Research Article Details
| Article ID: | A42696 |
| PMID: | 33739235 |
| Source: | Crit Rev Clin Lab Sci |
| Title: | Potential value and impact of data mining and machine learning in clinical diagnostics. |
| Abstract: | Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field. |
| DOI: | 10.1080/10408363.2020.1857681 |
| Strategy ID | Therapy Strategy | Synonyms | Therapy Targets | Therapy Drugs |
|---|
| Target ID | Target Name | GENE | Action | Class | UniProtKB ID | Entry Name |
|---|
| Diseases ID | DO ID | Disease Name | Definition | Class | |
|---|---|---|---|---|---|
| I17 | 1596 | Mental depression | Mental depression | disease of mental health/ cognitive disorder/ mood disorder | Details |
| I12 | 10763 | Hypertension | An artery disease characterized by chronic elevated blood pressure in the arteries. https://en.wikipedia.org/wiki/Hypertension, https://www.ncbi.nlm.nih.gov/pubmed/24352797 | disease of anatomical entity/ cardiovascular system disease/vascular disease/ artery disease | Details |
| I05 | 9352 | Type 2 diabetes mellitus | A diabetes that is characterized by chronic hyperglycaemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both. A diabetes mellitus that is characterized by high blood sugar, insulin resistance, and relative lack of insulin. http://en.wikipedia.org/wiki/Diabetes, http://en.wikipedia.org/wiki/Diabetes_mellitus_type_2 | disease of metabolism/inherited metabolic disorder/ carbohydrate metabolic disorder/glucose metabolism disease/diabetes/ diabetes mellitus | Details |
| I14 | 9970 | Obesity | An overnutrition that is characterized by excess body fat, traditionally defined as an elevated ratio of weight to height (specifically 30 kilograms per meter squared), has_material_basis_in a multifactorial etiology related to excess nutrition intake, decreased caloric utilization, and genetic susceptibility, and possibly medications and certain disorders of metabolism, endocrine function, and mental illness. https://en.wikipedia.org/wiki/Obesity | disease of metabolism/acquired metabolic disease/ nutrition disease/overnutrition | Details |
| Drug ID | Drug Name | Type | DrugBank ID | Targets | Category | Latest Progress | |
|---|---|---|---|---|---|---|---|
| D328 | Serine | Chemical drug | DB00133 | SRR | Improve insulin resistance | Under clinical trials | Details |
| D579 | Emfilermin | Miscellany | -- | adipocytes | Enhance lipid metabolism | Under investigation | Details |
| D316 | S-adenosyl-L-methionine | Chemical drug | DB00118 | GNMT cofactor | Antiviral | Under clinical trials | Details |