scholarly journals A Decision Support System to Facilitate Identification of Musculoskeletal Impairments and Propose Recommendations Using Gait Analysis in Children With Cerebral Palsy

2020 ◽  
Vol 8 ◽  
Author(s):  
Kohleth Chia ◽  
Igor Fischer ◽  
Pam Thomason ◽  
H. Kerr Graham ◽  
Morgan Sangeux
Keyword(s):  
Cerebral Palsy ◽  
Decision Support ◽  
Decision Support System ◽  
Gait Analysis ◽  
Support System ◽  
Class Imbalance ◽  
Brier Score ◽  
Musculoskeletal Impairments ◽  
Children With Cerebral Palsy ◽  
Evaluation Metric

The identification of musculoskeletal impairments from gait analysis in children with cerebral palsy is a complex task, as is formulating (surgical) recommendations. In this paper, we present how we built a decision support system based on gait kinematics, anthropometrics, and physical examination data. The decision support system was trained to learn the association between these data and the list of impairments and recommendations formulated historically by experienced clinicians. Our aim was 2-fold, train a computational model that would be representative of data-based clinical reasoning in our center, and support new or junior clinicians by providing pre-processed impairments and recommendations with the associated supportive evidence. We present some of the challenges we faced, such as the issues of dimensionality reduction for kinematic data, missing data imputations, class imbalance and choosing an appropriate model evaluation metric. Most models, i.e., one model for each impairments and recommendations, achieved a weighted Brier score lower than 0.20, and sensitivity and specificity greater than 0.70 and 0.80, respectively. The results of the models are accessible through a web-based application which displays the probability predictions as well as the (up to) 5 best predictors.

Developing an Evidence-Based Clinical Decision-Support System to Enhance Prosthetic Prescription

2021 ◽  
Vol 10 (1) ◽  
pp. 121-125
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Gait Analysis ◽  
Support System ◽  
Patient Specific ◽  
Evidence Based ◽  
Extensive Literature ◽  
Analysis Tool ◽  
Care Providers

With the rapid increase in the global amputee population, there is a clear need to assist amputee care providers with their decision-making during the prosthetic prescription process. To achieve this, an evidence-based decision support system that encompasses existing literature, current decision-making strategies employed by amputee care providers and patient-specific factors is proposed. Based on an extensive literature review combined with natural language processing and expert survey, the factors influencing the current decision-making of amputee care providers in prosthetic prescription were identified. Following that, the decision-making strategies employed by expert and novice prosthetists were captured and analyzed. Finally, a fundamental understanding of the effect gait analysis has on the decision-making strategies of prosthetists was studied. Findings from this work lay the foundation for developing a real-time decision support system integrated with a portable gait analysis tool to enhance prescription processes. This is critical in the low-income countries where there is a scarcity of amputee care providers and resources for an appropriate prescription.

scholarly journals Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data

2018 ◽  
Vol 8 (9) ◽  
pp. 1597 ◽  
Author(s):  
Huirui Han ◽  
Mengxing Huang ◽  
Yu Zhang ◽  
Jing Liu
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Class Imbalance ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Essential Information ◽  
Learning Techniques

The clinical decision support system provides an automatic diagnosis of human diseases using machine learning techniques to analyze features of patients and classify patients according to different diseases. An analysis of real-world electronic health record (EHR) data has revealed that a patient could be diagnosed as having more than one disease simultaneously. Therefore, to suggest a list of possible diseases, the task of classifying patients is transferred into a multi-label learning task. For most multi-label learning techniques, the class imbalance that exists in EHR data may bring about performance degradation. Cross-Coupling Aggregation (COCOA) is a typical multi-label learning approach that is aimed at leveraging label correlation and exploring class imbalance. For each label, COCOA aggregates the predictive result of a binary-class imbalance classifier corresponding to this label as well as the predictive results of some multi-class imbalance classifiers corresponding to the pairs of this label and other labels. However, class imbalance may still affect a multi-class imbalance learner when the number of a coupling label is too small. To improve the performance of COCOA, a regularized ensemble approach integrated into a multi-class classification process of COCOA named as COCOA-RE is presented in this paper. To provide disease diagnosis, COCOA-RE learns from the available laboratory test reports and essential information of patients and produces a multi-label predictive model. Experiments were performed to validate the effectiveness of the proposed multi-label learning approach, and the proposed approach was implemented in a developed system prototype.

Simulation of muscle contracture in cerebral palsy (SiMusCP). validation of a decision support system for hamstrings lengthening

2013 ◽  
Vol 38 ◽  
pp. S14
Author(s):  
Eric Desailly ◽  
Abdennour Sebsadji ◽  
Daniel Yepremian ◽  
Farid Hareb ◽  
Néjib Khouri
Keyword(s):  
Cerebral Palsy ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Muscle Contracture

Decision Support System Development Based On Android Using Analytical Hierarchy Process (AHP) Method For Determining The Middle Small Business Development Priority In Sorong City

2019 ◽  
Vol 5 (2) ◽  
pp. 25-39
Author(s):  
Luluk Suryani ◽  
Raditya Faisal Waliulu ◽  
Ery Murniyasih
Keyword(s):  
Decision Support ◽  
Small Business ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
System Development ◽  
Business Development ◽  
Ahp Method ◽  
Small Business Development ◽  
Hierarchy Process

Usaha Kecil Menengah (UKM) adalah salah satu penggerak perekonomian suatu daerah, termasuk Kota Sorong. UKM di Kota Sorong belum berkembang secara optimal. Ada beberapa penyebab diantaranya adalah mengenai finansial, lokasi, bahan baku dan lain-lain. Untuk menyelesaikan permasalah tersebut peneliti terdorong untuk melakukan pengembangan Aplikasi yang dapat membantu menentukan prioritas UKM yang sesuai dengan kondisi pelaku usaha. Pada penelitian ini akan digunakan metode Analitycal Hierarchy Process (AHP), untuk pengambilan keputusannya. Metode AHP dipilih karena mampu menyeleksi dan menentukan alternatif terbaik dari sejumlah alternatif yang tersedia. Dalam hal ini alternatif yang dimaksudkan yaitu UKM terbaik yang dapat dipilih oleh pelaku usaha sesuai dengan kriteria yang telah ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perankingan yang akan menentukan alternatif yang optimal, yaitu UKM. Aplikasi Sistem Pendukung Keputusan yang dikembangkan berbasis Android, dimana pengguna akan mudah menggunakannya sewaktu-waktu jika terjadi perubahan bobot pada kriteria atau intensitas.  Hasil akhir menunjukkan bahwa metode AHP berhasil diterapkan pada Aplikasi Penentuan Prioritas Pengembangan UKM.

Sign in / Sign up

Export Citation Format

Share Document