Shifting machine learning for healthcare from development to deployment and from models to data

In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.

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Main

In the past decade, machine learning (ML) for healthcare has been marked by particularly rapid progress. Initial groundwork has been laid for many healthcare needs that promise to improve patient care, reduce healthcare workload, streamline healthcare processes and empower the individual 1 . In particular, ML for healthcare has been successful in the translation of computer vision through the development of image-based triage 2 and second readers 3 . There has also been rapid progress in the harnessing of electronic health records 4,5 (EHRs) to predict the risk and progression of many diseases 6,7 . A number of software platforms for ML are beginning to make their way into the clinic 8 . In 2018, iDX-DR, which detects diabetic retinopathy, was the first ML system for healthcare that the United States Food and Drug Administration approved for clinical use 8 . Babylon 9 , a chatbot triage system, has partnered with the United Kingdom’s National Healthcare system. Furthermore, Viz.ai 10,11 has rolled out their triage technology to more than 100 hospitals in the United States.

As ML systems begin to be deployed in clinical settings, the defining challenge of ML in healthcare has shifted from model development to model deployment. In bridging the gap between the two, another trend has emerged: the importance of data. We posit that large, well-designed, well-labelled, diverse and multi-institutional datasets drive performance in real-world settings far more than model optimization 12,13,14 , and that these datasets are critical for mitigating racial and socioeconomic biases 15 . We realize that such rich datasets are difficult to obtain, owing to clinical limitations of data availability, patient privacy and the heterogeneity of institutional data frameworks. Similarly, as ML healthcare systems are deployed, the greatest challenges in implementation arise from problems with the data: how to efficiently deliver data to the model to facilitate workflow integration and make timely clinical predictions? Furthermore, once implemented, how can model robustness be maintained in the face of the inevitability of natural changes in physician and patient behaviours? In fact, the shift from model development to deployment is also marked by a shift in focus: from models to data.

In this Review, we build on previous surveys 1,16,17 and take a data-centric approach to reviewing recent innovations in ML for healthcare. We first discuss deep generative models and federated learning as strategies for creating larger and enhanced datasets. We also examine the more recent transformer models for handling larger datasets. We end by highlighting the challenges of deployment, in particular, how to process and deliver usable raw data to models, and how data shifts can affect the performance of deployed models.

Deep generative models

Generative adversarial networks (GANs) are among the most exciting innovations in deep learning in the past decade. They offer the capability to create large amounts of synthetic yet realistic data. In healthcare, GANs have been used to augment datasets 18 , alleviate the problems of privacy-restricted 19 and unbalanced datasets 20 , and perform image-modality-to-image-modality translation 21 and image reconstruction 22 (Fig. 1). GANs aim to model and sample from the implicit density function of the input data 23 . They consist of two networks that are trained in an adversarial process under which one network, the ‘generator’, generates synthetic data while the other network, the ‘discriminator’, discriminates between real and synthetic data. The generative model aims to implicitly learn the data distribution from a set of samples to further generate new samples drawn from the learned distribution, while the discriminator pushes the generator network to sample from a distribution that more closely mirrors the true data distribution.

figure 1

Over the years, a multitude of GANs have been developed to overcome the limitations of the original GAN (Table 1), and to optimize its performance and extend its functionalities. The original GAN 23 suffered from unstable training and low image diversity and quality 24 . In fact, training two adversarial models is, in practice, a delicate and often difficult task. The goal of training is to achieve a Nash equilibrium between the generator and the discriminator networks. However, simultaneously obtaining such an equilibrium for networks that are inherently adversarial is difficult and, if achieved, the equilibrium can be unstable (that is, it can be suddenly lost after model convergence). This has also led to sensitivity to hyperparameters (making the tuning of hyperparameters a precarious endeavour) and to mode collapse, which occurs when the generator produces a limited and repeated number of outputs. To remedy these limitations, changes have been made to GAN architectures and loss functions. In particular, the deep convolutional GAN (DCGAN 25 ), a popular GAN often used for medical-imaging tasks, aimed to combat instability by introducing key architecture-design decisions, including the replacement of fully connected layers with convolutional layers, and the introduction of batch normalization (to standardize the inputs to a layer when training deep neural networks) and ReLU (rectified linear unit) activation. The Laplacian pyramid of adversarial networks GAN (LAPGAN 26 ) and the progressively growing GAN (ProGAN 27 ) build on DCGAN to improve training stability and image quality. Both LAPGAN and ProGAN start with a small image, which promotes training stability, and progressively grow the image into a higher-resolution image.

