The screening inclusion and exclusion criteria were built iteratively via consensus (NS, TR and WB) (Table 1). 17 0 obj ANN were cautioned to be used as a proof of concept rather than a successful prediction model [66]. Articles were excluded if there was no explicit reference to artificial neural networks; the application was not in the health care domain or context of health care organizational decision-making, or was not a publication that was peer-reviewed (e.g. B(t�>�Vy��..p�����a�U��Ȍ�m� -]ЌY�!�#2cLҮvq��%Li�����$H�tGc�ь��J���ZwQUY=��q:��%Y� %:��Uť�ȃ8t�E%�C�a� ��Y�W�. Training/testing sets were in ratios of 50:50, 70:30 or 90:10 and the reported accuracy ranged between 50% and 100%. ANNs have been used by many authors for … In an overview of basic concepts, Agatonovic-Kustrin & Beresford (2000) describe ANN gather knowledge by detecting patterns and relationships in data and “learn” through experience. Another review reported various applications in areas of accounting and finance, health and medicine, engineering and marketing, however focused the review on feed-forward neural networks and statistical techniques used in prediction and classification problems [20]. endobj Figure 1: Depiction of a Neural Network, where each circle is a neuron and the arrows indicate the connections between neurons in consecutive layers. patients, cases, images, and signals) and sample sizes were used. Applications of ANN in health care include clinical diagnosis, prediction of. Users require less formal statistical training and the networks are able to detect complex non-linear relationships and interactions between dependent and independent variables. Applications of hybrid intelligent systems include robotics, medical diagnosis, speech/natural language understanding, monitoring of manufacturing processes. Methods include naïve Bayesian classification, support vector machines, and k-nearest-neighbour classification [32]. A review by Agatonovic-Kustrin & Beresford (2000) describes neural computation to be powered from the connection of its neurons and that each neuron has a weighted input, transfer function and a single output. Let’s see more about the potential of deep learning in the healthcare industry and its many applications in this field. Artificial intelligence lies at the nexus of new technologies with the potential to deliver health care that is cost-effective and appropriate care in real-time, manage effective and efficient communication among multidisciplinary stakeholders, and address non-traditional care settings, the evolving heathcare workplace and workforce, and the advent of new and disparate health information systems. 6 0 obj Its application is particularly valuable under one or more of several conditions: when sample data show complex interaction effects or do not meet parametric assumptions, when the relationship between independent and dependent variables is not strong, when there is a large unexplained variance in information, or in situations where the theoretical basis of prediction is poorly understood [23]. A systematic review on the use of ANN as decision-making tools in the field of cancer reported trends from 1994–2003 in clinical diagnosis, prognosis and therapeutic guidance for cancer from1994 to 2003, and suggested the need for rigorous methodologies in using neural networks [19]. 1 0 obj Applications of ANN were mainly found to be classification (22), prediction (14), and diagnosis (10) (Fig 4). conducted literature reviews of ANN used in business (from 1988–1995) [76] and finance (1990–1996) [77], at that time describing the promise of neural networks for increasing integration with other existing or developing technologies [76, 77]. <>/Border[0 0 0]/Dest(Rpone.0212356.ref009)>> 18 0 obj Applications of ANN to make decisions directly between providers and patients was categorized as ‘micro’, any decisions made by a larger group and not directly related to a patient was categorized as ‘meso’, and decisions beyond an organizational group (i.e. budget, resource allocation, technology acquisition, service additions/reductions, strategic planning) [6]. Yes <>/Border[0 0 0]/Dest(Rpone.0212356.ref005)>> For improved organizational readiness, the governance and operating model of health care organizations need to enable a workforce and culture that will support the use of AI to enhance efficiency, quality and patient outcomes [108]. endobj The Arksey & O’Malley framework (2005) was adopted to identify the (i) research question, (ii) relevant studies, (iii) select studies, (iv) chart the data and (v), collate, summarize and present findings. Neural networks in healthcare by Rezaul Begg, Joarder Kamruzzaman, unknown edition, ... "This book covers state-of-the-art applications in many areas of medicine and healthcare"- … 24 0 obj India. The authors further observe that in business applications, external data sources (e.g. endobj Hybrid approaches (e.g. Yes However, our study showed a significant use of hybrid models., Editor: Olalekan Uthman, The University of Warwick, UNITED KINGDOM, Received: October 4, 2018; Accepted: January 31, 2019; Published: February 19, 2019. We found ANN to be mainly used for classification, prediction and clinical diagnosis in areas of cardiovascular, telemedicine and organizational behaviour. Micro-level applications of ANN include diagnosis of pulmonary tuberculosis among hospitalized patients by health care providers using models developed for classification and risk group assignment [47], classify Crohn’s Disease medical images [51], analyse recorded ECG signals to trigger an alarm for patients and allow collection and transmission of patient information to health care providers[52]. endobj New information can be inputted into the model once the model has been trained and tested [26]. knowledge and temporal representation, machine learning), the adoption of key standards required for integration and knowledge sharing (e.g. After all, to many people, these examples of Artificial Intelligence in the medical industry are a futuristic concept.According to Wikipedia (the source of all truth) :“Neural Networks are In addition to S2 Appendix, Fig 4 illustrates the various applications of ANN identified in the literature review. Although ANN do not require knowledge of data source, they require large training sets due to the numerous estimated weights involved in computation [26]. An example of ANN facilitating Lean thinking adoption in health care contexts is its application to describe ‘information flow’ among cancer patients by modeling the relationship between quality of life evaluations made by patients, pharmacists and nurses [87]. Understanding Neural Networks can be very difficult. A working paper on the use of ANN in decision support systems states that the structure, quality and quantity of data used is critical for the learning process and that the chosen attributes must be complete, relevant, measurable and independent[18]. T : + 91 22 61846184 [email protected] Using more training data improves the classification model, whereas using more test data contributes to estimating error accurately [35]. India 400614. Yes Our background search did not identify seminal paper(s) published or advancements related to our research question, thereby justifying the rationale for not limiting the search to a specicic start date. Investigation, Investigation, The main activities involved in the KDD process include (i) integration and cleaning, (ii) selection and transformation, (iii) data mining and (iv) evaluation and interpretation. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. <>stream 25 0 obj Non-clinical applications have included improvement of health care organizational management [14], prediction of key indicators such as cost or facility utilization [15]. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. ANN architectures are commonly classified as feed-forward neural networks (e.g. automated electrocardiographic (ECG) interpretation used to diagnose myocardial infarction [13]), and drug development[12]. (2012) suggest that applications can broadly include fraud detection, target marketing, performance prediction, manufacturing and medical diagnosis. industry and trade databases) are typically used to supplement internal data sources. Policies encouraging transparency and sharing of core datasets across public and private sectors can stimulate higher levels of innovation-oriented competition and research productivity [112]. The integration of ANN with secondary AI and meta-heuristic methods such as fuzzy logic, genetic, bee colony algorithms, or artificial immune systems have been proposed to reduce or eliminate challenges related to ANN (e.g. Applications of artificial neural networks in health care organizational decision-making: A scoping review Titles and abstracts were first screened to include articles with keywords related to and/or in explicit reference to artificial neural networks. A subfield of AI, machine learning-as-a-service-market (MLaaS), is expected to reach $5.4 billion by 2022, with the health care sector as a notable key driver [9]. Deep Learning With Python. 13 0 obj (B) Number of articles by country. An example of numeric prediction is when a model is constructed to predict a continuous-valued function or ordered value (as opposed to a class label). Neural networks in healthcare by Rezaul Begg, Joarder Kamruzzaman, 2006, Idea Group Pub. No, PLOS is a nonprofit 501(c)(3) corporation, #C2354500, based in San Francisco, California, US,,,,,,,,,,,,,,,,,,,,,,,,,,, users of the system) respond to their environment based on internalized rule sets that are not necessarily explicit, shared or need to be understood by another agent [116]. Given their … Data curation, single-layer perceptron, multi-layer perceptron, radial basis function networks) or feed-back, or otherwise referred to as recurrent neural networks (e.g. The effectiveness of neural networks in healthcare applications has long since been recognized [4–6], however, most implementations A convolutional neural network. Authors reported neural networks reduced computation time in comparison to conventional planning algorithms [60] thereby enabling users to access model output faster in real-time, outperforming linear regression models in prediction [44, 56, 61–63] and support vector machines in classification [64, 65]. Agents (e.g. In total, 80 articles were used for data collection. For more information about PLOS Subject Areas, click %PDF-1.6 We found that application of ANN in health care decision-making began in the late 90’s with fluctuating use over the years. Screening of articles occurred in two stages. With the digitization of health care [86], hospitals are increasingly able to collect large amounts of data managed across large information systems [22]. As practical and flexible modelling tools, ANN have an ability to generalize pattern information to new data, tolerate noisy inputs, and produce reliable and reasonable estimates [23]. Both big companies and startups use this technology. The selection of the three disciplines reflects the core concepts embedded in our research question: ‘what are the different applications of ANN (Computer Science) in health care organizational decision-making (Health Administration and Business Management)?’