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"This book constitutes the refereed proceedings of the International Conference on Brain Informatics, BI 2012, held in Macau, China, in December 2012. The 34 revised full papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on computer science, information technology, artificial intelligence, web intelligence, cognitive science, neuroscience, medical science, life science, economics, data mining, data and knowledge engineering, intelligent agent technology, human computer interaction, complex systems, and system science."

"This state-of-the-art survey offers a collection of papers from the workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning."

"This textbook offers an insightful study of the intelligent Internet-driven revolutionary and fundamental forces at work in society. Readers will have access to tools and techniques to mentor and monitor these forces rather than be driven by changes in Internet technology and flow of money. These submerged social and human forces form a powerful synergistic foursome web of (a) processor technology, (b) evolving wireless networks of the next generation, (c) the intelligent Internet, and (d) the motivation that drives individuals and corporations. In unison, the technological forces can tear human lives apart for the passive or provide a cohesive set of opportunities for the knowledgeable to lead and reap the rewards in the evolved knowledge society.The book also provides in-depth coverage of the functions embedded in modern processors and intelligent communication networks. It focuses on the convergence of the design of modern processor technologies with the switching and routing methodologies of global intelligent networks. Most of the concepts that are generic to the design of terra-flop parallel processors and the terra-bit fiber-optic networks are presented. This book also highlights recent developments in computer and processor technologies into the microscopic and macroscopic medical functions in hospitals and medical centers."

"Stroke merupakan penyebab utama kematian dan kecacatan di Indonesia. Mengingat sempitnya jendela waktu pengobatan stroke iskemik hiperakut dan potensi komplikasi yang terkait dengan intervensi trombolisis, prognostikasi yang akurat esensial dalam memastikan terapi yang cepat dan tepat. Penelitian ini memanfaatkan pembelajaran mesin, khususnya Random Forest (RF), bertujuan untuk mengembangkan model yang mampu memprediksi hasil klinis (Δ NIHSS) pasien stroke iskemik hiperakut setelah trombolisis, berdasarkan CT scan otak, data klinis, dan nilai laboratorium. Klasifikasi Δ NIHSS menggunakan tiga skenario berbeda —CT, CT + Data klinis, dan CT + Data klinis + Data lab— dan dikategorikan menjadi 2 dan 3 kelas yang akan digunakan dalam pemantauan model prediksi mana yang memberikan performa paling optimal. Pengumpulan data studi kohort ini diperoleh saat kedatangan awal pasien, terdiri dari data klinis, laboratorium, dan data CT otak non-kontras dari rekam medis dan Picture Archiving Communication System (PACS) Rumah Sakit Cipto Mangunkusumo Jakarta dengan periode 10 tahun sejak November 2014 hingga Februari 2023 dan total 145 pasien. Arsitektur dari Bacchi et al.1 yakni convolutional neural network (CNN) dan model pembelajaran mesin konvensional lainnya juga dianalisis sebagai pendekatan alternatif. Hasil penelitian menunjukkan bahwa algoritma RF (2 kelas) menggunakan data validasi dan skenario CT + Data klinis + Data lab menampilkan akurasi tertinggi (75%) dan unggul dalam sensitivitas dan spesifisitas (0,61 dan 0,59). Performa metrik juga menunjukkan tren peningkatan dari setiap skenario. Model ini diharapkan dapat meningkatkan efisiensi penatalaksanaan stroke iskemik hiperakut dengan memberikan informasi tambahan kepada klinisi dalam pengambilan keputusan terkait intervensi trombolisis.

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Stroke is the leading cause of both mortality and disability in Indonesia. Given the narrow time frame for treating acute ischemic stroke and the potential complications associated with thrombolysis intervention, accurate prognostication is essential to ensure a prompt and appropriate treatment. The National Institutes of Health Stroke Scale (NIHSS) can be utilized to identify individuals who may benefit from reperfusion therapy. The data for this cohort study acquired during the initial presentation, comprising clinical, laboratory, and non-contrast brain CT data from the medical records and Picture Archiving Communication System (PACS) of Cipto Mangunkusumo Hospital Jakarta. The study included 145 patients who experienced acute ischemic stroke and received thrombolysis treatment from November 2014 to February 2023. Currently, there is no clinical outcome prediction model for hyperacute ischemic stroke using data from Indonesia. By utilizing machine learning, specifically Random Forest, the author aims to develop a model capable of predicting the clinical outcome (Δ NIHSS) of hyperacute ischemic stroke patients following thrombolysis, based on brain CT scans, clinical data, and laboratory values. The classification of Δ NIHSS used three distinctive scenarios —CT, CT + Clinic, and CT + Clinic + Lab— and is categorized by 2 and 3 classes will be used in monitoring which prediction model gives optimal performance. Architecture derived from the research conducted by Bacchi et al.1 employed a convolutional neural network (CNN) and other conventional machine learning models were also analyzed as alternative approach. Result revealed that RF algorithm (2 classes) using data validation and CT + Clinic + Lab scenario displays the highest accuracy (75%) and excels in sensitivity and specificity (0,61 and 0,59). The performance metrics show continuous improvement, indicating that this model can enhance hyperacute ischemic stroke management by providing clinicians with additional decision-making support for thrombolysis intervention."

"The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging."