AIMC Topic: Female

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Segmentation for mammography classification utilizing deep convolutional neural network.

BMC medical imaging
BACKGROUND: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently...

Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.

BMC infectious diseases
BACKGROUND: During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identi...

Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

BMC anesthesiology
BACKGROUND: To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequent...

Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.

BMC emergency medicine
BACKGROUND: Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study de...

An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.

BMC medical informatics and decision making
BACKGROUND: Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. This research aimed to develop an explain...

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

BMC medical informatics and decision making
BACKGROUND: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management.

A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.

BMC medical informatics and decision making
BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by reduced hematological triad. It is challenging to distinguish and diagnose these two diseases. Hence...

Machine learning models predict the progression of long-term renal insufficiency in patients with renal cancer after radical nephrectomy.

BMC nephrology
BACKGROUND: Chronic Kidney Disease (CKD) is a common severe complication after radical nephrectomy in patients with renal cancer. The timely and accurate prediction of the long-term progression of renal function post-surgery is crucial for early inte...

Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

BMC psychiatry
OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association betwe...