AIMC Topic: Machine Learning

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Artificial Intelligence in Cataract Surgery: A Systematic Review.

Translational vision science & technology
PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos.

A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.

European journal of dermatology : EJD
Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The reactivation rate of LCH exceeds 30%. However, an effective prediction model to predict reactivatio...

[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.

Meta predictive learning model of languages in neural circuits.

Physical review. E
Large language models based on self-attention mechanisms have achieved astonishing performances, not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not ...

Artificial intelligence- image learning and its applications in neurooncology: a review.

JPMA. The Journal of the Pakistan Medical Association
Image learning involves using artificial intelligence (AI) to analyse radiological images. Various machine and deeplearning- based techniques have been employed to process images and extract relevant features. These can later be used to detect tumour...

The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.

JPMA. The Journal of the Pakistan Medical Association
Radio genomics is an exciting new area that uses diagnostic imaging to discover genetic features of diseases. In this review, we carefully examined existing literature to evaluate the role of artificial intelligence (AI) and machine learning (ML) on ...

[Identification of Protein-Coding Gene Markers in Breast Invasive Carcinoma Based on Machine Learning].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Objective To screen out the biomarkers linked to prognosis of breast invasive carcinoma based on the analysis of transcriptome data by random forest (RF),extreme gradient boosting (XGBoost),light gradient boosting machine (LightGBM),and categorical b...

Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

JCO clinical cancer informatics
PURPOSE: Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to asses...

Machine learning and new insights for breast cancer diagnosis.

The Journal of international medical research
Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and bre...

Explainable Machine Learning Model to Preoperatively Predict Postoperative Complications in Inpatients With Cancer Undergoing Major Operations.

JCO clinical cancer informatics
PURPOSE: Preoperative prediction of postoperative complications (PCs) in inpatients with cancer is challenging. We developed an explainable machine learning (ML) model to predict PCs in a heterogenous population of inpatients with cancer undergoing s...