AIMC Topic: Humans

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netDx: interpretable patient classification using integrated patient similarity networks.

Molecular systems biology
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse da...

Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

BMC medical informatics and decision making
BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). H...

TADKB: Family classification and a knowledge base of topologically associating domains.

BMC genomics
BACKGROUND: Topologically associating domains (TADs) are considered the structural and functional units of the genome. However, there is a lack of an integrated resource for TADs in the literature where researchers can obtain family classifications a...

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study.

NeuroImage. Clinical
Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility...

Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aim...

Review of Medical Decision Support and Machine-Learning Methods.

Veterinary pathology
Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support...

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

Cancer letters
This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 sol...

Analysis and evaluation of handwriting in patients with Parkinson's disease using kinematic, geometrical, and non-linear features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting a...