AI Medical Compendium Topic:
Forecasting

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Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis.

JAMA network open
IMPORTANCE: Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial ...

[Data-driven integrated diagnostics: the natural evolution of clinical chemistry?].

Nederlands tijdschrift voor geneeskunde
In the near future, making a correct medical diagnosis will be increasingly supported by artificial intelligence. The development of algorithms that integrate all data from an individual into the diagnostic process calls for a multidisciplinary appro...

Artificial intelligence and colonoscopy: Current status and future perspectives.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is s...

Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia.

Computational intelligence and neuroscience
An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi-Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The s...

Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions.

Science and engineering ethics
Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI t...

[Neural network: A future in pathology?].

Annales de pathologie
Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models...

Deep learning and process understanding for data-driven Earth system science.

Nature
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, ...

Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.

Journal of cardiac failure
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...

Stock Market Forecasting Using Restricted Gene Expression Programming.

Computational intelligence and neuroscience
Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed ...