AIMC Topic: Machine Learning

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Sequential classification system for recognition of malaria infection using peripheral blood cell images.

Journal of clinical pathology
AIMS: Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood ...

Novel body fat estimation using machine learning and 3-dimensional optical imaging.

European journal of clinical nutrition
Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat predicti...

Machine Learning Diagnostic Modeling for Classifying Fibromyalgia Using B-mode Ultrasound Images.

Ultrasonic imaging
Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discrimina...

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Hypertension (Dallas, Tex. : 1979)
Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of c...

Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network.

Journal of chemical information and modeling
The use of machine learning in chemistry is on the rise for the prediction of chemical properties. The input feature representation or descriptor in these applications is an important factor that affects the accuracy as well as the extent of the expl...

From Bits of Data to Bits of Knowledge-An On-Board Classification Framework for Wearable Sensing Systems.

Sensors (Basel, Switzerland)
Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imp...

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

The AAPS journal
Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to...

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Scientific reports
Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents ...

Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD.

NeuroImage. Clinical
BACKGROUND: Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in ...