LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 2819

Search options

  1. Book: The Science and Clinical Practice of Attachment Theory: A Guide from Infancy to Adulthood

    Allen, Brian

    2023  

    Author's details Brian Allen
    Language English
    Size 307 p.
    Publisher American Psychological Association (APA)
    Document type Book
    Note PDA Manuell_20
    Format 152 x 226 x 18
    ISBN 9781433837616 ; 1433837617
    Database PDA

    Kategorien

    Order for free: Patron-Driven-Acquisition (PDA)

    This title is not yet in the ZB MED collection, but may be ordered for free via the ZB MED acquisition service PDA. The ordered item will be available in approximately one week.

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article: The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.

    Allen, Ben

    Journal of personalized medicine

    2024  Volume 14, Issue 3

    Abstract: This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with ... ...

    Abstract This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery.
    Language English
    Publishing date 2024-03-01
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2662248-8
    ISSN 2075-4426
    ISSN 2075-4426
    DOI 10.3390/jpm14030277
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article: Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence.

    Allen, Ben

    Biomedicines

    2023  Volume 11, Issue 3

    Abstract: Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a ...

    Abstract Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support. Yet, how machine learning models make decisions is often opaque. The spirit of explainable artificial intelligence is to use machine learning models that produce interpretable solutions. Here, we use topic modeling to synthesize recent literature on explainable artificial intelligence approaches to extracting domain knowledge from machine learning models relevant to deep brain stimulation. The results show that patient classification (i.e., diagnostic models, precision medicine) is the most common problem in deep brain stimulation studies that employ explainable artificial intelligence. Other topics concern attempts to optimize stimulation strategies and the importance of explainable methods. Overall, this review supports the potential for artificial intelligence to revolutionize deep brain stimulation by personalizing stimulation protocols and adapting stimulation in real time.
    Language English
    Publishing date 2023-03-03
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines11030771
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article ; Online: Flipping the intuition for games on dynamic networks.

    Allen, Benjamin

    Nature computational science

    2023  Volume 3, Issue 9, Page(s) 737–738

    MeSH term(s) Intuition ; Decision Making ; Game Theory
    Language English
    Publishing date 2023-10-19
    Publishing country United States
    Document type Journal Article
    ISSN 2662-8457
    ISSN (online) 2662-8457
    DOI 10.1038/s43588-023-00513-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Symmetry in models of natural selection.

    Allen, Benjamin

    Journal of the Royal Society, Interface

    2023  Volume 20, Issue 208, Page(s) 20230306

    Abstract: Symmetry arguments are frequently used-often implicitly-in mathematical modelling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal ... ...

    Abstract Symmetry arguments are frequently used-often implicitly-in mathematical modelling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain.
    MeSH term(s) Models, Genetic ; Selection, Genetic ; Markov Chains ; Probability ; Mutation
    Language English
    Publishing date 2023-11-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2156283-0
    ISSN 1742-5662 ; 1742-5689
    ISSN (online) 1742-5662
    ISSN 1742-5689
    DOI 10.1098/rsif.2023.0306
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article ; Online: An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

    Allen, Ben

    PloS one

    2023  Volume 18, Issue 10, Page(s) e0292341

    Abstract: Background: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable ... ...

    Abstract Background: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box.
    Objective: The goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence.
    Methods: This study shows the application of explainable artificial intelligence methods to machine learning models of cross-sectional obesity prevalence data collected from 3,142 counties in the United States. County-level features from 7 broad categories: health outcomes, health behaviors, clinical care, social and economic factors, physical environment, demographics, and severe housing conditions. Explainable methods applied to random forest prediction models include feature importance, accumulated local effects, global surrogate decision tree, and local interpretable model-agnostic explanations.
    Results: The results show that machine learning models explained 79% of the variance in obesity prevalence, with physical inactivity, diabetes, and smoking prevalence being the most important factors in predicting obesity prevalence.
    Conclusions: Interpretable machine learning models of health behaviors and outcomes provide substantial insight into obesity prevalence variation across counties in the United States.
    MeSH term(s) United States/epidemiology ; Humans ; Artificial Intelligence ; Prevalence ; Cross-Sectional Studies ; Obesity/epidemiology ; Machine Learning
    Language English
    Publishing date 2023-10-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0292341
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: Rescued by a Reception Revolution?