figure 2

figure 3

Transfer learning for NLP

Simultaneous and subsequent work following the release of the transformer resolved another main problem in NLP: the formalization of the process of transfer learning. Transfer learning has been used most extensively in computer vision, owing to the success of the ImageNet challenge, which made pre-trained CNNs widely available 120 . Transfer learning has enabled the broader application of deep learning in healthcare 17 , as researchers can fine-tune a pre-trained CNN adept at image classification on a smaller clinical dataset to accomplish a wide spectrum of healthcare tasks 3,37,121,122 . Until recently, robust transfer learning for NLP models was not possible, which limited the use of NLP models in domain-specific applications. A series of recent milestones have enabled transfer learning for NLP. The identification of the ideal pre-training language task for deep-learning NLP models (for example, masked-language modelling, predicting missing words from surrounding context, next-sentence prediction or predicting whether two sentences follow one another) was solved by universal language model fine-tuning (ULM-FiT 123 ) and embeddings from language model (ELMo 124 ). The generative pre-trained transformer (GPT 125 ) from Open AI and the bidirectional encoder representations from transformers (BERT 126 ) from Google Brain then applied the methods formalized by ULM-FiT and ELMo to transformer models, delivering pre-trained models that achieved unprecedented capabilities on a series of NLP tasks.

Transformers for the understanding of clinical text

Following the success of transformers for NLP, their potential to handle domain-specific text, specifically clinical text, was quickly assessed. The performances of the transformer-based model BERT, the RNN-based model ELMo and traditional word-vector embeddings 127,128 at clinical-concept extraction (the identification of the medical problems, tests and treatments) from EHR data were evaluated 106 . BERT outperformed traditional word vectors by a substantial margin and was more computationally efficient than ELMo (it achieved higher performance with fewer training iterations) 129,130,131,132 . Pre-training on a dataset of 2 million clinical notes (the dataset multiparameter intelligence monitoring in intensive care 132 ; MIMIC-III) increased the performance of all NLP models. This suggests that contextual embeddings encode valuable semantic information not accounted for in traditional word representations 106 . However, the performance of MIMIC-III BERT began to decline after achieving its optimal model; this is perhaps indicative of the model losing information learned from the large open corpus and converging to a model similar to the one initialized from scratch 106 . Hence, there may be a fine balance between learning from a large open-domain corpus and a domain-specific clinical corpus. This may be a critical consideration when applying pre-trained models to healthcare tasks.

To facilitate the further application of clinically pre-trained BERT 129 to downstream clinical tasks, a BERT pre-trained on large clinical datasets was publicly released. Because transformers and deep NLP models are resource-intensive to train (training the BERT model can cost US$50,000–200,000 133 ; and pre-training BERT on clinical datasets required 18 d of continuous training, an endeavour that may be out of the reach of many institutions), openly releasing pre-trained clinical models can facilitate widespread advancements of NLP tasks in healthcare. Other large and publicly available clinically pre-trained models (Table 3) are ClinicalBERT 130 , BioBERT 134 and SciBERT 135 .

figure 4

The fundamental challenge of creating an adept data pipeline arises from the need to anticipate the heterogeneity of the data. ML models often require a set of specific clinical inputs (for example, blood pressure and heart rate), which are extracted from a suite of dynamically changing health data. However, it is difficult to extract the relevant data inputs. Clinical data vary in volume and velocity (the rate that data are generated), thus prompting the question of how frequently data should be collected. Furthermore, clinical data can vary in veracity (data quality), thus requiring different pre-processing steps. Moreover, the majority of clinical data exist in an unstructured format that is further complicated by the availability of hundreds of EHR products, each with its own clinical terminology, technical specifications and capabilities 156 . Therefore, how to precisely extract data from a spectrum of unstructured EHR frameworks becomes critical.

Data heterogeneity must be carefully accounted for when designing the data pipeline, as it can influence throughput, latency and other performance factors. The data pipeline starts with the process of data ingestion (by which raw clinical data are moved from the data source and into the pipeline), a primary bottleneck in the throughput of the data through the pipeline. In particular, handling peaks of data generation may require the design and implementation of scalable ways to support a variable number of connected objects 157 . Such data-elasticity issues can take advantage of software frameworks that scale up or down in real time to more effectively use computer resources in cloud data centres 158 .

After the data enters the pipeline, the data-preparation stage involves the cleansing, denoising, standardization and shaping of the data into structured data that are ready for consumption by the ML system. In studies that developed data pipelines to handle healthcare data 156,159,160 , the data-preparation stage was found to regulate the latency of the data pipeline, as latency depended on the efficiency of the data queue, the streaming of the data and the database for storing the computation results.

A final consideration is how data should move throughout the data pipeline; specifically, whether data should move in discrete batches or in continuous streams. Batch processing involves collecting and moving source data periodically, whereas stream processing involves sourcing, moving and processing data as soon as they are created. Batch processing has the advantages of being high-throughput, comprehensive and economical (and hence may be advantageous for scalability), whereas stream processing occurs in real time (and thus may be required for time-sensitive predictions). Many healthcare systems use a combination of batch processing and stream processing 160 .

Established data pipelines are being harnessed to support real-time healthcare modelling. In particular, Columbia University Medical Center, in collaboration with IBM, is streaming physiological data from patients with brain injuries to predict adverse neurological complications up to 48 h before existing methods can 161 . Similarly, Yale School of Medicine has used a data pipeline to support real-time data acquisition for predicting the number of beds available, handling care for inpatients and patients in the intensive care unit (such as managing ventilator capacity) and tracking the number of healthcare providers exposed to COVID-19 161 . However, optimizing the components of the data pipeline, particularly for numerous concurrent ML healthcare systems, remains a challenging task.

Deployment in the face of data shifts

A main obstacle in deploying ML systems for healthcare has been maintaining model robustness when faced with data shifts 162 . Data shifts occur when differences or changes in healthcare practices or in patient behaviour cause the deployment data to differ substantially from the training data, resulting in the distribution of the deployment data diverging from the distribution of the training data. This can lead to a decline in model performance. Also, failure to correct for data shifts can lead to the perpetuation of algorithmic biases, missing critical diagnoses 163 and unnecessary clinical interventions 164 .

In healthcare, data shifts are common occurrences and exist primarily along the axes of institutional differences (such as local clinical practices, or different instruments and data-collection workflows), epidemiological shifts, temporal shifts (for example, changes in physician and patient behaviours over time) and differences in patient demographics (such as race, gender and age). A recent case study 165 characterizing data shifts caused by institutional differences reported that pneumothorax classifiers trained on individual institutional datasets declined in performance when evaluated on data from external institutions. Similar phenomena have been observed in a number of studies 41,163,166 . Institutional differences are among the most patent causes of data shifts because they frequently harbour underlying differences in patient demographics, disease incidence and data-collection workflows. For example, in an analysis of chest-X-ray classifiers and their potential to generalize to other institutions, it was found that one institution collected chest X-rays using portable radiographs, whereas another used stationary radiographs 41 . This led to differences in disease prevalence (33% vs 2% for pneumonia) and patient demographics (average age of 63 vs 45), as portable radiographs were primarily used for inpatients who were too sick to be transported, whereas stationary radiographs were used primarily in outpatient settings. Similarly, another study found that different image-acquisition and image-processing techniques caused the deterioration of the performance of breast-mammography classifiers to random performance (areas under the receiver operating characteristic curve of 0.4–0.6) when evaluated on datasets from four external institutions and countries 163 . However, it is important to note that the models evaluated were trained on data collected during the 1990s and were externally tested on datasets created in 2014–2017. The decline in performance owing to temporal shifts is particularly relevant; if deployed today, models that have been trained on older datasets would be making inferences on newly generated data.

Studies that have characterized temporal shifts have provided insights into the conditions under which deployed ML models should be re-evaluated. An evaluation of models that used data collected over a period of 9 years found that model performance deteriorated substantially, drifting towards overprediction as early as one year after model development 167 . For the MIMIC-III dataset 132 (commonly used for the development of models to predict clinical outcomes), an assessment of the effects of temporal shifts on model performance over time showed that, whereas all models experienced a moderate decline over time, the most significant drop in performance occurred owing to a shift in clinical practice, when EHRs transitioned systems 164 (from CareVue to MetaVision). A modern-day analogy would be how ML systems for COVID-19 (ref. 168 ) that were trained on data 169 acquired during the early phase of the pandemic and before the availability of COVID-19 vaccines would perform when deployed in the face of shifts in disease incidence and presentation.