. A small portion (10) of studies applied ANN at a macro level of decision-making mainly between policy and decision-makers across multiple facilities or health care systems, out of which 2 referenced macro- only. endobj Although the backpropagation learning rule enabled the use of neural networks in many hard medical diagnostic tasks, they have been typically used as black box classifiers lacking the transparency of generating knowledge as well as the ability to explain decision-making [22]. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. broad scope, and wide readership – a perfect fit for your research every time. Similarly, global revenue of $811 million is expected to increase 40% (Compound Annual Growth Rate) by 2021 due the artificial intelligence (AI) market for health care applications. Challenges in uptake include the current inability of AI-based solutions to read unstructured data, the perspectives of health care providers using AI-based solutions, and the lack of supportive infrastructure required for wide-scale implementation [107]. Poor interpretability remains a signicant challenge with implementing ANN in health care [90]. Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Canada, Roles <> Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. By means of this review, we will identify the nature and extent of relevant literature and describe methodologies and context used. endobj ‘Flow’ is a key concept in a Lean System and ‘information flow’ is an essential improvement target to the successful operation of a health care system using a Lean approach [87]. The second in popularity in healthcare, RNNs represent neural networks that make use of sequential information. endobj PLOS ONE promises fair, rigorous peer review, Table 1 lists the criteria used to screen, include or exclude articles in the review. Due to the primitive nature of computer technology mid-20th Century, most of the research in machine learning was theoretical or based on construction of special purpose systems [18]. Different from the classical neural network, deep learning uses more hidden layers so that the algorithms can handle complex data with various structures.27 In the medical applications, the commonly used deep learning algorithms include convolution neural network (CNN), recurrent neural network, deep belief network and deep neural network. Macro-level applications of ANN include risk-adjustment models for policy-makers of Taiwan’s National Health Insurance program [57], a global comparison of the perception of corruption in the health care sector [58], model revenue generation for decision-makers to determine best indicators of revenue generation in not-for-profit foundations supporting hospitals of varying sizes [59]. 2 More recent architectures often include more tips and tricks such as dropout, skip connection, bath normalization, and so forth to improve its abilities of approximation and generalization, often with more parameters or computations. 23 0 obj So, let’s look at some examples of neural network applications in different areas. 26 0 obj Despite its analytic capabilities, wide-scale adoption remains a challenge, mainly due to methodological complexities and scalability challenges [98]. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada, Roles In unsupervised learning, the network learns without knowledge of desired output and by discovering and adapting to features of the input patterns. endobj endobj Main topics or area of interest based on the article’s overall purpose included Organizational Behaviour (18%), Cardiovascular (14%), Infectious Disease and Telemedicine (7%) (Table 2). Using complex adaptive systems (CAS) theory to understand the functionality of AI can provide critical insights: first, AI enhances adaptability to change by strengthening communication among agents, which in turn fosters rapid collective response to change, and further, AI possesses the potential to generate a collective memory for social systems within an organization [114]. Competitive networks, Kohonen’s self-organizing maps, Hopfield networks) [25]. Like RNN (Recurrent Neural Network) and stock market prediction, drug discovery, and CNN is pure data tweaking. A short disclaimer before we get into the hands-on part: 3. A number of breakthroughs in the field of computer science and AI bring insight to reported publication patterns [82]. This trained neural network will classify the signature as being genuine or forged under the verification stage. We provide a seminal review of the applications of ANN to health care organizational decision-making. The authors describe regression analysis as a statistical methodology often used for numeric prediction and encompasses identification of distribution trends based on available data. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. It is also one of the most creative applications of convolutional neural networks in general. A few years later, the ability of neural networks to learn any type of function was demonstrated [84], suggesting capabilities of neural networks as universal approximators [85]. Sharma & Chopra (2013) describe the two most common types of neural networks applied in management sciences to be the feed-forward and recurrent neural networks (Fig 1) in comparison with feed-forward networks common to medical applications [28, 29]. patient health records, photos, reviews, social media data from mobile applications and devices) remain a critical unmet need for hospitals [107, 111]. Formal analysis, electronic medical records and DNA sequences), health care organizations are taking advantage of analyzing large sets of routinely collected digital information in order to improve service and reduce costs [7]. Subsequently, a full-text review of articles that met the initial screening criteria was conducted on basis of relevance and availability of information for data extraction. Literature suggests that current reviews on applications of ANN are limited in scope and generally focus on a specific disease [19] or a particular type of neural network [20], or they are too broad (i.e. simple random sampling, trial-and-error) to more deterministic (e.g. Despite the variety of study contexts and applications, ANN continues to be mainly used for classification, prediction and diagnosis. Support vector machines are used to model high-dimensional data and are considered state-of-the-art solutions to problems otherwise not amenable to traditional statistical analysis. Challenges related to such algorithms include the necessity of a previously defined architecture for the model, sensitivity to the initial conditions used in training [104]. These cells occur in layers and are often referred to as nodes. Appropriate data splitting is a technique commonly used in machine learning in order to minimize poor generalization (also referred to as over-training or over-fitting) of models [34]. This review is motivated by a need for a broad understanding the various applications of ANN in health care and aids researchers interested in bridging the disciplines of organizational behaviour and computer science. Conceptualization, Adopters of ANN or researchers new to the field of AI may find the scope and esoteric terminology of neural computing particularly challenging [18]. <>/Border[0 0 0]/Dest(Rpone.0212356.ref010)>> Image Compression –Vast amounts o… A recent survey of AI applications in health care reported uses in major disease areas such as cancer or cardiology and artificial neural networks (ANN) as a common machine learning technique [10]. endobj [13 0 R 14 0 R 15 0 R 16 0 R 17 0 R 18 0 R 19 0 R 20 0 R 21 0 R 22 0 R 23 0 R 24 0 R 25 0 R 26 0 R 27 0 R 28 0 R 29 0 R 30 0 R 31 0 R] 28 0 obj Traditional decision-making processes based on stable and predictable systems are no longer relevant, due to the complex and emergent nature of contemporary health care delivery systems [1]. A Convolutional neural network has some similarities to the feed-forward neural network, where the connections between units have weights that determine the influence of one unit on another unit. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fisher et al (2016) developed an ANN based monitoring method evaluating Parkinson’s disease motor symptoms and reported signiciant challenges with detecting disease states due to the inherent subjectivity underlying the interpretation of disease state descriptors (i.e. This is related to the fact that to the researchers are often given a large number of factual materials, for which there is no mathematical model. Table 2 lists the main topic areas of articles reviewed. endobj It presents basic and advanced concepts to help beginners and industry professionals get up to speed on the latest developments in soft computing and healthcare systems. The authors write that models called classifiers predict categorical class labels and can be used to predict the class label of objects for which the class label is unknown. here. ANN has been used as part of decision support models to provide health care providers and the health care system with cost-effective solutions to time and resource management [16]. Data Availability: All relevant data are within the manuscript and its Supporting Information files. The processor passes it on to the next tier a… As policy-makers adopt strategies towards a value-based, patient-centred model of care delivery, decision-makers are required to consider the readiness of health care organizations for successful implementation and wide-scale adoption of AI or ANN based decision-support tools. ANN are similar to statistical techniques including generalized linear models, nonparametric regression and discriminant analysis, or cluster analysis [24]. Neural networks are widely used in different industries. In an effort toward moving to value-based care, decision-makers are reported to be strategically shifting the focus to understanding and better alignment of financial incentives for health care providers in order to bear financial risk; population health management including analyses of trends in health, quality and cost; and adoption of innovative delivery models for improved processes and coordination of care.