neural network applications in healthcare

19 0 obj patients, cases, images, and signals) and sample sizes were used. 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. 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). Other advantages of ANN, relative to traditional predictive modeling techniques, include fast and simple operation due to compact representation of knowledge (e.g., weight and threshold value matrices), the ability to operate with noisy or missing information and generalize to similar unseen data, the ability to learn inductively from training data and process non-linear functionality critical to dealing with real-word data [37]. Neural network technologies are designed to solve many difficult tasks, starting from formulation, among which many medical problems. <>stream 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 mining pertains to extraction of significant patterns and knowledge discovery and employs inferring algorithms, such as ANN, to pre-processed data to complete data mining tasks such as classification and cluster analysis [79]. Click through the PLOS taxonomy to find articles in your field. Artificial neural … endobj A feed-forward network can be single-layered (e.g. 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. The authors further observe that in business applications, external data sources (e.g. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. here. Nida Shahid, Tim Rappon, Whitney Berta Formal analysis, Applications for prediction included developing a risk advisor model to predict the chances of diabetes complication according to changes in risk factors [42], identifying the optimal subset of attributes from a given set of attributes for diagnosis of heart disease [43], modelling daily patient arrivals in the Emergency Department [44]. Yes For example, a systematic review of deep learning models using electronic health record data recently identified challenges related to the temporality (e.g. Yes Applications with lowest estimated potential value include preliminary diagnosis ($5B), automated image ($3B) and cyber-security ($2B) [108]. 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. broad scope, and wide readership – a perfect fit for your research every time. endobj Yes The authors state that the neuron is activated by the weighed sum of inputs it receives and the activation signal passes through a transfer function to produce a single output. Hybrid approaches (e.g. Yes e0212356. controlled terminologies, semantic structuring, standards representing clinical decision logic) has been slow [101] Patel et al. 20 0 obj 3. The major function of the brain is to send information to the body in the form of signals. PLOS ONE promises fair, rigorous peer review, Our findings warrant the understanding of perspectives and beliefs of those adopting ANN-based solutions in clinical and non-clinical decision-making. 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]. Neural Network Tutorial – Multi-Layer Perceptron. No, Is the Subject Area "Machine learning" applicable to this article? Investigation, The authors describe regression analysis as a statistical methodology often used for numeric prediction and encompasses identification of distribution trends based on available data. The final search syntax was based on search terms refined through an iterative process involving examination of a preliminary set of results to ensure relevance (S1 Appendix). www.plosone.org 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. Supervision, Considering the prevalent use of medical information systems and medical databases, ANN have found useful applications in biomedical areas in diagnosis and disease monitoring [87]. Healthcare costs around the globe are on the rise, creating a strong need for new ways of assisting the requirements of the healthcare system. In total, 80 articles were used for data collection. <>/Border[0 0 0]/Dest(Rpone.0212356.ref007)>> Applications of hybrid intelligent systems include robotics, medical diagnosis, speech/natural language understanding, monitoring of manufacturing processes. The overarching goal of this scoping review is to provide a much-needed comprehensive review of the various applications of ANN in health care organizational decision-making at the micro-, meso-, and macro-levels. endstream Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. 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. here. endobj No, Is the Subject Area "Decision making" applicable to this article? Key success factors or differentiators that define effective machine learning technology in health care include access to extensive data sources, ease of implementation, interpretability and buy-in as well as conformance with privacy standards [9]. A number of breakthroughs in the field of computer science and AI bring insight to reported publication patterns [82]. <>/Border[0 0 0]/Dest(Rpone.0212356.ref003)>> Copyright: © 2019 Shahid et al. Han et al. endobj By means of this review, we will identify the nature and extent of relevant literature and describe methodologies and context used. A2A. 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]. Despite the variety of study contexts and applications, ANN continues to be mainly used for classification, prediction and diagnosis. Drug discovery is another major healthcare field with the extensive use of CNNs. Our neural network-based therapeutic care solutions for health care providers, their patients and health system customers are designed to address common health care problems that impact the human nervous system and improve neurological health outcomes. <>/Border[0 0 0]/Dest(Rpone.0212356.ref013)>> No, Is the Subject Area "Data mining" applicable to this article? Applications of ANN were mainly found to be classification (22), prediction (14), and diagnosis (10) (Fig 4). Methodology, across different institutions, a system or countries) was categorized as ‘macro’ level of decision-making. Table 1 lists the criteria used to screen, include or exclude articles in the review. Applications of ANN in health care include clinical diagnosis, prediction of Sharma & Chopra (2013) describe information flow in feed-forward networks to be unidirectional from input layer, through hidden layers to the output layer, without any feedback. <>/Border[0 0 0]/Dest(Rpone.0212356.ref009)>> Also referred to as the generalized delta rule, backpropagation refers to how an ANN is trained or ‘learns’ based on data. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. A convolutional neural network. 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. Neural networks are similar to linear regression models in their nature and use. In the world of neural networks, CNNs are widely used for image classification. In unsupervised learning, the network learns without knowledge of desired output and by discovering and adapting to features of the input patterns. 3 0 obj 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]. An artificial neural network (ANN) is a network of highly computed cells that mimics the physiological capability of the human brain. 1. 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 Prior efforts have concentrated on a specific domain or aspect of health care and/or limited study findings to a period of time. Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Although lacking the ability to explain decision-making, ANN-based decision-support tools enable health care organizational decision-makers to respond to complex and emergent environments using incoming and evolving data. Reported advantages of using a hybrid model included higher prediction accuracy rates (error rate of <2%), flexibility and faster performance (0.1 second) in comparison with a model using neural networks only (20 minutes learning time). Writing – original draft, 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]. 27 0 obj Conceptualization, edition, in English endobj Now let’s look at how a use case of Artificial Intelligence in healthcare can be implemented by using Deep Learning concepts. 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]. India 400614. This review provides a comprehensive review of the various applications of artificial neural networks in health care organizational decision-making. The theory of CAS has been used as an alternative approach to improve our understanding and scaling up of health services; CAS theory shifts decision-making towards embracing uncertainty, non-linear processes, varying context and emergent characteristics [115]. Formal analysis, Users require less formal statistical training and the networks are able to detect complex non-linear relationships and interactions between dependent and independent variables. 25 0 obj ANN’s application to facilitate more micro- and meso-level decision-making compared to macro-level may be explained by the type and volume of data required and available to build an effective model. We provide a seminal review of the applications of ANN to health care organizational decision-making. Writing – review & editing, Affiliations The strategy is integral to many consumer-facing technologies, such as chatbots, mHealth apps, and virtual personalities like … technique [ 10 ]. selection of network topology, initial weights, choice of control parameters) [106]. The error in computed and desired outputs can be used to improve model performance. A national study on the implementation of Health Information Technology (HIT) in the United States reported a poor understanding of IT staff, informaticians, health information managers and others playing a significant role in implementation of HIT in health care [102] Barriers to adoption of HIT include mismatch of return on investment, challenges to workflow in clinical settings, lack of standards and interoperability, and concerns about privacy and confidentiality [102]. With its ability to discover hidden knowledge and values, scholars have suggested using ANN to improve care performance and facilitate the adoption of ‘Lean thinking’ or value-based decision making in health care [87]. The raw data is received by the first tier, which is processed through interconnected nodes, having their own rules and packages of knowledge. Yet the health care organizational decision-making literature suggests the focus of decision-making persistently remains on problems that are visible, while the larger system within which health care delivery organizations exist remains unacknowledged [1]. endobj T : + 91 22 61846184 [email protected] Methods include naïve Bayesian classification, support vector machines, and k-nearest-neighbour classification [32]. endobj Our study found artificial neural networks can be applied across all levels of health care organizational decision-making. Both big companies and startups use this technology. ANN learn to perform tasks by using inductive learning algorithms requiring massive data sets [18]. No, Is the Subject Area "Neural networks" applicable to this article? 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]. The second in popularity in healthcare, RNNs represent neural networks that make use of sequential information. [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] Several limitations of ANN are identified in the literature: they are limited in their ability to explicitly identify possible causal relationships, they are challenging to use in the field, they are prone to over fitting, model development is empirical potentially requiring several attempts to develop an acceptable model [37], and there are methodological issues related to model development [31]. The processor passes it on to the next tier a… The global market for health care predictive analytics is projected was valued at USD 1.48 billion in 2015 and expected to grow at a rate of 29.3% (compound annual growth rate) by 2025 [8]. Although ANN do not require knowledge of data source, they require large training sets due to the numerous estimated weights involved in computation [26]. Applications of artificial neural networks in health care organizational decision-making: A scoping review Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. 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]. 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]. patient health records, photos, reviews, social media data from mobile applications and devices) remain a critical unmet need for hospitals [107, 111]. For example, a US based hospital has collaborated with a game development company to create a virtual world in which surgeons are guided through scenarios in the operating room using rules, conditions and scripts to practice making decisions, team communication, and leadership [110]. depth pertaining to layers of the network), was trained to classify 1.2 million images in record-breaking time as part of the ImageNet Large Scale Visual Recognition Challenge [92]. Funding: The authors received no specific funding for this work. Poor interpretability remains a signicant challenge with implementing ANN in health care [90]. Fig 1 illustrates the two types of networks with three layers (input, hidden and output). budget, resource allocation, technology acquisition, service additions/reductions, strategic planning) [6]. https://doi.org/10.1371/journal.pone.0212356.g004. Due to the cross-disciplinary nature of our query, the search strategy was designed to identify literature from multiple databases according to the key disciplines of Health Administration (Medline and Embase), Computer Science (ACM Digital Library and Advanced Technologies & Aerospace Database), and Business and Management (ABI/Inform Global and JSTOR). No, Is the Subject Area "Forecasting" applicable to this article? The book explores applications in soft computing and covers empirical properties of artificial neural network (ANN), evolutionary computing, fuzzy logic and statistical techniques. 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. ANN was applied for diagnosis of disease based on age, sex, body mass index, average blood pressure and blood serum measurements [45], comparing predictive accuracies of different types of ANN and statistical models for diagnosis of coronary artery disease [46], diagnosis and risk group assignment for pulmonary tuberculosis among hospitalized patients [47], and non-invasive diagnosis of early risk in dengue patients [48]. Automated electrocardiographic ( ECG ) interpretation used to inform health care organizational decision-making ;,... Web-Based platform, for screening, prediction and diagnosis study characteristics, aim, methodology context! Speech/Natural language understanding, monitoring of manufacturing processes are related to the next tier here! Decision making '' applicable to this article impacting the healthcare industry and many! Signals ) and backpropagation learning, every input pattern used to model high-dimensional data and providing effective [! 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