Big Data Center

Big Data Center IMERI was established on January 31st 2022 by the collaboration between the Indonesian Medical Education and Research Institute (IMERI) Faculty of Medicine Universitas Indonesia and UMG IDEALAB. Big Data Center IMERI was the first national health data center to support biomedical research on a national scale as well as wide development potential with a standardized billing system and powerful computing capabilities.

Big Data Center IMERI acts as a one-stop-solution platform to increase the value and productivity of health data, connecting researchers from the health cluster with researchers from engineering, data scientists, and computer sciences. BDC IMERI provides the service nationally, particularly for FMUI and 9 hospitals under the Academic Health System UI. The existence of this data center can further enable the development of Electronic Data Capture (EDC) which currently exists in IMERI. The data center acts as cloud computing, where researchers get access to high-performance computing (HPC), using algorithms that have been developed by IMERI. The billing system is implemented in the operation of the IMERI Health Data Center to optimize the use of cloud computing capabilities and services for using artificial intelligence-based algorithms. The following are our services:

Singel Sign On
Cloud Storage
Data Catalog
Data Labeling
Cloud Computing Analytics
BDC Consultation
Repository and Publications

Our Services

a. Cloud Storage
Users can store huge amounts of health research data in cloud-based storage with ease, convenience, and guaranteed security. Users can access data anywhere without the need to store and carry it physically.

b. Data Catalog
Anonymized health research data can be stored in the form of a data catalog. Owners can select accessibility as “public” or “private” to collaborate with data analysts in producing research and innovative products.

c. Data Labeling
This platform offers data labeling services with the system crowdsourcing. Medical doctors or other experts registered as content experts can label huge amounts of big data with certain incentives. Data will be labeled by at least two experts to ensure expert agreement.

d. Data Analytics (Yavai) in Collaboration with Solusi247
Solusi247 is an Information and Data Technology company that focuses on massive scale data processing and high technology systems. Big Data Center collaborates with Solusi247 to develop data analytics called Yavai. Health Big Data that is stored and labeled can be further processed with flexible and affordable cloud computing technology. Here, artificial intelligence modeling can handle automatic data categorization or prediction.

e. Big Data Consultations
By using this service, users can consult with BDC IMERI-IDEALAB experts to get guidance starting from preparing, obtaining, cleaning, processing, and analyzing their health big data.

f. Repository and Publication
Repositories help in organizing data efficiently to store data in a structured and organized manner. This is particularly important in collaborative environments where multiple users may be working on the same data.

Activities

A. BDC TALK

b. Roadshow

Roadshow to Medical Departements IMERI and FKUI
Unlocking Doors to Medical Al Advancements

c. Workshop

Tools, Tips, and Skills Labeling Data for Medical Research
The Recipe of Artificial Intelligence in Medical Imaging
Learning A to the Z Cloud Computing Analytics

d. Seminar

Next BDC 2022 (Noteworthy Expert Talk)
Sinergi Pusat Biobank, Bioinformatika, dan Big Data pada Bidang Penelitian Kesehatan

e. BDC Matchmaking Day

Research and Innovation

Market Research & Validation: ANALISIS RISET PROMOSI DAN SEGMENTASI PASAR PUSAT MAHADATA KESEHATAN IMERI

Ongoing Projects

Labeling Stethosoul Project 2021 – 2022. (PI: Dr. dr. Khamelia Malik, Sp.KJ (K))
Labeling Project Pemanfaatan Stetoskop Digital dengan Machine Learning Serta Computer Aided Diagnosis Radiografi Dada untuk Peningkatan Diagnosis Tuberkulosis di DKI Jakarta 2023. (PI: dr. RR. Diah Handayani, Sp.PKR, Subsp.IP)
Labeling Stethosoul 2.0 Project 2023 (PI: Dr. dr. Khamelia Malik, Sp.KJ (K))
Labeling Project ICG Lymphography Classification 2023. (PI: dr. Bayu Brahma, Sp.B(K) Onk)
Pengembangan Machine Learning dan Metode Ekstraksi Fitur Sinyal Fisiologis (EEG, ECG, EMG dan sebagainya) untuk Kebutuhan Medis dan Non Medis 2023. (PI: Dr. Prima Dewi Purnamasari)
Pengembangan model machine learning ECG Non-Invasif untuk Deteksi FHR 2023. (PI: Tomy Abuzairi, ST, MT, M.Sc, Ph.D)
Prediksi Keberhasilan Ekstubasi dengan Machine Learning 2023. (PI: dr. Adhrie Sugiarto, SpAn-KIC)
Pengembangan Model Prediksi Diagnostik Post Intensive Care Syndrome Berbasis Machine Learning 2023. (PI: dr. Peggy)
Pengembangan ANN data activated endothelial cell markers sebagai penanda keparahan infeksi dengue 2023. (PI: Beti Ernawati Dewi, Ph.D)
Pengembangan Machine Learning untuk Deteksi Dini Catheter-Related Bloodstream Infections (CRBSI) menggunakan Bedside Ultrasound pada Pasien yang terpasang Catheter Double Lumen (CDL) dan Central Venous Catheter (CVC). 2023. (PI: Rejoel Mangasa Siagian)
Rancang Bangun Sistem Deteksi Retinopati Diabetik Berbasis Deep Learning 2023. (PI: Mohammad Ikhsan, S.T., M.T., Ph.D.)
Pengembangan model machine learning untuk memprediksi PPOK 2023. (PI: Rizka Yulvina, B.Sc)
Pengembangan Registry Untuk Kusta yg bisa diimplementasikan secara nasional 2023. (PI: Dr.dr. Yunia Irawati, Sp.M (K))
Pendeteksian Dini Kondisi Abnormal pada Janin dengan dari Interpretasi Sinyal Cardiotocography (CTG) menggunakan Deep Learning 2023. (PI: Dr. Basari, S.T., M.Eng.)
Pengembangan cervical cancer early detection 2023. (PI: Nidya Fakultas Teknik UI)
Perhitungan femoral neck-shaft angle (NSA) secara otomatis pada radiografi pelvis standar posisi anterior-posterior. 2023. (PI: Livia Fakultas Teknik UI)
Large Language Model in Healthcare 2023. (PI: Prof. Alhadi Bustamam, Ph.D)
Pengembangan Aplikasi Kecerdasan Buatan Berbasis Komputer Berdasarkan Data CT-Scan Dalam Mendiagnosis dan Memberikan Rekomendasi Tatalaksana Pada Pasien Fraktur Wajah 2023. (PI: dr. Vika Tania, SpBP-RE, Subsp. KF)