安全人工智能中心（安全）在生物医学信息学院的AT学院重点是协调计算机科学，应用数学，生物统计学，化学和药理学的方法，以促进和加速生物医学数据研究和发现。beplay苹果手机能用吗由...领着Xiaoqian Jiang, PhD, the SAFE consists of faculty, staff, programmers, and graduate students. The strength of combining secure and privacy-preserving solutions with advanced machine learning models to meet the emerging needs of healthcare make SAFE unique in exploring massive and sensitive data across different modality and sources. Here is a list of sample projects that are currently conducted by us.
Miran Kim (Assistant Professor)
- Efficient multi-key homomorphic encryption with packed ciphertexts
- 我们将实际的多键的变种momorphic encryption scheme with packed ciphertexts, which will be used a wide range of applications in secure computation between multiple data owners. We will apply this technology to secure neural networks, where input data and pre-trained model are encrypted under different keys.
- 保护分布式数据的安全且私密的机器学习
- 我们将通过协调同构加密和差异隐私技术来开发安全和隐私的机器学习框架。这种安全的技术将保护对分布式源的敏感数据的计算以及数据分析的结果。
- Development of secure genotype-phenotype association models with efficient correction for population stratification
- We will propose a novel framework to develop secure genotype-phenotype association models with efficient correction for population stratification based on the application of homomorphic encryption.
- 安全外包基因组数据分析的实际应用
- 该项目旨在开发安全的技术，用于在临床应用中使用患者的基因组数据，同时确保在计算过程中患者信息安全和隐私。我们将开发同态加密的基因组查询算法，以支持对人类基因组数据的安全存储和分析。
- Secure outsourced genotype imputation using homomorphic encryption
- This project will provide a secure framework of genotype imputation in genome-wide association study (GWAS) based on homomorphic encryption. This model can securely estimate genotypes of missing variants on encrypted genotypic data.
- On-chip private computation of deep neural networks for face recognition
- We will develop on-chip computations with homomorphic encryption for face recognition. Once we can train homomorphic-encryption friendly neural network models for detection, we will implement a secure evaluation phase on encrypted data on trained models.
Yejin Kim（助理教授）
计算表型：
Method developments:
- Federated TF：基于TF的表型方法需要大量不同的样本以避免人口偏见。一个开放的挑战是如何在多家医院共同得出表型，在该医院中，由于机构隐私政策，无法直接患者级数据共享。我们开发了一种新颖的解决方案，可以使联合TF用于计算表型，而无需共享患者级数据。我们的方法可以帮助从EHR中获得有用的表型来克服由于隐私问题而克服政策障碍。
- Supervised TF：我们开发了一种新型的TF方法来生成歧视表型。表型应该拥有的重要特征之一是对某些感兴趣的临床结果有歧视，例如死亡率，再入院，成本等。为了区分高风险群体（高死亡率），我们在分解过程中纳入了逻辑回归的估计概率。
- 相似性感知的TF: We developed a novel TF method for generating distinct phenotype. Phenotypes should be distinct from each other, because otherwise clinicians cannot interpret and use the phenotypes easily
- Multi-modal TF：我们开发了多模式的TF方法来结合其他模态数据源（例如将人口统计数据纳入诊断和药物病史）。
应用程序：
- 重症监护室（ICU）表型: Using a large publicly available dataset MIMIC-III from critical care units, we derived representative ICU phenotypes: sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comport care), intraabdominal conditions, and alcohol abuse/withdrawal.
- 组合药物重新定位：使用Cerner Health Facts数据集（包含来自600名Cerner客户医院的约50m独特患者），我们得出了有效预防阿尔茨海默氏病的药物组合。
最佳治疗和诊断的强化学习
- A personalized medication regimen for symptom management of Parkinson’s disease (PD)：我们得出对个别PD患者个性化的药物治疗方案，以减少运动功能障碍。我们的模型提出了一种迭代的药物选择，可最大程度地减少预期的运动功能下降。
- A personalized test procedure for differential diagnosis: We proposed a novel decision process framework that detect the target disease by iteratively applying tests and reducing the ambiguity of disease diagnoses. It is based on partially observed MDP in which multiple tests can be performed simultaneously in partially observed environments. We developed solving schemes of the proposed decision process using integer programming for incorporating practical constraints. We applied our proposed model to derive a dynamic immunohistochemistry (IHC) staining test procedures that can detect lymphoid neoplasm with high accuracy while minimizing testing burden (i.e., time and cost).
