AIMC Topic: Sensitivity and Specificity

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Severe Dengue Prognosis Using Human Genome Data and Machine Learning.

IEEE transactions on bio-medical engineering
UNLABELLED: Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate.

Potential roles of artificial intelligence learning and faecal immunochemical testing for prioritisation of colonoscopy in anaemia.

British journal of haematology
Iron deficiency anaemia (IDA) is the most common cause of anaemia and a frequent indication for colonoscopy, although the prevalence of colorectal cancer (CRC) in IDA is low. Measurement of faecal haemoglobin by immunochemical techniques (FIT) is use...

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.

NeuroImage. Clinical
We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detecti...

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

International journal of medical informatics
OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cance...

Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis.

Annals of emergency medicine
STUDY OBJECTIVE: The Third International Consensus Definitions (Sepsis-3) Task Force recommended the use of the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score to screen patients for sepsis outside of the ICU. However, subseq...

Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data.

International journal of environmental research and public health
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic vari...

Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.

PloS one
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause c...

Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error.

Systematic reviews
BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm ...