Artificial Intelligence

Use of artificial neural networks for machine learning has enabled major advancements in intelligent systems, helping millions of people in their daily lives. During the past decade, progress has greatly accelerated thanks to the availability of massive amounts of data and use of specialized hardware to build deeper networks and perform faster optimization. Furthermore, better insight into the inner workings of deep neural networks has enabled both researchers and practitioners to achieve improvements in training and generalization (Erhan, 2010; Ioffe, 2015; Srivastava, 2014).

There are numerous environments where systems powered by artificial neural networks shape our experiences and influence our behavior. These systems routinely manifest in our experiences with e-commerce, web search, as well as in communication interfaces such as smart speakers, messaging, and email applications.

An important area where the use of machine learning is still in its infancy is population health. While deep learning has been used for medical diagnosis applications (Poplin, 2018; Cruz-Roa, 2014), building predictive models for behavior of healthcare consumers is a relatively unexplored subject. This is a potential use case that we are passionate about at Accolade.

Accolade at a Glance:

Our mission at Accolade is to provide personalized health and benefits solutions to improve the experience, outcomes, and cost of healthcare for employers, health plans, and health plan members. We provide a single point of contact for all health and benefits resources and work with employees and their families to help them utilize the best care options available. Our ability to be proactive about consumer behavior has always been crucial to our mission.

Predicting Members’ Healthcare Usage with Deep Learning

People pursue and obtain healthcare through various channels. For instance, they can visit primary care physicians or specialists, and they may receive care at clinics or hospitals and fill prescriptions at drugstores. However, while they often seek information to help in their decision-making from the internet, friends, and providers, choosing the right healthcare and using it properly has become an increasingly challenging and complex task.

In addition to these conventional methods, Accolade members can call our team of healthcare assistants or reach out to them through direct messaging. Furthermore, our technology enables informing our health assistants about changes in members’ health status that may require support and guidance. We consider all these as other forms of interaction between our members and the healthcare system.

By drawing on what we know about how our members use healthcare and related benefits, we have considered building models to predict members’ future usage patterns. This provides our team of health assistants with valuable insight to use in outreach and guidance. Such targeted interventions improve members’ health outcomes and their decision-making about using health and benefit resources, which in turn saves medical costs.

When it comes to learning from our members’ experience over time, events are not isolated from each other. Occurrence of a healthcare event can generally be traced back to a prior event. Let’s make this concrete with the following hypothetical scenario. Fig. 1a) shows a series of events that an Accolade member might experience over time.

Figure 1 a) Sequence of a member health events over time. b) An LSTM network learning from the sequence of events in a). {yi} are labels corresponding to the events whose feature vectors are {xi}.

Here, the member visited a primary care physician (event #1), who referred him/her to a specialist (event #2). For the purpose of diagnosis, the specialist then asked the member to take medical tests (event #4). However, in the meantime, the member decided to consult his/her dedicated health specialist at Accolade (event #3). The member then returned to the specialist to discuss the results (event #5). Other events may follow.

Clearly, most of these events are result of other events that happened earlier in the member’s timeline. For example, the lab visit was requested by the specialist, to whom the member was referred because he/she visited a primary care physician in the first place.

Furthermore, there is some amount of data that describe the context of each event. For example, there are diagnosis codes in specialist claims or lab visits, and procedure codes associated with operations or tests performed on members in medical facilities. Combined with member attributes (age, gender, family information, location, employer, etc.), these form comprehensive feature vectors {xi,i=1,…} describing individual members and the events they experience as they navigate through the healthcare system.

Having identified event sequences and feature vectors describing each event, we use recurrent neural networks, Fig. 1b), to learn the underlying trends in the members’ healthcare journey. This enables us to make informed predictions about what is likely to come next in the members’ interaction with us or the healthcare providers.

Recurrent Neural Networks

Recurrent neural networks (RNNs) are at the forefront of neural network models used for learning from sequential data. Examples are time series problems and natural language understanding tasks such as machine translation and speech recognition (Cho, 2014; Graves, 2013). What makes RNNs powerful in dealing with sequential data is their stateful design: RNNs have number of internal states that are updated as consecutive elements of a sequence are processed. These internal states are then used, along with current input, to predict sequences of outputs. This gives rise to a model whose individual predictions, in addition to the current observation, are influenced by sequence of prior observations.

RNNs come in different flavors that generally differ in their details of internal computational steps that connect their inputs and outputs. In our case, since sequence of member events can be quite long, we used LSTM (long short-term memory) networks that are designed to handle long-term dependencies (Colah, 2015).

This model is currently used for the following applications:

Identifying High-Cost Claimant Members

One of our mandates at Accolade is to help our customers manage the healthcare spending of their employees. Employers often incur inflated medical costs owing to employees who are heavy users, usually because they make frequent visits to healthcare providers and/or have expensive medical claims. Identifying those people enables our health assistants to engage with them early on to provide guidance, ensure they use their healthcare and benefits properly, and inform them about alternative options available to them through their health plan.