预测或实时检测结果
- Real-time detection of end of EEG suppression: Using the real-time EEG signal data, we detected end of EEG suppression after seizure, which can automatically monitor patient’s status with minimal human’s supervision.
Shayan Shams（助理教授）
Privacy-protecting video and Image analysis:
We integrate big data and deep learning techniques to develop Artificial intelligence (AI) models on edge devices for live video processing. In this line of research, we use air-gapped embedding devices to constantly monitor senior people and cognitively impaired patients. These algorithms are capable of constant monitoring of the patients' status without violating their privacy and with minimal human supervision.
Breast cancer screening and diagnosis:
We are developing AI-driven and clinically useful multi-modality pipeline for breast cancer screening and diagnosis by incorporating imaging, mammograms and ultrasound images, and non-imaging information such as EMR and blood biomarkers. Our AI-driven pipeline imitates the clinical screening-to-diagnosis pathway to increase the specificity and sensitivity of breast cancer screening and diagnosis. Additionally, our model will be optimized for embedded edge devices, so it can be employed in mobile mammography units to extend the coverage to underserved communities.
Blueprint for tissue engineering:
主要重点是开发能够为人体组织打印生成蓝图的AI模型。我们的端到端多模式深度学习算法使用来自多词，生物医学成像的多方面生物学数据，并整合来自每种模式的信息，以应对软组织再生中的挑战。
牙周疾病筛查和诊断：
We are developing deep learning models to screen dental X-rays for a variety of periodontal diseases. Our envision algorithm is capable of detecting the region of interests and classifying them to periodontal defects. This algorithm provides per tooth report and can improve periodontal diagnosis and eliminate the use of periodontal probes. Additionally, the algorithm can draw attention to certain image features and/or identify important overlooked image features to compensate for the variation in the skill and experience.
通过磁共振成像（MRI）测量胶质母细胞瘤肿瘤体积：
我们正在开发并将实施一项全面的AI技术，以实现GBM肿瘤的体积测量，从而区分非增强肿瘤浸润与水肿与辐射变化的气体异常变化以及对假孕期的真实进展的鉴定。
EEG抑制的实时检测：
我们正在开发多模深alg学习orithms to use the real-time patient’s EEG signals and video to detect the end of EEG suppression after a seizure. This algorithm can lead to the development of a framework to automatically monitor patients' status with minimal human supervision.
社交媒体分析：
As an innovative social sensing technology, social media data can provide real-time georeferenced information on human interests, responses, perceptions, and behavior in various situations. In this research, we aim to develop algorithms and frameworks to derive practical information from social media such as Tweeter. This practical information can help us to identify and investigate risky behaviors that can have correlations to communicable diseases infection or bad habits such as opioid addiction.
Third generation sequence assembly and alignment:
由于低成本的便携式3G音序器使现场测序非常实惠，因此我们将开发嵌入式设备友好的组件和对齐程序，以提供现场边缘分析。这些算法可以使个性化医学成为可行且负担得起的选择。
Luyao Chen（科学程序员）
Luyao Chen是一位经验丰富的程序员，拥有10年以上的经验（包括在Oracle的8年）。他是我们中心的骨干之一，并支持各种协作项目，包括但不限于：
- 通过优化高级绿色分布式数据库和图形数据库来加快大数据分析和查询
- 在我们的大多数研究中，由队列选择使用并维护倾向得分匹配的服务
- Drug repurposing: Use Cerner data to find out drugs or drug combinations for cancer treatment (brain cancer, breast cancer, pancreatic cancer etc.)
- SEPIS2 Cerner/SBMI竞赛：为国家竞赛的主要技术支持
- Developing novel harmonizing algorithms for national birth data of 48 years
- Data preparation for cross-sites diagnosis code embedding
- Various other collaborative projects within and outside SBMI
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