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Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten TR, Ryan ND, Kirisci L, Wang L. DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health. J Pers Med 2024; 14:94. [PMID: 38248795 PMCID: PMC10817272 DOI: 10.3390/jpm14010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects.
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Affiliation(s)
- Oshin Miranda
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
| | - Peihao Fan
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
| | - Xiguang Qi
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
| | - Haohan Wang
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA;
| | | | - Thomas R. Kosten
- Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Neal David Ryan
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Levent Kirisci
- Center for Education and Drug Abuse Research, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Lirong Wang
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
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Jaltotage B, Ihdayhid AR, Lan NSR, Pathan F, Patel S, Arnott C, Figtree G, Kritharides L, Shamsul Islam SM, Chow CK, Rankin JM, Nicholls SJ, Dwivedi G. Artificial Intelligence in Cardiology: An Australian Perspective. Heart Lung Circ 2023; 32:894-904. [PMID: 37507275 DOI: 10.1016/j.hlc.2023.06.703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, while also detailing challenges such as data privacy, ethical considerations, and integration within existing health infrastructures. Overall, this paper aims to provide insights into the potential benefits of artificial intelligence in cardiology, while also acknowledging the barriers that need to be addressed to ensure safe and effective implementation into an Australian health system.
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Affiliation(s)
- Biyanka Jaltotage
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia. https://twitter.com/cardiacimager
| | - Abdul Rahman Ihdayhid
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; School of Medicine, Curtin University, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Nick S R Lan
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Faraz Pathan
- Department of Cardiology, Nepean Hospital and Charles Perkins Centre, Nepean Clinical School, Faculty of Medicine and Health, Sydney University, Sydney, NSW, Australia
| | - Sanjay Patel
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia and The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Clare Arnott
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia and The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Gemma Figtree
- Kolling Institute, Royal North Shore Hospital and Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Leonard Kritharides
- Department of Cardiology, Concord Repatriation General Hospital and ANZAC Research Institute, University of Sydney, Sydney, NSW, Australia
| | | | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - James M Rankin
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
| | | | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia.
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Fan P, Miranda O, Qi X, Kofler J, Sweet RA, Wang L. Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer's Disease through Electronic Medical Records with Deep Learning Models. Pharmaceuticals (Basel) 2023; 16:911. [PMID: 37513822 PMCID: PMC10385983 DOI: 10.3390/ph16070911] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 07/30/2023] Open
Abstract
Around 50% of patients with Alzheimer's disease (AD) may experience psychotic symptoms after onset, resulting in a subtype of AD known as psychosis in AD (AD + P). This subtype is characterized by more rapid cognitive decline compared to AD patients without psychosis. Therefore, there is a great need to identify risk factors for the development of AD + P and explore potential treatment options. In this study, we enhanced our deep learning model, DeepBiomarker, to predict the onset of psychosis in AD utilizing data from electronic medical records (EMRs). The model demonstrated superior predictive capacity with an AUC (area under curve) of 0.907, significantly surpassing conventional risk prediction models. Utilizing a perturbation-based method, we identified key features from multiple medications, comorbidities, and abnormal laboratory tests, which notably influenced the prediction outcomes. Our findings demonstrated substantial agreement with existing studies, underscoring the vital role of metabolic syndrome, inflammation, and liver function pathways in AD + P. Importantly, the DeepBiomarker model not only offers a precise prediction of AD + P onset but also provides mechanistic understanding, potentially informing the development of innovative treatments. With additional validation, this approach could significantly contribute to early detection and prevention strategies for AD + P, thereby improving patient outcomes and quality of life.
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Affiliation(s)
- Peihao Fan
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Oshin Miranda
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Xiguang Qi
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Julia Kofler
- Department of Pathology, Division of Neuropathology, UPMC Presbyterian Hospital, Pittsburgh, PA 15213, USA
| | - Robert A Sweet
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Lirong Wang
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Zhang A, Xing L, Zou J, Wu JC. Shifting machine learning for healthcare from development to deployment and from models to data. Nat Biomed Eng 2022; 6:1330-1345. [PMID: 35788685 DOI: 10.1038/s41551-022-00898-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 05/03/2022] [Indexed: 01/14/2023]
Abstract
In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.
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Affiliation(s)
- Angela Zhang
- Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, CA, USA. .,Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA. .,Greenstone Biosciences, Palo Alto, CA, USA. .,Department of Computer Science, Stanford University, Stanford, CA, USA.
| | - Lei Xing
- Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, USA
| | - James Zou
- Department of Computer Science, Stanford University, Stanford, CA, USA.,Department of Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, CA, USA. .,Greenstone Biosciences, Palo Alto, CA, USA. .,Departments of Medicine, Division of Cardiovascular Medicine Stanford University, Stanford, CA, USA. .,Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
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DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients. J Pers Med 2022; 12:jpm12040524. [PMID: 35455640 PMCID: PMC9025406 DOI: 10.3390/jpm12040524] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 11/25/2022] Open
Abstract
Identifying patients with high risk of suicide is critical for suicide prevention. We examined lab tests together with medication use and diagnosis from electronic medical records (EMR) data for prediction of suicide-related events (SREs; suicidal ideations, attempts and deaths) in post-traumatic stress disorder (PTSD) patients, a population with a high risk of suicide. We developed DeepBiomarker, a deep-learning model through augmenting the data, including lab tests, and integrating contribution analysis for key factor identification. We applied DeepBiomarker to analyze EMR data of 38,807 PTSD patients from the University of Pittsburgh Medical Center. Our model predicted whether a patient would have an SRE within the following 3 months with an area under curve score of 0.930. Through contribution analysis, we identified important lab tests for suicide prediction. These identified factors imply that the regulation of the immune system, respiratory system, cardiovascular system, and gut microbiome were involved in shaping the pathophysiological pathways promoting depression and suicidal risks in PTSD patients. Our results showed that abnormal lab tests combined with medication use and diagnosis could facilitate predicting SRE risk. Moreover, this may imply beneficial effects for suicide prevention by treating comorbidities associated with these biomarkers.
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