    Allen, Ben

    The British journal of general practice : the journal of the Royal College of General Practitioners

    2022  Volume 72, Issue 718, Page(s) 229

    Language English
    Publishing date 2022-04-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 1043148-2
    ISSN 1478-5242 ; 0035-8797 ; 0960-1643
    ISSN (online) 1478-5242
    ISSN 0035-8797 ; 0960-1643
    DOI 10.3399/bjgp22X719357
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.B 576: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 100: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Article ; Online: Etiological Pathways to the Emergence of Preteen Problematic Sexual Behavior: An Exploratory Mediational Model.

    Allen, Brian

    Sexual abuse : a journal of research and treatment

    2022  Volume 35, Issue 4, Page(s) 488–502

    Abstract: Studies examining the etiology of problematic sexual behavior (PSB) among pre-teen children often rely on identifying correlational relationships without examining potential causal mechanisms. This study describes an exploratory analysis of a potential ... ...

    Abstract Studies examining the etiology of problematic sexual behavior (PSB) among pre-teen children often rely on identifying correlational relationships without examining potential causal mechanisms. This study describes an exploratory analysis of a potential mediational model where child sexual abuse (CSA) and child physical abuse (CPA) predict the onset of PSB through their impact on the emergence of posttraumatic stress (PTS) symptoms and self-dysregulation. The caregivers of 189 children between the ages of 3 and 11 years presenting for mental health treatment in the United States completed a battery of measures designed to assess each of the variables in the model. Cross-sectional, regression-based mediational analyses showed that the overall model performed adequately (
    MeSH term(s) Child ; Adolescent ; Humans ; United States ; Child, Preschool ; Cross-Sectional Studies ; Sexual Behavior/psychology ; Child Abuse/psychology ; Child Abuse, Sexual/psychology ; Causality
    Language English
    Publishing date 2022-09-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1283507-9
    ISSN 1573-286X ; 1079-0632
    ISSN (online) 1573-286X
    ISSN 1079-0632
    DOI 10.1177/10790632221128313
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 3271: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Article ; Online: Etiological Perspectives on Problematic Sexual Behavior of Preteen Children: Implications for Treatment.

    Allen, Brian

    Clinical child and family psychology review

    2022  Volume 26, Issue 1, Page(s) 50–64

    Abstract: Problematic sexual behavior (PSB) among preteen children is a poorly understood clinical phenomenon that may leave even the most skilled and knowledgeable of clinicians at a loss when attempting to develop an evidence-based treatment approach. Much of ... ...

    Abstract Problematic sexual behavior (PSB) among preteen children is a poorly understood clinical phenomenon that may leave even the most skilled and knowledgeable of clinicians at a loss when attempting to develop an evidence-based treatment approach. Much of this lack of practical direction can be credited to the relatively scarce clinical trial research examining this outcome. Nonetheless, the etiological research on PSB provides clearer directions and suggests the implementation of already well-established interventions may be effective. This paper reviews the current state of the etiological research pertaining to PSB and places these findings within developmental psychopathology, social learning theory, and post-traumatic stress disorder symptomatology frameworks. Specific treatment directives derived from these three viewpoints are then reviewed, including a review of the current evidence base for the treatment of PSB. Finally, a treatment planning algorithm is specified to help clinicians identify the most beneficial approach to treating PSB in a given case.
    MeSH term(s) Humans ; Child ; Sexual Behavior ; Child Abuse, Sexual/therapy ; Stress Disorders, Post-Traumatic
    Language English
    Publishing date 2022-09-12
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural
    ZDB-ID 1445774-x
    ISSN 1573-2827 ; 1096-4037
    ISSN (online) 1573-2827
    ISSN 1096-4037
    DOI 10.1007/s10567-022-00412-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 5047: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  10. Book ; Online: Symmetry in models of natural selection

    Allen, Benjamin

    2023  

    Abstract: Symmetry arguments are frequently used -- often implicitly -- in mathematical modeling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal ... ...

    Abstract Symmetry arguments are frequently used -- often implicitly -- in mathematical modeling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain.

    Comment: 21 pages, 4 figures
    Keywords Quantitative Biology - Populations and Evolution ; Mathematics - Group Theory ; Mathematics - Probability ; 92D15
    Subject code 190
    Publishing date 2023-07-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top