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A self-supervised method for treatment recommendation in sepsis Research Articles

Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 926-939 doi: 10.1631/FITEE.2000127

Abstract: treatment is a highly challenging effort to reduce mortality in hospital intensive care units since the treatment response may vary for each patient. Tailored s are desired to assist doctors in making decisions efficiently and accurately. In this work, we apply a self-supervised method based on (RL) for on individuals. An uncertainty evaluation method is proposed to separate patient samples into two domains according to their responses to treatments and the state value of the chosen policy. Examples of two domains are then reconstructed with an auxiliary transfer learning task. A distillation method of privilege learning is tied to a variational auto-encoder framework for the transfer learning task between the low- and high-quality domains. Combined with the self-supervised way for better state and action representations, we propose a deep RL method called high-risk uncertainty (HRU) control to provide flexibility on the trade-off between the effectiveness and accuracy of ambiguous samples and to reduce the expected mortality. Experiments on the large-scale publicly available real-world dataset MIMIC-III demonstrate that our model reduces the estimated mortality rate by up to 2.3% in total, and that the estimated mortality rate in the majority of cases is reduced to 9.5%.

Keywords： 治疗推荐；脓毒症；自监督学习；强化学习；电子病历    

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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical

Longbing Cao

Engineering 2016, Volume 2, Issue 2,   Pages 212-224 doi: 10.1016/J.ENG.2016.02.013

Abstract:

While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

Keywords： Independent and identically distributed (IID)     Non-IID     Heterogeneity     Coupling relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1273-1286 doi: 10.1631/FITEE.2200304

Abstract: Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations, a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in encoding. In the process of encoding information, an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition, we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.

Keywords： Point-of-interest recommendation     Spatiotemporal effects     Long short-term memory (LSTM)     Attention mechanism    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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Cohort-based personalized query auto-completion Regular Papers-Research Articles

Dan-yang JIANG, Hong-hui CHEN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1246-1258 doi: 10.1631/FITEE.1800010

Abstract: Query auto-completion (QAC) facilitates query formulation by predicting completions for given query prefix inputs. Most web search engines use behavioral signals to customize query completion lists for users. To be effective, such personalized QAC models rely on the access to sufficient context about each user’s interest and intentions. Hence, they often suffer from data sparseness problems. For this reason, we propose the construction and application of cohorts to address context sparsity and to enhance QAC personalization. We build an individual’s interest profile by learning his/her topic preferences through topic models and then aggregate users who share similar profiles. As conventional topic models are unable to automatically learn cohorts, we propose two cohort topic models that handle topic modeling and cohort discovery in the same framework. We present four cohortbased personalized QAC models that employ four different cohort discovery strategies. Our proposals use cohorts’ contextual information together with query frequency to rank completions. We perform extensive experiments on the publicly available AOL query log and compare the ranking effectiveness with that of models that discard cohort contexts. Experimental results suggest that our cohort-based personalized QAC models can solve the sparseness problem and yield significant relevance improvement over competitive baselines.

Keywords： Query auto-completion     Cohort-based retrieval     Topic models    

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Toward Privacy-Preserving Personalized Recommendation Services Review

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Engineering 2018, Volume 4, Issue 1,   Pages 21-28 doi: 10.1016/j.eng.2018.02.005

Abstract:

Recommendation systems are crucially important for the delivery of personalized services to users. With personalized recommendation services, users can enjoy a variety of targeted recommendations such as movies, books, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users’ personal data for processing and analytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions.

Keywords： Privacy protection     Personalized recommendation services     Targeted delivery     Collaborative filtering     Machine learning    

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Diagnosis and treatment for one case with advanced abdominal pregnancy

Shen Ying,Dai Shuyan

Strategic Study of CAE 2015, Volume 17, Issue 6,   Pages 61-64

Abstract:

Objective：To explore the diagnosis and treatment of advanced abdominal pregnancy. Methods：A retrospective analysis and summary were performed about 1 case of advanced abdominal pregnancy diagnosis and treatment in our hospital. Results：The case was diagnosis by MRI，and surgical treatment was successful. Conclusion：Abdominal pregnancy is not a typical clinical symptom and early diagnosis is difficult，MRI is an effective diagnosis. Surgery is the most important treatment.

