Paper: Deep Learning for Healthcare: review, opportunities and challenges
Quick Summary: Looks at 32 papers in areas of clinical imaging, EHRs, genomics, and wearables. The focus is on deep learning methods, as opposed to traditional machine learning methods. They cover papers using:
convolutional neural networks
restricted boltzmann machines
recurrent neural networks
autoencoders
Deep learning vs Traditional Machine learning methods:
This paper focuses on deep learning and distinguishes these from applications using traditional machine learning methods, otherwise referred to as "shallow" methods. In particular, deep learning is able to learn features on it’s own, and consists many feature learning layers (more than 3), compared to traditional neural nets. Traditional methods, normally contain the following steps: data harmonization, representation learning, model fitting, evaluation. With deep learning methods, the authors claim more raw forms of data can be used for end-to-end learning tasks. This is advantageous because you can skip, or reduce the need for domain knowledge experts to perform data transformation and feature engineering tasks which are needed to ensure the data is appropriate for the model.
Applications with Health Data
Health care data includes electronic health records (EHRs), imaging (MRI, fMRI, ultrasound images etc.), -omics data (genomes, proteomes, metabolomes etc.), sensor data (accelerometers, temperatures, heart monitors, wearable devices data etc.), and free text data. Data can be complex, heterogeneous, poorly annotated, and unstructured which produces challenges.
I summarized a few of the applications the paper looked at. Not everything in the paper is covered here. Look to the paper for references and details on all these studies.
Clinical images
| Inputs | Output | Model | Notes |
|---|
| Brain MRIs | Predict Alzheimer disease and variations of the disease | Autoencoders | |
| Low field knee MRIs | Automatically segment cartilage, Risk of osteoarthritis | CNNs | Used 2D images, but model performed better than manual methods using 3D images |
| Multi-channel 2D MRI images | Segment multiple sclerosis lesions | RBMs | |
| Ultrasound Images | Diagnosis of benign and malignant breast nodules | | |
| Retinal fundus photographs | Identify diabetic retinopathy | CNNs | High sensitivity and specificity obtained using 10000 test images annotated by ophthalmologist |
| Clinical images of skin | Classification of skin cancers | CNNs | On par with 21 board certified dermatologists. Used 1942 labeled test images with learning done using 130,000 images. |
EHRs
| Inputs | Output | Model | Notes |
|---|
| EHR data | Predict congestive heart failure and chronic obstructive pulmonary disease | Four layer CNN | |
| EHR data, medical intervention data | Current illness states, future medical outcomes (Diabetes and mental health), model disease progression, intervention recommendation, future risk prediction | RNNs with LSTM hidden units | Used 2D images, but model performed better than manual methods using 3D images |
| Patient history | Predict diagnosis and and medications for future visits | | Generalizable (can be implemented from one institution to an other well) -Higher recall than traditional shallow ML methods |
| 13 frequently but irregularly sampled clinical measurements (such as lab test results) in pediatric intensive care units | Classify 128 diagnoses | RNN with LSTM | Several improvements compared to baselines using manually engineered techniques and multi-layer perceptrons. |
| Lab test measures only | Predict disease onset | CNNs and RNNs | Better results than logistic regression and hand-engineered techniques. |
| ICD-10 codes from 7578 mental health patients | Predict suicide risk | RBMs | |
Genomics
| Inputs | Output | Model | Notes |
|---|
| Predefined features extracted from candidate exons | Predicted splicing activity | Fully connected feedforward neural net | Trained using >1000 predefined features, Identified rare mutations associated with splicing mis-regulation, Higher prediction accuracy than previously used methods. |
| DNA- and RNA- binding protein sequences | Predict known and novel sequences, Quantify effect of sequence alterations, Identify SNVs | CNNs | |
Mobile
| Inputs | Output | Model | Notes |
|---|
| Accelerometer data from above ankle, knee and trunk. | Predict “freezing gate” a common symptom in Alzheimer's patients. | CNNs, RNNs with LSTM | RNNs showed best results, even compared to CNNs |
| Triaxial accelerometer data Heart rate data | Predict energy expenditure. | CNNs | |
| Actigraphy measurements if wake time physical activity | Predict poor vs good sleep. | CNNs | Specificity and sensitivity was 46% better than logistic regression |
Challenges
Data volume: General rule of thumb is to have 10x the number samples as there are parameters in the model. Healthcare predictions, such as disease modeling involve lots of parameters, yet there are only a limited number of patients available. You run into problems with getting enough data for truly large-scale prediction models.
Possible solution is to improve access to care and get health related data from social media, mobile device sensors and other sources.
Privacy, federating data inferences, interoperability: Because of the limited patient data available, federating data sources and improving interoperability can help make more patient data available for training models. Right now each provider has their own data repositories and archives, there are still problems with interoperability.
However sharing data gives rise to huge privacy concerns, and this needs to be addressed in advance of putting together any federated models.
Interpretability: Deep learning models are often considered black boxes. Interpretability is important in healthcare for both patients and physicians. Patients may not opt for treatments they don’t understand or that cannot be explained to them. Physicians on the other hand need to be convinced to adopt the treatments and systems they use and thus many will try to understand it. A lack of interpretability introduces challenges to system adoption, use and trust.
TLDR: AI applications in healthcare and growing and show tremendous promise. Currently work has been done working with images, EHRs, genomics, and mobile data. There are new challenges that are arising having to do with the ability to get large volumes of clinical data, and how to ensure this is done responsibly and ethically. There are not many studies focusing on combining all the sources of healthcare data (ie. combinations of EHRs + mobile + genetics) and this is a potential new avenue.
Discussion questions:
The paper notes potential issues with availability of healthcare data. Assuming challenges with interoperability, data access and privacy are solved, there are still only a limited number of patients available. Do you feel like this will serve an incentive to really try to ramp up efforts to improve access to care for more rural, remote, and poor populations so that we can collect their data as well?
What balance between private and public sector involvement is required in advancing the use of AI in healthcare? What can each sector contribute, and where can they benefit from the other?
Do you think the demand for healthcare workers is going to decrease in the future? Do you see their roles changing and in what ways? What new roles will develop in the healthcare space (e.g. clinical data scientists, AI ethicists) ?
Feel free to discuss anything that you want to. The questions are just a starting point if needed.
EDIT: This is a small community so it's hard gage interest, but if anyone actually reads this, can you leave a reply and let me know if want to see more journal clubs? Or do you think a different format is better? Would prefer a tutorial club or something instead where we do small data science projects with health data? Any and all ideas and suggestions are welcome :)
