Review
Biochemistry & Molecular Biology
Hidetoshi Komatsu, Emi Watanabe, Mamoru Fukuchi
Summary: Learning and environmental adaptation are crucial for survival and quality of life, with decision-making involving coordination among multiple neural network systems. Neurological and psychiatric disorders often impact these processes, but machine learning approaches have the potential to redefine mental illnesses and improve therapeutic outcomes. Early disease detection and personalized treatment regimes may be possible through measurable endophenotypes and the application of artificial intelligence in psychiatric practice.
Article
Multidisciplinary Sciences
Francesco Piccialli, Francesco Calabro, Danilo Crisci, Salvatore Cuomo, Edoardo Prezioso, Roberta Mandile, Riccardo Troncone, Luigi Greco, Renata Auricchio
Summary: Potential Celiac Patients (PCD) with genetic predisposition may not develop clinical symptoms or small intestine mucosal damage progression for several years. Machine Learning (ML) techniques can be used to predict the natural history of PCD patients.
SCIENTIFIC REPORTS
(2021)
Review
Cardiac & Cardiovascular Systems
Evangelos K. Oikonomou, Rohan Khera
Summary: Artificial intelligence and machine learning have the potential to revolutionize healthcare, particularly in the management of diabetes and its cardiovascular complications. This review provides an overview of the various data-driven methods and their application in personalized care for diabetes patients at increased cardiovascular risk. The article discusses the role of artificial intelligence in diagnosis, prognostication, phenotyping, and treatment, as well as the challenges and ethical considerations that arise. It also emphasizes the need for regulatory standards to ensure the effectiveness and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
CARDIOVASCULAR DIABETOLOGY
(2023)
Article
Clinical Neurology
Giuseppe Maria Della Pepa, Valerio Maria Caccavella, Grazia Menna, Tamara Ius, Anna Maria Auricchio, Giovanni Sabatino, Giuseppe La Rocca, Silvia Chiesa, Simona Gaudino, Enrico Marchese, Alessandro Olivi
Summary: This study successfully utilized a machine learning (ML) model to identify a subset of patients with GBM who were at high risk for early recurrence, using a random forest prediction model with high discriminative ability. By optimizing the predictive value derived from the selected input features, the ML-based model outperformed traditional multivariable logistic regression across all performance metrics.
Review
Clinical Neurology
S. E. Cohen, J. B. Zantvoord, B. N. Wezenberg, J. G. Daams, C. L. H. Bockting, D. Denys, G. A. van Wingen
Summary: This study assessed the accuracy of electroencephalography (EEG) in predicting treatment response in major depressive disorder. The results showed that EEG can accurately predict the response to antidepressant treatment, but further validation studies are needed to develop a clinical tool for guiding interventions in MDD.
JOURNAL OF AFFECTIVE DISORDERS
(2023)
Article
Psychiatry
Yao Xiao, Fay Y. Womer, Shuai Dong, Rongxin Zhu, Ran Zhang, Jingyu Yang, Luheng Zhang, Juan Liu, Weixiong Zhang, Zhongchun Liu, Xizhe Zhang, Fei Wang
Summary: The study developed a precision medicine framework for depression based on neuroimaging and achieved promising results in clinical practice. By utilizing subtype classification and precise rTMS treatment, the research provides new insights into individualized diagnosis and treatment of depression.
ASIAN JOURNAL OF PSYCHIATRY
(2024)
Review
Biotechnology & Applied Microbiology
Sarah J. MacEachern, Nils D. Forkert
Summary: Precision medicine utilizes multi-modal data to make individualized treatment decisions, requiring advanced computer techniques such as machine learning to process and analyze large-scale complex datasets, ultimately enhancing understanding of human health and disease.
Article
Clinical Neurology
Cheng-Ta Li, Chi-Sheng Chen, Chih-Ming Cheng, Chung-Ping Chen, Jen-Ping Chen, Mu-Hong Chen, Ya-Mei Bai, Shih-Jen Tsai
Summary: This study investigated the prediction of left prefrontal cortex iTBS responses using linear and non-linear EEG features, and compared different ML models for rTMS and iTBS. The results showed that the SVM model using combined EEG features outperformed frontal theta by logistic regression. Especially, the RF model demonstrated higher accuracy in predicting rTMS and iTBS.
JOURNAL OF AFFECTIVE DISORDERS
(2023)
Review
Biotechnology & Applied Microbiology
Yihao Liu, Minghua Wu
Summary: Deep learning has been successfully applied to various tasks in different fields, including disease diagnosis in medicine. By extracting multilevel features from medical data, deep learning helps doctors automatically assess diseases and monitor patients' physical health.
BIOENGINEERING & TRANSLATIONAL MEDICINE
(2023)
Review
Biochemistry & Molecular Biology
Elettra Barberis, Shahzaib Khoso, Antonio Sica, Marco Falasca, Alessandra Gennari, Francesco Dondero, Antreas Afantitis, Marcello Manfredi
Summary: This review discusses the application of recent technological innovations in mass spectrometry to metabolomics analysis, with a focus on the use of artificial intelligence (AI) strategies. The article also explores the challenges and limitations of implementing metabolomics-AI systems, as well as recent tools and studies in disease classification and biomarker identification.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Health Care Sciences & Services
Kiwon Kim, Je Il Ryu, Bong Ju Lee, Euihyeon Na, Yu-Tao Xiang, Shigenobu Kanba, Takahiro A. Kato, Mian-Yoon Chong, Shih-Ku Lin, Ajit Avasthi, Sandeep Grover, Roy Abraham Kallivayalil, Pornjira Pariwatcharakul, Kok Yoon Chee, Andi J. Tanra, Chay-Hoon Tan, Kang Sim, Norman Sartorius, Naotaka Shinfuku, Yong Chon Park, Seon-Cheol Park
Summary: A prediction model for concurrent psychotic symptoms in patients with depressive disorders was developed using a machine learning algorithm, with severe depression being the most important variable in the model. Patients with psychotic symptoms were characterized by significant differences in mood, behavior, appetite, and severity of depression.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Biochemical Research Methods
Damian Gola, Inke R. Konig
Summary: One important aspect of precision medicine is constructing prediction models with high predictive ability, which can be achieved using machine learning methods such as model-based MDR algorithm. The new algorithm outperforms other state-of-the-art algorithms in simulation studies and real datasets, especially in scenarios with interactions.
BMC BIOINFORMATICS
(2021)
Review
Multidisciplinary Sciences
Pedro Sanchez, Jeremy P. Voisey, Tian Xia, Hannah I. Watson, Alison Q. O'Neil, Sotirios A. Tsaftaris
Summary: Causal machine learning has gained popularity in healthcare and plays a significant role in clinical decision support systems. This paper uses Alzheimer's disease as an example to illustrate the advantages of CML in clinical scenarios and discusses the challenges in healthcare applications, as well as the potential solutions offered by research in causal representation learning, causal discovery, and causal reasoning.
ROYAL SOCIETY OPEN SCIENCE
(2022)
Article
Computer Science, Information Systems
Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, Bing Wang
Summary: Recent studies have shown that fusing GPS and WiFi data can predict depression more accurately. More complete data leads to stronger correlations and improves the accuracy of depression prediction.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Clinical Neurology
Colin B. Josephson, Samuel Wiebe
Summary: Precision medicine aims to provide individualized treatment based on unique patient characteristics, but faces challenges in data integration, privacy issues, and acceptance by clinicians. Despite accumulating vast amounts of data, there is still a need to address the complexities of advanced analytics models and ensure patient consent and data security. Systematic approaches to data collection and education are necessary to facilitate the translation of precision medicine concepts into routine clinical practice for conditions like epilepsy.
Article
Psychology, Clinical
Mayron Piccolo, Emily L. Belleau, Laura M. Holsen, Madhukar H. Trivedi, Ramin V. Parsey, Patrick J. McGrath, Myrna M. Weissman, Diego A. Pizzagalli, Kristin N. Javaras
Summary: This study found that hyperphagic MDD may be associated with altered activity and connectivity between interoceptive and reward regions.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Neurosciences
Ke Chen, Florian Schlagenhauf, Miriam Sebold, Soren Kuitunen-Paul, Hao Chen, Quentin J. M. Huys, Andreas Heinz, Michael N. Smolka, Ulrich S. Zimmermann, Maria Garbusow
Summary: This study examined the effects of Pavlovian-to-instrumental transfer (PIT) in a large sample of alcohol dependent patients and healthy controls, and found that behavioral non-drug-related PIT and left NAcc PIT effects were associated with prospective relapse risk in alcohol dependence. These findings highlight the clinical relevance of PIT mechanisms to treatment outcome and suggest the need for further research to understand the neural mechanisms and modulators of PIT in relapse in alcohol dependence.
BIOLOGICAL PSYCHIATRY
(2023)
Article
Biochemistry & Molecular Biology
Hao Chen, Matthew J. Belanger, Maria Garbusow, Soeren Kuitunen-Paul, Quentin J. M. Huys, Andreas Heinz, Michael A. Rapp, Michael N. Smolka
Summary: Pavlovian cues can affect ongoing instrumental behavior, and susceptibility to interference between Pavlovian and instrumental control can predict drinking trajectories and hazardous alcohol use during young adulthood. The interference PIT effect is characterized by increased error rates and enhanced neural responses in specific brain regions during conflict situations. Stronger neural responses during conflict at age 18 were associated with higher drinking trajectories, while high error rates and enhanced neural responses at age 21 predicted increasing alcohol consumption until age 24. Targeted interventions may be beneficial for individuals at higher risk.
Article
Psychiatry
Jeffrey R. Strawn, Jeffrey A. Mills, Vikram Suresh, Taryn Mayes, Melanie T. Gentry, Madhukar Trivedi, Paul E. Croarkin
Summary: Understanding how age affects antidepressant response is important for treatment selection. This study analyzed participant-level data from NIH-sponsored trials to identify the impact of age on antidepressant response.
