AIMC Topic: Logistic Models

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Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might sol...

An Improvised Classification Model for Predicting Delirium.

Studies in health technology and informatics
With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, th...

Artificial intelligence in drug combination therapy.

Briefings in bioinformatics
Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a...

Eyelid Movement Command Classification Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four...

Predicting Gastrointestinal Bleeding Events from Multimodal In-Hospital Electronic Health Records Using Deep Fusion Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Applying machine learning (ML) methods on electronic health records (EHRs) that accurately predict the occurrence of a variety of diseases or complications related to medications can contribute to improve healthcare quality. EHRs by nature contain mu...

Application of Machine Learning to Prediction of Surgical Site Infection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducte...

Daytime Data and LSTM can Forecast Tomorrow's Stress, Health, and Happiness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day's well-being (specifically focusing ...

Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk ...

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury.

The American surgeon
Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learnin...

Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.

Epidemiology (Cambridge, Mass.)
BACKGROUND: Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversel...