Interpretability in the medical field: A systematic mapping and review study

H Hakkoum, I Abnane, A Idri - Applied Soft Computing, 2022 - Elsevier
Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing
to the availability of historical datasets and advanced computational power. This growth is …

Using heterogeneous sources of data and interpretability of prediction models to explain the characteristics of careless respondents in survey data

L Kopitar, G Stiglic - Scientific reports, 2023 - nature.com
Prior to further processing, completed questionnaires must be screened for the presence of
careless respondents. Different people will respond to surveys in different ways. Some take …

[HTML][HTML] Development of prediction models using machine learning algorithms for girls with suspected central precocious puberty: retrospective study

L Pan, G Liu, X Mao, H Li, J Zhang… - JMIR medical …, 2019 - medinform.jmir.org
Background: Central precocious puberty (CPP) in girls seriously affects their physical and
mental development in childhood. The method of diagnosis—gonadotropin-releasing …

Development and validation of clinical diagnostic model for girls with central precocious puberty: machine-learning approaches

QTV Huynh, NQK Le, SY Huang, BT Ho, TH Vu… - PLoS …, 2022 - journals.plos.org
Background A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test
which solely focused on LH 30-minute post-stimulation was considered to identify girls with …

How much is the black box? The value of explainability in machine learning models

J Wanner, LV Herm, C Janiesch - 2020 - aisel.aisnet.org
Abstract Machine learning enables computers to learn from data and fuels artificial
intelligence systems with capabilities to make even super-human decisions. Yet, despite …

Genetic algorithm based automatic out-patient experience management system (GAPEM) using RFIDs and sensors

S Safdar, SA Khan, A Shaukat, MU Akram - IEEE Access, 2020 - ieeexplore.ieee.org
This article introduces a novel framework which combines the outputs from Radio Frequency
Identification (RFID) technology, the automated outpatient feedback survey form, Hospital …

Gaining insights into patient satisfaction through interpretable machine learning

N Liu, S Kumara, E Reich - IEEE journal of biomedical and …, 2020 - ieeexplore.ieee.org
Patient satisfaction is a key performance indicator of patient-centered care and hospital
reimbursement. To discover the major factors that affect patient experiences is considered …

[PDF][PDF] A Systematic Map of Interpretability in Medicine.

H Hakkoum, I Abnane, A Idri - HEALTHINF, 2022 - scitepress.org
Machine learning (ML) has been rapidly growing, mainly owing to the availability of
historical datasets and advanced computational power. This growth is still facing a set of …

Analysis of Patient Satisfaction through Interpretable Machine Learning Algorithms

C Jamunadevi, R Subith, S Deepa… - … Conference on Edge …, 2023 - ieeexplore.ieee.org
This research study intends to reduce the features and predict whether the patients are
satisfied with the service provided by the hospitals. The proposed system classifies top five …

Understanding Employee Attrition Using Explainable AI

N Sabbineni - 2020 - etda.libraries.psu.edu
Artificial Intelligence and Machine Learning communities and applications have come a long
way from being result focused to being more human intuitive, due to the myriad of fields that …