Data shifts and model deterioration can also occur when models are deployed on patients with gender, racial or socioeconomic backgrounds that are different from those of the patient population that the model was trained on. In fact, it has been shown that ML models can be biased against individuals of certain races 170 or genders 42 , or particular religious 171 or socioeconomic 15 backgrounds. For example, a large-scale algorithm used in many health institutions to identify patients for complex health needs underpredicted the health needs of African American patients and failed to triage them for necessary care 172 . Using non-representative or non-inclusive training datasets can constitute an additional source of gender, racial or socioeconomic biases. Popular chest-X-ray datasets used to train classifiers have been shown to be heavily unbalanced 15 : 67.6% of the patients in these datasets are Caucasian and only 8.98% are under Medicare insurance. Unsurprisingly, the performance of models trained with these datasets deteriorates for non-Caucasian subgroups, and especially for Medicare patients 15 . Similarly, skin-lesion classifiers that were trained primarily on images of one skin tone decrease in performance when evaluated on images of different skin tones 173 ; in this case, the drop in performance could be attributed to variations in disease presentation that are not captured when certain patient populations are not adequately represented in the training dataset 174 .

These findings exemplify two underlying limitations of ML models: the models can propagate existing healthcare biases on a large scale, and insufficient diversity in the training datasets can lead to an inadequate generalization of model outputs to different patient populations. Training models on multi-institutional datasets can be most effective at combating model deterioration 15 , and directly combating existing biases in the training data can also mitigate their impact 171 . There are also solutions for addressing data shifts that involve proactively addressing them during model development 175,176,177,178 or retroactively by surveilling for data shifts during model deployment 179 . A proactive attitude towards recognizing and addressing potential biases and data shifts will remain imperative.

Outlook

Substantial progress in the past decade has laid a foundation of knowledge for the application of ML to healthcare. In pursuing the deployment of ML models, it is clear that success is dictated by how data are collected, organized, protected, moved and audited. In this Review, we have highlighted methods that can address these challenges. The emphasis will eventually shift to how to build the tools, infrastructure and regulations needed to efficiently deploy innovations in ML in clinical settings. A central challenge will be the implementation and translation of these advances into healthcare in the face of their current limitations: for instance, GANs applied to medical images are currently limited by image resolution and image diversity, and can be challenging to train and scale; federated learning promises to alleviate problems associated with small single-institution datasets, yet it requires robust frameworks and infrastructure; and large language models trained on large public datasets can subsume racial and ethnic biases 171 .

Another central consideration is how to handle the regulatory assessment of ML models for healthcare applications. Current regulation and approval processes are being adapted to meet the emerging needs; in particular, initiatives are attempting to address data shifts and patient representation in the training datasets 165,180,181 . However, GANs, federated learning and transformer models add complexities to the regulatory process. Few healthcare-specific benchmarking datasets exist to evaluate the performance of these ML systems during clinical deployment. Moreover, the assessment of the performance of GANs is hampered by the lack of efficient and robust metrics to evaluate, compare and control the quality of synthetic data.

Notwithstanding the challenges, the fact that analogous ML technologies are being used daily by millions of individuals in other domains, most prominently in smartphones 100 , search engines 182 and self-driving vehicles 68 , suggests that the challenges of deployment and regulation of ML for healthcare can also be addressed.

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Acknowledgements

This work was supported in part by the National Institutes of Health via grants F30HL156478 (to A.Z.), R01CA227713 (to L.X.), R01CA256890 (to L.X.), P30AG059307 (to J.Z.), U01MH098953 (to J.Z.), P01HL141084 (to J.C.W), R01HL163680 (to J.C.W), R01HL130020 (to J.C.W), R01HL146690 (to J.C.W.) and R01HL126527 (to J.C.W.); by the National Science Foundation grant CAREER1942926 (to J.Z.); and by the American Heart Association grant 17MERIT3361009 (to J.C.W.). Figures were created with BioRender.com.

Author information

Authors and Affiliations

  1. Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, CA, USA Angela Zhang & Joseph C. Wu
  2. Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA Angela Zhang
  3. Greenstone Biosciences, Palo Alto, CA, USA Angela Zhang & Joseph C. Wu
  4. Department of Computer Science, Stanford University, Stanford, CA, USA Angela Zhang & James Zou
  5. Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, USA Lei Xing
  6. Department of Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA, USA James Zou
  7. Departments of Medicine, Division of Cardiovascular Medicine Stanford University, Stanford, CA, USA Joseph C. Wu
  8. Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA Joseph C. Wu
  1. Angela Zhang