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ai4ph-data-challenge-2024

This is the main site for the 2024 AI4PH Summer Institute Data Challenge.

bloodlaboratorytestredundancy

crowdsourcing-for-digital-public-health-surveillance

To explore and evaluate the application of crowdsourcing, in general, and AMT, in specific, for developing digital public health surveillance systems, we collected 296,166 crowd-generated labels for 98,722 tweets, labelled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviours related to physical activity, sedentary behaviour, and sleep quality (PASS) among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied four statistical consensus methods that are agnostic of task features and only focus on worker labelling behaviour. Moreover, to model the meta-information associated with each labelling task and leverage the potentials of context-sensitive data in the truth inference process, we developed seven ML models, including traditional classifiers (offline and active), a deep-learning-based classification model, and a hybrid convolutional neural network (CNN) model.

delirium_prediction

discharge-prediction_eicu-crd

This repository presents the implementation of different machine learning architectures to determine the efficacy of the Acute Physiology andChronic Health Evaluation (APACHE) IV score as well as the patient characteristics that comprise it to predict the discharge destination for critically ill patients within 24 hours of ICU admission.

evaluation_metrics_considering_case_difficulty

icd10-unsupervised-feature-selection

Unsupervised Feature Selection to Identify Important ICD-10 Codes for Machine Learning

icu_outcome_prediction_transfer_learning

Using domain adaptation and inductive transfer learning to improve patient outcome prediction in the intensive care unit

lpheada-labelled-public-health-dataset

LPHEADA is a multicountry and fully Labelled digital Public HEAlth DAtaset of tweets originated in Australia, Canada, UK, or US between November 28th, 2018 to June 19th, 2020. This dataset contains 366,405 crowd-generated labels (three labels per tweet) for 122,135 PASS-related tweets, labelled by 708 unique annotators on Amazon Mechanical Turk.

measuring_case_difficulty

mhealth_emotional_state-transition_detection_public

nlp--modes_of_communication--families-healthcare_professionals

Natural language processing to measure communication between healthcare professionals and family members of critically ill patients

python-starter

A simple starter project written in python!

rl4cad

RL4CAD: Personalized Decision Making for Coronary Artery Disease Treatment using Offline Reinforcement Learning