AIMC Topic: Machine Learning

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Machine learning applications in the diagnosis of leukemia: Current trends and future directions.

International journal of laboratory hematology
Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing tr...

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

Experimental hematology
Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling tech...

Designing combination therapies with modeling chaperoned machine learning.

PLoS computational biology
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational d...

An analysis of training and generalization errors in shallow and deep networks.

Neural networks : the official journal of the International Neural Network Society
This paper is motivated by an open problem around deep networks, namely, the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case ...

External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were pre...

Clustering suicides: A data-driven, exploratory machine learning approach.

European psychiatry : the journal of the Association of European Psychiatrists
Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into "violent" versus "non-violent" method. Interestingly, since the proposition of this dichot...

Named entity recognition in electronic health records using transfer learning bootstrapped Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Neural networks (NNs) have become the state of the art in many machine learning applications, such as image, sound (LeCun et al., 2015) and natural language processing (Young et al., 2017; Linggard et al., 2012). However, the success of NNs remains d...