AIMC Topic: Machine Learning

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A Proof-of-Concept Development on Speech Analysis for Concussion Detection.

Studies in health technology and informatics
Speech signal analysis to support objective clinical decision-making has gained immense interest, especially in neurological disorders. This research assessed the feasibility of speech analysis on the detection of concussions. Using a speech dataset ...

Development and Validation of Machine-Learning Algorithms to Predict the Onset of Depression Using Electronic Health Record Data: A Prognostic Modeling Study.

Studies in health technology and informatics
INTRODUCTION: Early detection and intervention are crucial for reducing the impacts of depression and associated healthcare costs. Few studies have used electronic health records (EHR) and machine learning (ML) with a longitudinal design to predict d...

Towards Automated Eye Movement Characterization for Stroke Patients Using Synthetic Video Data and Machine Learning.

Studies in health technology and informatics
Stroke is a critical medical emergency that can cause permanent disability or death. Rapid identification of stroke, especially in prehospital settings, is crucial for timely treatment. Video analysis and machine learning (ML) could facilitate the pr...

Constrained Tensor Factorization for Cancer Phenotyping and Mortality Prediction.

Studies in health technology and informatics
Electronic health records (EHR) enable machine learning methods like tensor factorization to extract computational phenotypes. Using Northwestern Medicine data (2000-2015), we analyzed breast, prostate, colorectal, and lung cancer cohorts to predict ...

Machine-Learning-Based Prediction of Suicide Risk Using Preliminary Questionnaire and Consultation Text.

Studies in health technology and informatics
In Japan, chat-based mental health counseling services have low response rates due to understaffing. In this article, machine learning (ML) based suicide risk classification methods are proposed. A dataset was constructed including a medical question...

Using Machine Learning Techniques for Lung Cancer Survival Prediction.

Studies in health technology and informatics
Lung cancer is one of the most common and lethal types of cancer. Early diagnosis and appropriate treatment play a crucial role in reducing mortality. Artificial intelligence techniques can be used to support clinical approaches to lung cancer, helpi...

Predicting Postpartum Depression Risk Using Social Determinants of Health.

Studies in health technology and informatics
Postpartum depression (PPD) affects approximately 20% of women after childbirth and has complex etiology. Existing predictive models of PPD lack training on large, national datasets and comprehensive integration of clinical and social determinants. T...

Predicting Antidepressant Deprescription with Machine Learning Using Administrative Data.

Studies in health technology and informatics
The high prevalence of failed antidepressant deprescription attempts makes it difficult for clinicians to identify suitable candidates for discontinuation. In this study, we use the Pharmaceutical Benefits Scheme (PBS) dataset, which contains rich lo...

Predicting Nephrectomy Risk in Patients with Renal Cancer Using Real-World Electronic Health Records.

Studies in health technology and informatics
Nephrectomy, the surgical removal of a kidney, is a critical treatment for renal cancer, and predicting its likelihood can help guide clinical decision-making and optimize preoperative planning. This study utilized real-world electronic health record...

AI Bias and Confounding Risk in Health Feature Engineering for Machine Learning Classification Task.

Studies in health technology and informatics
Recent advancements in machine learning bring unique opportunities in health fields but also pose considerable challenges. Due to stringent ethical considerations and resource constraints, health data can vary in scope, population coverage, and colle...