Feb 2021
Cited by 52 | Viewed by 10160
Abstract
Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature [...] Read more.
Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML. Full article
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
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