Table 1.
Summary of contributions made by researchers over time
| Application of ML in healthcare | References | Year | Contribution |
|---|---|---|---|
| Electronic health records (EHRs) | Stojanovic et al. [68] | 2017 | Modeled healthcare quality via compact representations of EHRs |
| Brisimi et al. [69] | 2018 | Presented Chronic disease prediction hospitalization from EHRs | |
| Shickel et al. [70] | 2018 | Analyzed advances in DL techniques for EHRs | |
| Fuente et al. [71] | 2019 | Developed a solution for searching behavioral patterns in EHRs using the Random Forest algorithm | |
| Harerimana et al. [72] | 2019 | Presented deep learning strategies for EHRs analytics | |
| Bernardini et al. [73] | 2020 | Developed solutions for discovering type-2 diabetes in EHRs using sparse balanced SVMs | |
| Tsang et al. [74] | 2020 | Modeled skimpy data for feature selection in the prediction of Dementia patient’s admission using EHRs | |
| Lee et al. [75] | 2021 | Proposed classification of opioid usage for total joint replacement patients | |
| Kumar et al. [15] | 2021 | Developed Ensemble ML approaches for morbidity identification from clinical data | |
| Medical image analysis | Zebari et al. [76] | 2020 | Improved automated segmentation of pectoral muscle and breast cancer boundary in mammogram images |
| Zech et al. [77] | 2018 | Developed Automated annotation of clinical radiology reports using natural language-based models | |
| Jing et al. [78] | 2018 | Developed Automatic generation of radiology imaging reports | |
| Li et al. [79] | 2021 | Developed solution Using histopathological images to classify and diagnose lung cancer subtypes | |
| Mandal et al. [64] | 2018 | Surveyed on medical imaging transformation across the healthcare spectrum | |
| Umamaheswari et al. [80] | 2018 | Developed digital imaging to Classify and segment acute lymphoblastic leukemia cells | |
| Wang et al. [81] | 2019 | Used sparse multi-regularization learning and multi-level dual network features to classify breast cancer images | |
| Abhinaav et al. [82] | 2019 | Developed ML mechanism using extracted Papanicolaou Smear images to detect abnormality and severity of cells | |
| Bora et al. [83] | 2020 | Proposed a radiograph generating reconstruction mechanism for facilitating AI in medical imaging | |
| Treatment | Weng et al. [84] | 2017 | Provided analysis on ML prediction of cardiovascular risk using routine medical data |
| Fatima et al. [85] | 2017 | Surveyed ML algorithms for disease diagnosis | |
| Zhao et al. [86] | 2019 | Applied ML approach for drug repositioning of Schizophrenia and anxiety disorders | |
| Jamshidi et al. [87] | 2020 | Proposed DL approaches for diagnosis and treatment of the novel coronavirus | |
| Li et al. [88] | 2019 | Assessed ML for predicting severity in liver fibrosis for chronic HBV | |
| Noaro et al. [89] | 2021 | Developed ML-based model for improving the calculation of Insulin Bolus of type-1 diabetes therapy | |
| Yang et al. [90] | 2017 | Proposed a combined ML algorithm for effective medical diagnosis and treatment using an inference engine | |
| Chaitra et al. [91] | 2020 | Proposed an ML model for diagnostic prediction of autism spectrum disorder | |
| Computer aided-detection (CAD) | Saygılı et al. [92] | 2021 | Developed ML methods and soft computing strategies for computer-aided Covid-19 detection from CT-Scan and X-ray images |
| Abdelsalam et al. [93] | 2018 | Presented the computer-aided detection of leukemia using microscopic blood-based ML | |
| Wu et al. [94] | 2018 | Developed DL techniques to detect hookworm in wireless endoscopy images | |
| Yu et al. [95] | 2021 | Implemented ML-aided imaging analytics for histopathological image diagnosis | |
| Disease prediction and diagnosis | Suresh et al. [96] | 2017 | Presented clinical event prediction and analysis using DL mechanisms |
| Rau et al. [97] | 2018 | Presented a study using ML for predicting the mortality rate of the isolate to severe traumatic brain injury patients | |
| Kim et al. [98] | 2017 | Proposed ML-based diagnosis of major depressive disorder by combining heart rate data | |
| Pellegrini et al. [99] | 2018 | Developed ML assisted diagnosis of dementia and cognitive impairment | |
| Akbulut et al. [100] | 2018 | Presented an ML system for foetal health condition prediction based on maternal clinical history | |
| Karhade et al. [101] | 2018 | Developed ML algorithms for predicting survival of a 5-year spinal chordoma patient | |
| Abdar et al. [102] | 2019 | Proposed a new ML technique for the diagnosis of coronary artery disease | |
| Burdick et al. [103] | 2020 | Used ML to develop a prediction system for respiratory decompensation in coronavirus patients | |
| Hashem et al. [104] | 2020 | Developed ML models for diagnosis of HCV-related chronic liver disease and hepatocellular carcinoma | |
| Magesh et al. [105] | 2020 | Developed explainable ML using LIME on imagery computers model for pre-detection of Parkinson’s disease | |
| Shen et al. [106] | 2021 | Presented risk predicting ML models in the diagnosis of Escherichia coli sepsis in patients | |
| Montolío et al. [107] | 2020 | ML in disability prediction and diagnosis of multiple sclerosis utilizing optical coherence tomography computers | |
| Clinical time-series data | Yu-Wei et al. [108] | 2019 | Used recurrent neural networks for prediction of unplanned ICU readmission |
| Xie et al. [110] | 2020 | Compared benchmarks of classical time-series ML models with new algorithms on glucose prediction in the blood of type-1 diabetes | |
| Pezoulas et al. [111] | 2021 | Used time-series gene expression data for the detection of a diagnostic biomarker in Kawasaki disease | |
| Nancy et al. [112] | 2017 | Observed a bio-statistical quarry approach for the classification of multivariate clinical time-series data observed at varying intervals | |
| Froc et al. [113] | 2021 | Characterized urinary tract endometriosis over a collected one-year national series data of 232 patients | |
| Wallace et al. [114] | 2018 | Simplified the function of speech recognition admissibility in medical documentation aspects | |
| Clinical speech and audio processing | Zamani et al. [115] | 2020 | Presented an automated Pterygium detection using ML/DL approaches |
| Prognosis | Ke et al. [117] | 2019 | Presented an automated Image annotation based on multi-label data augmentation and deep CNNs |
| Davi et al. [118] | 2019 | Utilized ML and human genome data for severe dengue prognosis | |
| Liu et al. [119] | 2019 | Proposed a weakly supervised DL technique for brain disease prognosis using MRI data and incomplete clinical scores | |
| Fang et al. [120] | 2020 | Discussed the ML approach for feature selection in stroke prognosis | |
| Wang et al. [121] | 2019 | Presented transfer learning least squares SVM mechanism in bladder cancer prognosis | |
| Cai et al. [122] | 2020 | Presented ML models and CT quantification approaches for assessment of disease prognosis and severity of coronavirus patients | |
| Zack et al. [123] | 2019 | Developed ML techniques for forecasting patient prognosis after percutaneous coronary intervention | |
| He et al. [124] | 2021 | Developed ML prediction model for acute kidney injury following after donation |