Table 1.
List of primary references.
| Healthcare Area | Type of Machine Learning Model | Description | Applied or Experiment | References |
|---|---|---|---|---|
| EHRs | SVM, DT | Using EHRs for predicting diagnoses | Applied | Liang et al. 2014 [26] |
| - | RNN | Predicting post-stroke pneumonia using deep neural network approaches | Experiment | Ge et al., 2019 [35] |
| - | LSTM, CNN | Deep EHR: Chronic Disease Prediction Using Medical Notes | Experiment | Liu, Zhang & Razavian 2018 [40] |
| - | ML | SRML-Mortality Predictor: A hybrid machine learning framework to predict mortality in paralytic ileus patients using Electronic Health Records (EHRs) | Experiment | Ahmad et al., 2020 [41] |
| Medical Imaging | CNN | Dermatologist-level classification of skin cancer with deep neural networks | Experiment | Esteva et al. 2017 [7] |
| - | CNN | Chexnet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep-Learning | Applied | Rajpurkar et al., 2017; Tsai & Tao, 2019 [8] |
| - | CNN | International evaluation of an AI system for breast cancer screening | Experiment | McKinney et al. 2020 [49] |
| - | Deep CNN | Deep-learning algorithm predicts diabetic retinopathy progression in individual patients | Experiment | Arcadu et al. 2019 [56] |
| - | DBN | Structural MRI classification for Alzheimer's disease detection using deep belief network | Experiment | Faturrahman et al., 2017 [37] |
| - | Decision tree | Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction | Experiment | Patel et al., 2015 [27] |
| Genetic Engineering & Genomics | RT | Application of machine learning models to predict tacrolimus stable dose in renal transplant recipients | Experiment | Tang et al. 2017 [10] |
| - | ML | Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs | Applied | Malone et al. 2020 [15] |
| - | Deep CNN, Deep FFs | Off-target predictions in CRISPR-Cas9 gene editing using deep learning | Applied | Lin & Wong 2018 [76] |
| - | RNNs | DeepHF: Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning | Applied | Wang et al., 2019 [85] |
| - | Random Forest | CUNE: Unlocking HDR-mediated nucleotide editing by identifying high- efficiency target sites using machine learning | Applied | O’Brien et al., 2019 [86] |
| - | CNNs | ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity | Applied | Pan et al., 2020 [87] |
Applied is defined as an algorithm or application that is currently available on a public or private platform to healthcare professionals. It also refers to applications that are currently applied in medical practices such as clinics, hospitals, etc. An experiment is defined as an algorithm or application that has been used in a research study. EHR: Electronic Health Records, SVM: Support Vector Machine, LSTM: Long Short-Term Memory Neural Network, CNN: Convolutional Neural Network, MLP: Multi-Layer perceptron Neural Network, RNN: Recurrent Neural network, DBN: Deep Belief Network, ANN: Artificial Neural Network, ML: Machine Learning.