AIMC Topic: Severity of Illness Index

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An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Journal of the National Cancer Institute
BACKGROUND: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid applicati...

An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures.

Journal of psychiatry & neuroscience : JPN
BACKGROUND: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case–control classification and predicting conver...

Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs.

Journal of digital imaging
Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using the 0-4 Kellgren Lawrence (KL) grading system where 0 is normal, 1 shows doubtful signs of OA, 2 is mild OA, 3 is moderate OA, and 4 is severe OA. KL grading ...

Burn wound classification model using spatial frequency-domain imaging and machine learning.

Journal of biomedical optics
Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have...

The economic cost of robotic rehabilitation for adult stroke patients: a systematic review.

JBI database of systematic reviews and implementation reports
OBJECTIVE: The objective of this review was to examine the economic cost of robotic therapy compared to conventional therapy for adult stroke patients, from the perspective of hospitals.

Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.

Journal of the American Heart Association
Background An angiography-based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80. Methods and Results With a 4:1 ratio, 1501 patients with 1501 intermediate lesions we...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...