AIMC Topic: Health Status

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An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

British journal of anaesthesia
BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores ...

Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?

PloS one
BACKGROUND: Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all ...

Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.

JACC. Heart failure
OBJECTIVES: This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with...

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

PloS one
Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To impr...

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.

Scientific reports
Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activi...

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.

IEEE journal of biomedical and health informatics
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-rep...

How do "robopets" impact the health and well-being of residents in care homes? A systematic review of qualitative and quantitative evidence.

International journal of older people nursing
BACKGROUND: Robopets are small animal-like robots which have the appearance and behavioural characteristics of pets.

Construction of medical equipment-based doctor health monitoring system.

Journal of medical systems
The health status of doctors has been overlooked by the society and even the doctors themselves, especially those doctors who work long hours. Their attention is always on patients, so they are more likely to ignore their own health problems. Therefo...

Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation
OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions.