Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.
Machine Learning-driven Analysis of Gastrointestinal Symptoms in Post-COVID-19 Patients
College of Science, University of Al-Qadisiyah（卡迪西亚大学理学院）；Liverpool John Moors University（利物浦约翰摩尔大学）；College of Medicine, Kufa University（库法大学医学院）；Imperial College（帝国理工学院）；Al-Sadder Teaching Hospital（萨德尔教学医院）
Tracking agitation in people living with dementia in a care environment
KITE, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE）；McMaster University（麦克马斯特大学）；University of Toronto（多伦多大学）；Ryerson University（瑞尔森大学）
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用技术大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Regensburg（雷根斯堡大学）；Leipzig University（莱比锡大学）
Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals
Stanford University（斯坦福大学）；University of California, San Francisco（加州大学旧金山分校）；Emory University（埃默里大学）；Sungkyunkwan University（成均馆大学）；Jeju National University Hospital（济州国立大学医院）
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）
本文构建像素级标注的大规模直肠癌 CT 数据集 CARE，并提出融合提示、卷积与跳跃连接的 U-SAM 分割模型，在 CARE 和 WORD 上均取得优于现有方法的表现。
Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19
Bangabandhu Sheikh Mujibur Rahman Science and Technology University（班加班杜谢赫穆吉布·拉赫曼科技大学）；Khulna University（库尔纳大学）；Jatiya Kabi Kazi Nazrul Islam University（贾蒂亚·卡比·卡齐·纳兹鲁尔·伊斯兰大学）；University of Canberra（堪培拉大学）；Nagasaki University（长崎大学）；The Garvan Institute of Medical Research（加尔文医学研究所）；Royal North Shore Hospital SERT Institute（皇家北岸医院SERT研究所）；University of New South Wales（新南威尔士大学）；World Health Organization (WHO) Centre on eHealth（世界卫生组织电子健康中心）
Atrial Fibrillation: A Medical and Technological Review
Jadavpur University（贾达普大学）
房颤（AF）是美国最常见的导致住院治疗的心律失常类型（希腊语a-，损失+心律失常，心律失常=心律失常）。虽然有时AF是无症状的，但它除了降低健康相关生活质量（HRQOL）外，还增加了患者中风和心力衰竭的风险。AF相关护理每年花费医疗保健系统$6.0 to $ 260亿美元。房颤的早期发现和临床关注可以帮助改善患者的症状和HRQOL，并降低护理成本。然而，AF检测的流行范例依赖于在单个时间点记录的心电图（ECG），并且没有阐明症状与心律或AF的关系。在最近十年中，由于健康监测器的民主化和高性能计算机的出现，机器学习算法已被证明在从患者的ECG识别AF方面是有效的。本文概述了房颤的症状、诊断以及该领域研究的未来前景。
One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；Radiopharmacy Center, Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院放射药学中心）
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
Faculty of Computers & Artificial Intelligence, Cairo University（开罗大学计算机与人工智能学院）；Scientific Academy for Service Technology e.V. (ServTech)（服务技术科学学会）；University of New South Wales（新南威尔士大学）；University of Macquarie（麦考瑞大学）；Egypt University of Informatics（埃及信息学大学）
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；University of São Paulo（圣保罗大学）
A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data
Stanford University（斯坦福大学）；Stanford University School of Medicine（斯坦福大学医学院）
AmarDoctor: An AI-Driven, Multilingual, Voice-Interactive Digital Health Application for Primary Care Triage and Patient Management to Bridge the Digital Health Divide for Bengali Speakers
MedAi Bangladesh Limited（MedAi孟加拉有限公司）；Central Police Hospital, Dhaka, Bangladesh（达卡警察医院）；University of Birmingham, UK（英国伯明翰大学）
