Keywords
Artificial intelligence - machine learning - deep learning - feature engineering - federated learning
Introduction
Enormous volumes of healthcare data combined with improved capabilities to store, process, and analyze such data have prompted rapid advances in Artificial Intelligence (AI) techniques in the medical domain. A recent review summarized the growing number of peer-reviewed publications addressing the development and use of AI algorithms in medicine and highlighted the significant need for additional advances and evidence for these innovations to be translated effectively into practice [1].
In 2018, AI researchers tackled some of the most significant challenges in practically applying AI in health, such as incomplete and inconsistent data and the need to access and analyze sensitive healthcare data sets across institutions.
Paper Selection Method
The recommended, standardized methodology for selection of best papers for the International Medical Informatics Association (IMIA) Yearbook [2] was adapted for the special section theme of “AI in Health: New Opportunities, Challenges, and Practical Implications.” The Ovid MEDLINE® and Web of Science® databases were queried in February 2019 for peer-reviewed, original research articles that addressed the special section theme and were published in the English language in 2018. Editorials, letters, and opinion pieces were deliberately excluded from consideration as AI in health was a popular topic in health journals in 2018, and perspectives were common and often redundant.
Section editors generated Medical Subject Headings (MeSH®) and keyword search terms that represented state-of-the-art AI techniques and topics, and queries were refined iteratively for each database. Concepts incorporated included artificial intelligence, machine learning, deep learning, federated learning, reinforcement learning, neural network, convolutional network, variational autoencoder, deep segmentation, Bayesian model, latent variable, feature engineering, network embedding, gene interaction network, and automated pattern recognition. In PubMed, the exploded MeSH® term “Medical Informatics” was used as a domain filter. In Web of Science®, the search was limited by the Research Area of “Medical Informatics.” Article type filters were applied to retrieve research articles from journals or published peer-reviewed proceedings.
The MEDLINE® and Web of Science® queries retrieved 921 and 621 citations, respectively. After removal of duplicates, 1,480 citations remained. Section editors screened this initial list for relevance to the theme and scientific quality, and they rated each paper as “keep,” “pend,” or “discard.” Papers rated as “keep” by one of the section editors were independently reviewed and scored by section editors to yield the top 15 candidate best papers [3]–[17]. Criteria for scoring included innovation beyond established AI techniques, work that addressed substantial challenges in the field, and rigorous scientific evaluations.
The top-ranked 15 manuscripts underwent formal peer review by at least four internationally renowned scientists from across the world. Based on ratings and comments from peer review, section editors’ recommendations, and input from the IMIA Yearbook Editorial Board, three papers [3], [8], [11] were chosen as 2018 best papers in the AI for Health special section of the IMIA Yearbook.
Table 1
Best paper selection of articles for the IMIA Yearbook of Medical Informatics 2019 in the section ‘Artifical Intelligence in Health’. The articles are listed in alphabetical order of the first author's surname
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Section
Artificial Intelligence in Health
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• Albers DJ, Levine ME, Stuart A, Mamykina L, Gluckman B, Hripcsak G. Mechanistic Machine Learning: How Data Assimilation Leverages Physiological Knowledge Using Bayesian Inference to Forecast the Future, Infer the Present, and Phenotype. J Am Med Inform Assoc 2018;25(10):1392-401.
• Lee J, Sun J, Wang F, Wang S, Jun CH, Jiang X. Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis. JMIR Med Inform 2018;6(2):e20.
• Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, de Marvao A, Dawes T, O'Regan DP, Kainz B, Glocker B, Rueckert D. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Trans Med Imaging 2018;37(2):384-95.
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Conclusions and Outlook
In 2018, researchers published promising advances in the science of AI to address some of the most significant challenges to its application in health. Two of the best papers enhanced performance of learning methods by incorporating domain knowledge to limit the learning space. Albers et al., re-introduced the technique of data assimilation, commonly used in aeronautical applications, to the problem of predicting glucose values in type 2 diabetes. In this study, they combined machine learning with an endocrine physiological model to make accurate glucose level predictions, generated recommendations to produce desired outcomes, and learned model parameters to represent phenotypes [3]. This approach allowed accurate prediction with sparse data, which can be common in healthcare, and supported trust through explainability. The authors highlighted the need for a multidisciplinary biomedical informatics pipeline to apply data assimilation to the domain of medicine. Oktay et al., presented a novel strategy to train neural networks to learn underlying anatomy from cardiac imaging data using a stacked autoencoder constrained by global anatomic knowledge [11]. In this study, anatomic rather than physiologic knowledge was leveraged to demonstrate excellent performance in image segmentation tasks using multimodal magnetic resonance imaging (MRI) and ultrasound data sets. Importantly, this approach was able to overcome the very common problems of motion artifact and a lack of internal consistency, which limit many approaches to image interpretation. Further, although the methodology was designed for segmentation, the algorithm showed good performance in pathological classification.
The third best paper addressed the challenging task of applying learning models in data sets that are distributed across institutions. Lee et al., presented a novel privacy-preserving analytics platform for patient similarity learning in a federated setting, through a multi-hash approach for context dependent cross-institution patient representation, and incorporation of homomorphic encryption for privacy preservation [8]. This work addressed the important problem of enabling privacy-preserving learning in healthcare, where sufficient data to make inferences might be stored across a wide variety of sites.
Five of the remaining selected papers described advances in medical imaging analysis through novel application and enhancements of deep learning methods. Shi et al., applied an emerging technique of stacked deep polynomial networks to a multimodal imaging-classification task [14]. These scientists used a staged approach to building machine learning algorithms, applying stacked deep polynomial networks to MRI and positron emission tomography (PET) images to fuse and learn feature representation to classify images from patients with Alzheimer’s disease, normal controls, and mild cognitive impairment who subsequently did or did not progress to Alzheimer’s disease. Their approach demonstrated excellent accuracy in a challenging multi-class classification task for categories of a disease that often progresses gradually and unpredictably. While MRI has become an indispensable diagnostic tool, its slow acquisition process makes it expensive and less accessible. To address this challenge, Schlemper et al., proposed a well-designed and well-executed framework for reconstructing dynamic sequences of two-dimensional cardiac MRI images from aggressively under-sampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process [13]. Liu et al., focused on another difficult task in using MRI for diagnosis of brain diseases including Alzheimer’s disease - that of automatic generation of clinically meaningful features. They proposed a novel approach of identifying discriminative regions called landmarks, followed by application of a CNN for patch-based deep feature learning [10]. Addressing challenges in other common imaging modalities, Gruezemacher et al., developed a three-dimensional (3D) adaptation of deep neural nets (DNNs) for lung nodule detection from computed tomography (CT) images [6]; Hassan et al., presented a novel approach combining tensor-based segmentation, Delauday triangulation, and deep learning models to provide complete 3D presentation of macula to support automated diagnosis of various macula conditions [7].
Chang et al., addressed another important problem of how to train deep learning models across distributed data sets as an alternative to data sharing [5]. They proposed three non-parallel heuristics (i.e., ensembling single institution models, single weight transfer, and cyclic weight transfer) for training deep learning models using distributed data and produced valuable insights through a set of very well designed simulations run on three public data sets. Cyclic weight transfer resulted in performance similar to centrally hosted data, even with low resolution images and institutional variability in class distributions.
Successful development of deep learning models requires abundant labeled data, which is particularly difficult to obtain in medical domain. Li et al., proposed an approach that leverages a type of multi-task predictive neural network called generalized auxiliary-task augmented network (GATAN) to avoid overfitting [9], and they demonstrated improved performance when the learning algorithm has access to limited labeled data.
AI methods for clinical concept and knowledge extraction from text is another important topic, particularly on use of deep leaning methods to reduce the need for human-engineered features and thus improve generalizability. Luo et al., proposed a CNN-based method called segment convolutional networks (seg-CNNs) that used word embedding only for the extraction of entity relationships in clinical notes with promising results [17].
Causal inference is an area that is receiving increasing attention, as the field moves from initial proof-of-concept predictive models demonstrating the value of real world data analysis to methods that can provide more actionable insights. Estimating the heterogeneous effect of a treatment using observation data such as that found in electronic health records (EHRs) is one of the most common and challenging use cases of causal inference. Powers et al., compared and evaluated a number of well know methods for estimating heterogeneous treatment effects through a preliminary study using the SPRINT data set [12], [18]. They identified causal boosting and causal multivariate adaptive regression splines as two of the most promising methods and emphasized the need for much more effort in this area.
Three of the selected papers covered topics in genomics, an area that has seen increasing adoption of AI methods. Uppu and Krishna proposed a hybrid approach combining DNN and random forest for the detection of multi-locus interactions among single nucleotide polymorphisms [15]. Xiao et al., proposed a semi-supervised learning framework based on stacked sparse auto-encoder for cancer prediction from RNA-seg data [16]. Belciug and Gorunescu proposed a new learning method based on extreme learning machine to train a single layered feed forward network on microarray and mass spectrometry data for cancer prediction [4].