JOURNAL OF PSYCHIATRIC RESEARCH
(2023)
Article
Psychiatry
Cherise R. Chin Fatt, Abu Minhajuddin, Manish K. Jha, Taryn Mayes, A. John Rush, Madhukar H. Trivedi
Summary: This study used resting state functional magnetic resonance imaging (fMRI) to subgroup patients with Major Depressive Disorder (MDD) and found that the different subgroups showed differential treatment outcomes.
JOURNAL OF PSYCHIATRIC RESEARCH
(2023)
Article
Nutrition & Dietetics
Chad D. Rethorst, Thomas J. Carmody, Keith E. Argenbright, Taryn L. Mayes, Heidi A. Hamann, Madhukar H. Trivedi
Summary: This study investigated the impact of physical activity intervention on depressive symptoms in breast cancer survivors. The results showed that the Active Living Every Day (ALED) intervention significantly reduced depressive symptoms, while other intervention strategies had no significant impact on depressive symptoms.
INTERNATIONAL JOURNAL OF BEHAVIORAL NUTRITION AND PHYSICAL ACTIVITY
(2023)
Article
Clinical Neurology
Karabi Nandy, A. John Rush, Holli Slater, Taryn L. Mayes, Abu Minhajuddin, Manish Jha, Joseph C. Blader, Ryan Brown, Graham Emslie, Madeleine N. Fuselier, Cynthia Garza, Kim Gushanas, Beth Kennard, Eric A. Storch, Sarah M. Wakefield, Madhukar H. Trivedi
Summary: This study evaluated the psychometric properties of the 9-item Concise Health Risk Tracking SelfReport (CHRT-SR9) as a measure of suicidality in adolescent psychiatric outpatients. The CHRT-SR9 demonstrated excellent model fit and measurement invariance, with good reliability and validity. The findings suggest that CHRT-SR9 is a useful screening measure of suicidal risk in adolescent psychiatric outpatients. Rating: 8 out of 10.
JOURNAL OF AFFECTIVE DISORDERS
(2023)
Letter
Medicine, General & Internal
Chayakrit Krittanawong, Neil Sagar Maitra, Yusuf Kamran Qadeer, Zhen Wang, Sonya Fogg, Eric A. Storch, Christopher M. Celano, Jeff C. Huffman, Manish Jha, Dennis S. Charney, Carl J. Lavie
AMERICAN JOURNAL OF MEDICINE
(2023)
Article
Clinical Neurology
Madhukar H. Trivedi, Abu Minhajuddin, Holli Slater, Regina Baronia, Joseph C. Blader, Jamon Blood, Ryan Brown, Cynthia Claassen, Melissa DeFilippis, David Farmer, Cynthia Garza, Jennifer L. Hughes, Beth D. Kennard, Israel Liberzon, Sarah Martin, Taryn L. Mayes, Jair C. Soares, Cesar A. Soutullo, Eric A. Storch, Sarah M. Wakefield
Summary: American youth are seriously impacted by depression and suicide. The Texas Youth Depression and Suicide Research Network (TX-YDSRN) initiated a participant registry study to develop predictive models for treatment outcomes in youth with depression and/or suicidality. This report presents the baseline characteristics of the first 1000 participants, including age, ethnicity, and depression and anxiety scores.
JOURNAL OF AFFECTIVE DISORDERS
(2023)
Editorial Material
Psychiatry
Sanjay J. Mathew, Manish K. Jha, Amit Anand
Summary: This Viewpoint discusses important issues arising from recent reports comparing electroconvulsive therapy (ECT) and ketamine for improving depressive symptoms in patients with treatment-resistant depression (TRD).
Article
Behavioral Sciences
Kruti Joshi, M. Janelle Cambron-Mellott, Halley Costantino, Alanna Pfau, Manish K. Jha
Summary: This study evaluated the relationship between insomnia symptom severity and the clinical, economic, and patient-centric burden in adults with MDD. The results showed that greater insomnia symptom severity was associated with higher depression severity, anxiety, daytime sleepiness, healthcare utilization, impaired work productivity and activity, and poorer mental and physical health-related quality of life. Therefore, addressing insomnia symptoms is important in treating MDD.
BRAIN AND BEHAVIOR
(2023)
Meeting Abstract
Neurosciences
Caroline Grant, Jean Marrero-Polanco, Taryn Mayes, Thomas Carmody, Abu Minhajuddin, Manish Jha, Paul Croarkin, William Bobo, Cherise Chin Fatt, Arjun Athreya, Madhukar Trivedi
BIOLOGICAL PSYCHIATRY
(2023)
Meeting Abstract
Psychiatry
M. I. Husain, J. A. Foster, B. L. Mason, S. Chen, W. Wang, S. Rotzinger, S. Rizvi, K. Ho, R. Lam, G. MacQueen, R. Milev, B. N. Frey, D. Mueller, G. Turecki, M. Jha, M. Trivedi, S. H. Kennedy
EUROPEAN PSYCHIATRY
(2023)
Meeting Abstract
Psychology, Multidisciplinary
Chad D. Rethorst, Joseph Trombello, Thomas Carmody, Patricia Chen, Madhukar Trivedi
ANNALS OF BEHAVIORAL MEDICINE
(2023)