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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
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KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
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Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
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Dataset Name: FIFA23 OFFICIAL DATASET
Basic Description: From FIFA17 to FIFA23 statistics for each football player
📖 FULL DATASET DESCRIPTION:
The dataset contains +17k unique players and more than 60 columns, general information and all KPIs the famous videogame offers. As the esport scene keeps rising espacially on FIFA, I thought it can be useful for the community (kagglers and/or gamers)
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (14 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/bryanb/fifa-player-stats-database
📊 Additional information:
File count not found
Views: 107,000
Downloads: 66,500
@Machine_learn
Python Programming Hans-Petter Halvorsen
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@Machine_learn
با عرض سلام برای مقاله زیر نیاز به نفر ۳ داریم.
KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
....
Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
3 :15 milion
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@Machine_learn
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🛠️OpenAI just released new guide on how coding agents like GPT-5.1-Codex-Max plug into everyday engineering workflow
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🔹 Title: ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18271
• PDF: https://arxiv.org/pdf/2508.18271
• Project Page: https://objfiller3d.github.io/
• Github: https://github.com/objfiller3d/ObjFiller-3D
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Matplotlib: Visualization with Python
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Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers
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Advanced, Overlooked Python Typing
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📑 A comprehensive review of cluster methods for drug–drug interaction network
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دوستانی که می خوان تو حوزه ی LLM مقاله داشته باشن می تونن تو این مقاله شرکت کنند.
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https://arxiv.org/pdf/2511.22082
Weighted Ensemble Transformer for Identifying Psychiatric Stressors Related to Suicide X (formerly Twitter)
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Title: Recurrent Neural Networks
Basic deficiencies: NP-complet feature order
Abstract:
The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. ....
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@Raminmousa
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برای این مقاله امکان واگذاری کامل هم وجود داره...!
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🔹 Title: Autoregressive Universal Video Segmentation Model
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19242
• PDF: https://arxiv.org/pdf/2508.19242
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💻 ++101 Linux commands Open-source eBook
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Dataset Name: Malaria Bounding Boxes
Basic Description: P. vivax (malaria) infected human blood smears
📖 FULL DATASET DESCRIPTION:
Malaria is a disease caused by Plasmodium parasites that remains a major threat in global health, affecting 200 million people and causing 400,000 deaths a year. The main species of malaria that affect humans are Plasmodium falciparum and Plasmodium vivax.
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (5 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/kmader/malaria-bounding-boxes
📊 Additional information:
File count not found
Views: 54,400
Downloads: 4,657
@Machine_learn
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
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🔹 Title: Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18921
• PDF: https://arxiv.org/pdf/2508.18921
• Github: https://github.com/jmichankow/deep_learning_probability
@Machine_learn
🔹 Title: ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks
🔹 Publication Date: Published on Aug 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15804
• PDF: https://arxiv.org/pdf/2508.15804
@Machine_learn
دوستان برای این مقاله نیاز به نفرات ۴ و ۵ داریم
Title: Recurrent Neural Networks
Basic deficiencies: NP-complet feature order
Abstract:
The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. ....
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4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money
با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم.
KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
....
Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
2 :20 milion
3 :15 milion
@Raminmousa
@Machine_learn
@paper4money
Designing Machine Learning Systems book
@Machine_learn
با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال
Title: Recurrent Neural Networks
Basic deficiencies: NP-complet feature order
Abstract:
The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. ....
Price:
2: 500$
3:400$
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money
Python Programming for Economics and Finance
📚 Book
@Machine_learn
با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم.
KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
....
Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
2 :20 milion
3 :15 milion
@Raminmousa
@Machine_learn
@paper4money
"Competitive Programming in Python"
This 267-pages book from Cambridge University will teach you 128 Algorithms. Don't miss.
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@Machine_learn
📄 Deep learning methods for protein representation and function prediction: A comprehensive overview
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با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال
Title: Recurrent Neural Networks
Basic deficiencies: NP-complet feature order
Abstract:
The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. ....
Price:
2: 500$
3:400$
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money
📹 Unlock Discovery with AI-Powered Genomics
💥 From Oracle
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@Machine_learn