Keywords： abdominal pregnancy     diagnosis     treatment     MRI    

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EncyCatalogRec: catalog recommendation for encyclopedia article completion Article

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 436-447 doi: 10.1631/FITEE.1800363

Abstract: Online encyclopedias such as Wikipedia provide a large and growing number of articles on many topics. However, the content of many articles is still far from complete. In this paper, we propose EncyCatalogRec, a system to help generate a more comprehensive article by recommending catalogs. First, we represent articles and catalog items as embedding vectors, and obtain similar articles via the locality sensitive hashing technology, where the items of these articles are considered as the candidate items. Then a relation graph is built from the articles and the candidate items. This is further transformed into a product graph. So, the recommendation problem is changed to a transductive learning problem in the product graph. Finally, the recommended items are sorted by the learning-to-rank technology. Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation in both warm- and cold-start scenarios. We have validated our approach by a case study.

Keywords： Catalog recommendation     Encyclopedia article completion     Product graph     Transductive learning    

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Medical Cyclotron Used for Diagnostic or Therapy

Fan Mingwu

Strategic Study of CAE 2000, Volume 2, Issue 12,   Pages 9-15

Abstract:

Cyclotron operated within a hospital to deliver radiation to produce short life radioactive isotopes for diagnostic purpose is used in many hospitals. But cyclotron used for proton therapy is just at beginning. Proton beam therapy is a technologically advanced approach since it achieves precise radiation dose distribution. As the depth of penetration is a function of its energy, the point of energy release of the proton can be very precisely determined. The path of the proton beam is linear and causes very little secondary lateral scatter. Healthy tissues upstream from the tumor receive only a small dose and those located around the tumor and behind receive virtually none. Thus proton beam therapy is the most effective method. On the other hand, the cyclotron must be reliable, and the beam must be stable to ensure patient´s safety since the proton beam is used directly. Efficiency of beam delivery to the treatment rooms, raster scanning techniques to ensure the correct control of beam energy, intensity, and position stability, rapid and precise flexibility in changing beam parameter are also essential for satisfying clinical needs. The design efforts for developing a hsopital-based medical therapy cyclotron including the clinical beam delivery systems and integrated control system are really complex, and are less well developed than patients real need. A conception scheme for the purpose is proposed.

Keywords： cyclotron     positron emission tomography     short life radioisotope     proton beam therapy    

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Fast code recommendation via approximate sub-tree matching Research Article

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1205-1216 doi: 10.1631/FITEE.2100379

Abstract: Software developers often write code that has similar functionality to existing code segments. A tool that helps developers reuse these code fragments can significantly improve their efficiency. Several methods have been proposed in recent years. Some use sequence matching algorithms to find the related recommendations. Most of these methods are time-consuming and can leverage only low-level textual information from code. Others extract features from code and obtain similarity using numerical feature vectors. However, the similarity of feature vectors is often not equivalent to the original code’s similarity. Structural information is lost during the process of transforming abstract syntax trees into vectors. We propose an approximate sub-tree matching based method to solve this problem. Unlike existing tree-based approaches that match feature vectors, it retains the tree structure of the query code in the matching process to find code fragments that best match the current query. It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list. In this way, the structural information can be used for tasks that have high time requirements. We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method. The results show that our method outperforms two compared methods, SENSORY and Aroma, in terms of the recall value on all the datasets, and can be applied to large datasets.

Keywords： Code reuse     Code recommendation     Tree similarity     Structure information    

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RepoLike: amulti-feature-based personalized recommendation approach for open-source repositories None

Cheng YANG, Qiang FAN, Tao WANG, Gang YIN, Xun-hui ZHANG, Yue YU, Hua-min WANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 222-237 doi: 10.1631/FITEE.1700196

Abstract:

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness, a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called “RepoLike,” to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

Keywords： Social coding     Open-source software     Personal recommendation     GitHub    

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A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation Research Article

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1416-1429 doi: 10.1631/FITEE.2200662

Abstract: (TCM) is an interesting research topic in China’s thousands of years of history. With the recent advances in artificial intelligence technology, some researchers have started to focus on learning the TCM prescriptions in a data-driven manner. This involves appropriately recommending a set of herbs based on patients’ symptoms. Most existing models disregard TCM domain knowledge, for example, the interactions between symptoms and herbs and the TCM-informed observations (i.e., TCM formulation of prescriptions). In this paper, we propose a knowledge-guided and TCM-informed approach for . The knowledge used includes path interactions and co-occurrence relationships among symptoms and herbs from a generated from TCM literature and prescriptions. The aforementioned knowledge is used to obtain the discriminative feature vectors of symptoms and herbs via a . To increase the ability of herb prediction for the given symptoms, we introduce TCM-informed observations in the prediction layer. We apply our proposed model on a TCM prescription dataset, demonstrating significant improvements over state-of-the-art methods.

Keywords： Traditional Chinese medicine     Herb recommendation     Knowledge graph     Graph attention network    

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Treatment of retaining reproductive function?in cesarean scar pregnancy：A clinical analysis of 46 cases

Guan Rui,Liu Yuhuan,Xu Mingjuan,Zhang Junjie,Hui Ning,Cui Ying

Strategic Study of CAE 2015, Volume 17, Issue 6,   Pages 45-49

Abstract:

To investigate the treatment methods of retaining reproductive function in cesarean scar pregnancy. Clinical datus of 46 casesSin our hospital during 2000―2014Swere analyzed retrospectively.SThere were 45 cases of first-trimesterSpregnancy andS1 case of second-trimester pregnancy. 13 casesS(28.9 %)Sof first-trimesteSpregnancy were misdiagnosed and had received artificial abortion before hospitalization. According to CSP classificationS,S19Scases were type I,S13 cases were Stype II,Sall cases wereSsuccessful inSretaining the uterus. 63.1 % of type IScases receivedSuterine curettage under ultrasoundSmonitoring,S21%Sof type I casesSreceivedSuterineScurettageSafter uterine artery embolizationS(UAE)S. 84.6 %Sof type IIScases received surgery in which lesion were resected and uterine scar were repaired. The csp patient of second-trimester pregnancy suffered from thrombosis of lower extremity and rebleeding 30 days after UAE, she received the surgery of resecting lesion. Conclusion: STheSkeySforStheSconservativeStreatmentSSofS CSPSSis early diagnosis and early treatment. TreatmentSshouldSbeSindividualizedSaccording to CSP clinicSclassification ,β-HCG and so on.Furthermore, there are still someSrisk of UAE.WeSshould be careful to select UAESfor patients.

Keywords： Scesarean scar pregnancy     Sindividualized treatment     Spreservation of fertility     uterine artery embolization    

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Experimental Studies on Recombinant Hepatitis B Immunogenic Complex Therapeutic Vaccine (YIC)

Wenyu mei,He Lifang,Qu Di,Ma Zhangmei,Yao Xin

Strategic Study of CAE 1999, Volume 1, Issue 1,   Pages 38-42

Abstract:

A therapeutic vaccine composed of HBsAg complexed to anti-HBs (IC) has been developed for viral hepatitis B. Enhanced immune response was induced in hosts immunized with this complex, and the mechanisms of this vaccine was studied in mice. It was shown that the Fc fragment of anti-HBs in the complex was critical for induction of potent immune responses. The Fc fragment of the anti-HBs in the complex could attach to the Fc receptors on the antigen presenting cells (APC), increasing the uptake of HBsAg into these cells. After being ingested, the antigen complexed to antibody could be more effectively processed and presented to T cells. After incubation with macrophages previously treated with IC, T cells showed higher proliferation rate, and higher level of interferon-7 mRNA was detected. Enhanced immune response in host has also been shown by comparing anti-HBs titer in mice immunized with antigen-antibody complex versus the anti-HBs titer in mice immunized only with HBsAg. More than tenfold increase in anti-HBs was observed in the latter group. In addi-tion, this complex was used to immunize an HBsAg low-responder mouse strain (B10.S). Compared to the normal responsive counterpart mouse strain (BIO), immunization with HBsAg induced low titer of antibody, whereas, immunization using HBsAg-anti-HBs complex, BIO.S mice responded by producing similar level of anti-HBs as that induced in the BIO mice. When the complex was used to immunize HBsAg positive transgenic mice (TgE)，after four injections, in the female mice, 72% cleared HBsAg and developed anti-HBs (mean titer 1 • 1070 by EIA); while in the male mice, 54% cleared HBsAg and developed anti-HBs (mean titer 1:455 by EIA). Though some of non-immunized mice lost their HBsAg spontaneously during the experiment, none developed anti-HBs. Data showed that this immunogenic complex has promising potential to be used for the treatment of hepatitis B patients. For human use, a therapeutic vaccine composed of yeast-derived recombinant HBsAg complexed to human high-titer anti-HBs immunoglobulin (HBIG) has been developed. Standard procedure for manufacturing this complex, as well as in vitro assay for monitoring its effect were also established. This complex will be further optimized for mass production and application for clinical trial will be submitted.

Keywords： hepatitis B     therapeutic vaccine     antigen-antibody complex     immune responses    

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Ovulation detection by transvaginal ultrasound and ovulation induction for two types of ovulation disorder

Liu Li,Wu Chunlin,Hu Yajun

Strategic Study of CAE 2015, Volume 17, Issue 6,   Pages 31-35

Abstract:

Objective：To find out the general clinical characteristics from patients with normal ovulation，small follicular ovulation and luteinized unruptured follicle and observe the result of ovulation detection repeatedly and the effect of ovarian stimulatioin for later two types. Methods：Retrospective analyzing the difference of the patients’age，basal serum gonadal steroid hormones and follicle size before ovulation in the groups of normal ovulation（n=258）， small follicular ovulation（n=65）and luteinized unruptured follicle（n=27）. Then we observed the result of ovulation detection repeatedly by transvaginal ultrasound in the latter two groups. Finally，the ovulation induction with clomiphene citrate was carried out in the 58 cases of small follicles ovulation and 25 cases of luteinizing unruptured follicle which obtained the consistent results during the twice ovulation detection，we observed the rates of normal ovulation and clinical pregnancy in the two types of ovulatory dysfunctions. Results：Average age and basal serum Gonadal steroid hormones levelsdid not have significant difference（P<0.01）in thegroups of normal ovulation，small follicular ovulation and luteinized unruptured follicle，average diameter of follicles before ovulation in the three groups had significant difference（F=117.9，P< 0.01）. Incidence rates of small follicular ovulation and luteinized unruptured follicle were 18.5 % and 7.71 % respectively. Both were detected repeatedly，repetition rates were 89.23 % and 92.59 % respectively. After combined induced ovulation by clomiphene withluteal support， normal ovulation rates were 70.69 % and 80 % in the groups of small follicular ovulation and unruptured follicle ovulation luteinized respectively，clinical pregnancy rateswere 29.31 % and 48 % respectively. Conclusions：Small follicular ovulation and luteinized unruptured follicle are two special types of ovulatory dysfunctions，basal serum gonadal steroid hormones levels cannot predict ovulation，and transvaginal ultrasound is the most direct method to diagnosisthe small follicular ovulation and luteinized unruptured follicle. Without treatment，the repetition rate is high in thetwo types of ovulatory dysfunctions. Combined induced ovulation withluteal support is effective in the patients of the small follicular ovulation and luteinized unruptured follicle.

Keywords： transvaginalultrasound     ovulation detection     small follicular ovulation     luteinized unruptured follicle     ovulation induction    

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Title Author Date Type Operation

A self-supervised method for treatment recommendation in sepsis

Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn

Journal Article

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao

Journal Article

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article

Cohort-based personalized query auto-completion

Dan-yang JIANG, Hong-hui CHEN

Journal Article

Toward Privacy-Preserving Personalized Recommendation Services

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Journal Article

Diagnosis and treatment for one case with advanced abdominal pregnancy

Shen Ying,Dai Shuyan

Journal Article

EncyCatalogRec: catalog recommendation for encyclopedia article completion

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Journal Article

Medical Cyclotron Used for Diagnostic or Therapy

Fan Mingwu

Journal Article

Fast code recommendation via approximate sub-tree matching

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Journal Article

RepoLike: amulti-feature-based personalized recommendation approach for open-source repositories

Cheng YANG, Qiang FAN, Tao WANG, Gang YIN, Xun-hui ZHANG, Yue YU, Hua-min WANG

Journal Article

A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Journal Article

Treatment of retaining reproductive function?in cesarean scar pregnancy：A clinical analysis of 46 cases

Guan Rui,Liu Yuhuan,Xu Mingjuan,Zhang Junjie,Hui Ning,Cui Ying

Journal Article

Experimental Studies on Recombinant Hepatitis B Immunogenic Complex Therapeutic Vaccine (YIC)

Wenyu mei,He Lifang,Qu Di,Ma Zhangmei,Yao Xin

Journal Article

Ovulation detection by transvaginal ultrasound and ovulation induction for two types of ovulation disorder

Liu Li,Wu Chunlin,Hu Yajun

Journal Article