We use RNNs on sequences of our members’ historic claims to predict whether a given member is likely to become a high-cost claimant in a certain time period, for example by the end of the calendar year. The resulting model is periodically applied on existing medical claims data of individual members to give the probability for a member becoming a high-cost claimant later on in the year. This enables Accolade to identify future high-cost claimants and reach out to them before they actually incur such increased costs.

Forecasting Member Interactions

Calls and/or direct messages are another type of event making up sequences of longitudinal health data of Accolade members.  These interactions are two of the primary methods of communication with our members. As described earlier, interactions with Accolade are interrelated with claim events. For example, members contact Accolade to inquire about their past or upcoming medical claims.

We train an RNN-driven model on sequences of member claims and call events, in order to predict the probability that a member will contact us in any given time period. If more members are predicted to have higher likelihood of calling Accolade, bigger call volumes can be expected. Anticipating this volume enables us to be proactive about members’ healthcare and benefit needs and plan accordingly for our own staffing requirements.

Works Cited:

• Cho, K. e. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP (pp. 1724-1734). Doha: Association for Computational Linguistics.
• Colah, C. (2015). 1. Retrieved from github: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
• Cruz-Roa, A. e. (2014). Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. SPIE Medical Imaging, 904103–904103.
• Erhan, D. e. (2010). Why Does Unsupervised Pre-training Help Deep Learning? JMLR, 625-660.
• Graves, A. a. (2013). Speech recognition with deep recurrent neural networks. International Conference on Acoustics, Speech and Signal Processing (pp. 26-31). Vancouver, BC: IEEE.
• Ioffe, S. S. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. JMLR, 448-456.
• Poplin, R. e. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 158–164.
• Srivastava, N. e. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. JMLR, 1929-1958.

Inspection of carry-on bag scans for security at airports is currently manual. This results in long wait lines, frustrated travelers and susceptibility to human error. At present, Computational Tomography (CT) is used to scan carry-on bags for items and then a security officer visualizes each of the scans and decides whether the carry-on has any unsafe items in it or not. Manual visualization of the scanned images takes time, and, since it’s a manual process, chances of human error is high. There is also no uniform analysis of scanned images because of varying decision making from one security officer to another.

A New Security Screening Solution

So how about we try something new? We could incorporate an Artificial Intelligence (AI) based solution in current CT scan machines at airports, which could automatically distinguish between safe and unsafe items from the scanned images of carry-on bags at airports.

The major advantages of this system:

• Shorter wait lines and less frustrated travelers. AI based automated recognition of unsafe carry-on bags will make the carry-on security check process faster and help reduce traveler frustration. And when passengers are not waiting in lines, they might be at restaurants or shops at the airport, which could add to revenue generation.
• Higher accuracy and better security because of reduced human error. A high level of accuracy will be possible through the use of machine learning algorithms as well as the regular upgrade of the classifiers used by those algorithms as labeled data becomes available for re-training them. Once the algorithms are re-trained, their deployment on the CT scan machines in the field can be done cheaply and quickly without disrupting the field operations in a significant way.

So, now you might be thinking, that sounds a natural progression, hasn’t anyone already researched this field? It’s not that research has not been done in this field. The U.S. Department of Homeland Security (DHS) has teamed up with Google and its crowdsourcing site, Kaggle, to search for new algorithms to identify concealed objects detected by airport security body scanners. The problem that DHS and Google are trying to solve here is not exactly same as mentioned in this blog but is a similar concept of using AI algorithms to detect concealed dangerous objects on the human body rather than in carry-on bags.

There have been CT scan machine manufacturers that have introduced 3D CT scans that will create 3D images of carry-on bags and help security officers inspect the scanned images in 3 dimensions. This provides the security officers the capability to analyze each scanned image from various angles and make a better judgement of an object security level in the carry-ons. Though this idea is not based on AI algorithms, it is trying to solve the same problem of speeding up carry-on security bag checks at airports.

Creating the Solution

The idea is to have an artificial intelligence-based security bag-check solution that integrates with existing computational tomography (CT) scanning systems at airports. CT scans of carry-on bags at airport security yields a lot of information that security officers have to individually analyze and evaluate comprehensively in a short time. The proposed solution will use AI to help security officers make better and faster decisions by performing automated image recognition of dangerous objects in carry-on bags. It will use deep learning technology, which combines neural network architectures, to automatically identify visual differences between safe and unsafe/abnormal items in scanned images.  Figure 1 shows a very high level pictorial representation of the AI solution described here, while Figure 2 and Figure 3 describe the AI algorithm in more detail.

Figure 1: Pictorial respresentation of the AI solution described in the blog

The novelty of this idea lies in the AI solution that is comprised of inference on the edge (CT scanners), with training happening in cloud. The AI solution consists of 3 major steps:

1. Initialization
2. Deployment
3. Re-training (happening as needed)

a.     Initialization

The first step of initialization would consist of training an appropriate deep learning model before it can be deployed for use. One of the biggest challenges in this process would be to acquire enough data to train the model. The performance of neural networks (i.e. deep learning) depends on the number of input images you use to train them. For a more generic problem such as recognizing a cat in a scene, the training samples may not have to be huge to provide a reasonable level of accuracy. However, given the diverse nature of threatening items in a bag (e.g. guns, explosives, liquids, drugs, live animals, etc.) and the infinite ways in which these can be positioned in a bag, the required number of training samples will be pretty high. This challenge of training data collection can be addressed in three ways:

• Collaboration with Department of Homeland Security (DHS) to make CT scan images available for training. This would be similar to what DHS is working on with Google for body scanners as mentioned above.
• Perform continuous data collection at airports to create a large, secure pool of data to do training. Also while this data is being collected, the decisions made by the human operators after examining each bag can be used as labeling data, which establishes the “ground truth” information that makes the collected data suitable for training the models. This approach also alleviates the need to finance the offline labeling of the collected data, which can be very expensive. Because CT scan machines used at airports are pretty much standard devices, training data and trained models used on one device can also be used on others reliably. This also ensures a high level of standardized security checks independent of the experience of the security officers based at various airports around the globe.
• Deep learning algorithms generally have feature extraction and classification as two main steps. Apart from getting real data from CT scanners at the airport, the CT scan manufacturers can work to automatically generate multiple view angles of threatening objects (e.g. guns). This will help create a subset of features for known unsafe objects, which can be fed to training model.

Figure 2:  Initialization phase: Flowchart highlighting the data collection and labeling process performed by the human inspectors, with initially no AI in the loop.

Figure 2 shows a flowchart on how the AI model will be trained on safe & unsafe objects during the initialization phase. In this phase the human inspection would be happening as usual however, the human inspector would be labeling the scanned objects that will be used for model training.

b.   Deployment

In the second step, the trained model will be deployed in the CT scanners at the airports, assisting security officers to identify unsafe objects in the scanned images of carry-on bags. Depending on the CT scanner model, the deployment could potentially be just an upgrade to the current software stack of the CT scanners, with AI algorithm added on to it. After the AI algorithm has been deployed in the CT scanners it will be inferring the safety level of the scanned images of carry-on bags.

Figure 3: Flowchart showing invention algorithm in deployment phase

As shown in Figure 3, once the trained model has been deployed, every time it infers the safety level of the bag that could result in multiple states, depending on the inferencing accuracy.  If the AI model safety accuracy is higher than the defined criteria, the bag will pass security check and move ahead to be picked up by the traveler. However, if the accuracy is below the required criteria, human inspection would be needed. Depending on the safety of the bag, human inspector will mark bag as safe or unsafe and this information will be sent to cloud to store that as training information.

In cases, where the AI model detects bag as unsafe, human inspection will be required. The human inspector will then decide the safety level of the bag and send the labeled information on the scanned image of bag to the cloud for storing as training data.
In the description of this solution a “Negative” would mean the bag is safe and the input image does not have any identified unsafe objects in it.

The four states mentioned in Figure 3 can be described as follows:

• True Negative: This will happen when the AI model accuracy is less than set criteria and it infers that the bag is safe and the security officer finds that to be true.
• False Negative: This will happen when AI model accuracy is less than set criteria and it infers that the bag is safe, while on cross checking the security officer finds unsafe objects in the bag.
• True Positive: When the AI model infers the input image to be unsafe and security officer agrees with that output.
• False Positive: When the AI model infers the input image to be unsafe and security officer finds that to be untrue.

The solution seeks to support security officer & not replace them. The security officers will always be responsible for the final interpretation of the scanned images. In all states of the AI algorithm, the final decision made by the security officer (ground truth), along with the raw input image and the labeled data is sent to cloud database for next batch of training data. In case of false negative & false positive, the security officer will be expected to correctly label the specific objects that AI model inferred incorrectly. This will help in continuously evolving the system to improve its decision making as data collection and labeling will be performed continuously once the intelligent CT scan machines are deployed in the field. Security officers are ultimately responsible for accepting or rejecting the image tags. The system uses this feedback to ultimately improve its accuracy and robustness as it encounters more examples.

c. Re-training

Once the CT scanner with AI algorithm has been deployed and is being used, re-training of the model in the CT scanner will need to happen continuosly over cloud. The labeled input & output data from deployment stage will be collected and stored on the edge device (CT scanner) and sent to the cloud periodically for re-training the model, as shown in Figure 3. The updated re-trained model or the new classifier will be downloaded on the edge device on a regular basis to improve its level of inferecing accuracy.
After the AI algorithm at the edge device starts performing at expected accuracy for prolonged periods of time, then human inspection for the “negative” state of bags can be progressively removed, with only random cross-check of the results. Also, the retraining frequency of the model can be adjusted as needed.

Conclusion

The pieces described in this solution like image recognition, deep learning neural networks, and training and inferencing models for imaging, already exist in the ecosystem currently. The novelty of this idea will be that it will have an AI solution that integrates with current CT scanners at airports and helps automatically detect unsafe objects in scanned images of carry-on bags at airports as well as continuously learning to improve its accuracy. The author encourages the readers to further explore the applicability and benefits of this solution.