# Machine Learning in Healthcare

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/machine-learning-in-healthcare/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of deep learning techniques in healthcare, particularly in the analysis of electronic health records (EHR). The papers cover a wide range of topics including predictive modeling, patient similarity, disease risk prediction, medical concept embedding, and temporal data analysis. The goal is to leverage deep learning to improve healthcare decision-making and enable precision medicine. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t13702 |
| Works | 275 |

## Topic papers all

Showing 15 of 275.

- [Artificial intelligence in healthcare: past, present and future](https://scholariq.org/papers/artificial-intelligence-in-healthcare-past-present-and-future/)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://scholariq.org/papers/recurrent-neural-networks-for-multivariate-time-series-with-missing-values-2/)
- [Deep Learning for Health Informatics](https://scholariq.org/papers/deep-learning-for-health-informatics/)
- [XAI—Explainable artificial intelligence](https://scholariq.org/papers/xai-explainable-artificial-intelligence/)
- [Can AI Help in Screening Viral and COVID-19 Pneumonia?](https://scholariq.org/papers/can-ai-help-in-screening-viral-and-covid-19-pneumonia/)
- [Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence](https://scholariq.org/papers/interpreting-black-box-models-a-review-on-explainable-artificial-intelligence/)
- [Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records](https://scholariq.org/papers/deep-patient-an-unsupervised-representation-to-predict-the-future-of-patients/)
- [Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data](https://scholariq.org/papers/potential-biases-in-machine-learning-algorithms-using-electronic-health-record/)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://scholariq.org/papers/score-based-generative-modeling-through-stochastic-differential-equations/)
- [<p>Taiwan’s National Health Insurance Research Database: past and future</p>](https://scholariq.org/papers/and-lt-p-and-gt-taiwan-s-national-health-insurance-research-database-past-and/)
- [Artificial Intelligence in Precision Cardiovascular Medicine](https://scholariq.org/papers/artificial-intelligence-in-precision-cardiovascular-medicine/)
- [Clinical information extraction applications: A literature review](https://scholariq.org/papers/clinical-information-extraction-applications-a-literature-review/)
- [Predictive data mining in clinical medicine: Current issues and guidelines](https://scholariq.org/papers/predictive-data-mining-in-clinical-medicine-current-issues-and-guidelines/)
- [Toward expert-level medical question answering with large language models](https://scholariq.org/papers/toward-expert-level-medical-question-answering-with-large-language-models/)
- [Flexible Imputation of Missing Data](https://scholariq.org/papers/flexible-imputation-of-missing-data/)

## Topic primary papers

Showing 15 of 62.

- [Deep Learning for Health Informatics](https://scholariq.org/papers/deep-learning-for-health-informatics/)
- [Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records](https://scholariq.org/papers/deep-patient-an-unsupervised-representation-to-predict-the-future-of-patients/)
- [Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data](https://scholariq.org/papers/potential-biases-in-machine-learning-algorithms-using-electronic-health-record/)
- [Artificial Intelligence in Precision Cardiovascular Medicine](https://scholariq.org/papers/artificial-intelligence-in-precision-cardiovascular-medicine/)
- [Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence](https://scholariq.org/papers/evaluation-and-accurate-diagnoses-of-pediatric-diseases-using-artificial/)
- [Preparing for Precision Medicine](https://scholariq.org/papers/preparing-for-precision-medicine/)
- [How to Read Articles That Use Machine Learning](https://scholariq.org/papers/how-to-read-articles-that-use-machine-learning/)
- [From Big Data to Precision Medicine](https://scholariq.org/papers/from-big-data-to-precision-medicine/)
- [From hype to reality: data science enabling personalized medicine](https://scholariq.org/papers/from-hype-to-reality-data-science-enabling-personalized-medicine/)
- [Benchmarking deep learning models on large healthcare datasets](https://scholariq.org/papers/benchmarking-deep-learning-models-on-large-healthcare-datasets/)
- [Linking inpatient clinical registry data to Medicare claims data using indirect identifiers](https://scholariq.org/papers/linking-inpatient-clinical-registry-data-to-medicare-claims-data-using-indirect/)
- [Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare](https://scholariq.org/papers/clinical-artificial-intelligence-quality-improvement-towards-continual/)
- [The Chang Gung Research Database—A multi‐institutional electronic medical records database for real‐world epidemiological studies in Taiwan](https://scholariq.org/papers/the-chang-gung-research-database-a-multi-institutional-electronic-medical/)
- [Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review](https://scholariq.org/papers/explainable-artificial-intelligence-models-using-real-world-electronic-health/)
- [Recommendations for Reporting Machine Learning Analyses in Clinical Research](https://scholariq.org/papers/recommendations-for-reporting-machine-learning-analyses-in-clinical-research/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.