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Article ; Online: Future of ChatGPT in Pharmacovigilance.

Wang, Hanyin / Ding, Yanyi Jenny / Luo, Yuan

2023 Volume 46, Issue 8, Page(s) 711–713

MeSH term(s)	Humans ; Pharmacovigilance ; Artificial Intelligence
Language	English
Publishing date	2023-06-12
Publishing country	New Zealand
Document type	Editorial
ZDB-ID	1018059-x
ISSN	1179-1942 ; 0114-5916
ISSN (online)	1179-1942
ISSN	0114-5916
DOI	10.1007/s40264-023-01315-2
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Article ; Online: DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients.

Wang, Hanyin / Gao, Chufan / Dantona, Christopher / Hull, Bryan / Sun, Jimeng

NPJ digital medicine

2024 Volume 7, Issue 1, Page(s) 16

Abstract: In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces DRG-LLaMA, an advanced large language model (LLM) fine-tuned on clinical notes to enhance DRGs assignment. ...

Abstract	In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces DRG-LLaMA, an advanced large language model (LLM) fine-tuned on clinical notes to enhance DRGs assignment. Utilizing LLaMA as the foundational model and optimizing it through Low-Rank Adaptation (LoRA) on 236,192 MIMIC-IV discharge summaries, our DRG-LLaMA -7B model exhibited a noteworthy macro-averaged F1 score of 0.327, a top-1 prediction accuracy of 52.0%, and a macro-averaged Area Under the Curve (AUC) of 0.986, with a maximum input token length of 512. This model surpassed the performance of prior leading models in DRG prediction, showing a relative improvement of 40.3% and 35.7% in macro-averaged F1 score compared to ClinicalBERT and CAML, respectively. Applied to base DRG and complication or comorbidity (CC)/major complication or comorbidity (MCC) prediction, DRG-LLaMA achieved a top-1 prediction accuracy of 67.8% and 67.5%, respectively. Additionally, our findings indicate that DRG-LLaMA 's performance correlates with increased model parameters and input context lengths.
Language	English
Publishing date	2024-01-22
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00989-3
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Article ; Online: Correction to: Machine Learning in Causal Inference: Application in Pharmacovigilance.

Zhao, Yiqing / Yu, Yue / Wang, Hanyin / Li, Yikuan / Deng, Yu / Jiang, Guoqian / Luo, Yuan

Drug safety

2022 Volume 45, Issue 8, Page(s) 927

Language	English
Publishing date	2022-06-20
Publishing country	New Zealand
Document type	Published Erratum
ZDB-ID	1018059-x
ISSN	1179-1942 ; 0114-5916
ISSN (online)	1179-1942
ISSN	0114-5916
DOI	10.1007/s40264-022-01199-8
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Article ; Online: Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants

Hanyin Wang / Yikuan Li / Andrew Naidech / Yuan Luo

BMC Medical Informatics and Decision Making, Vol 22, Iss S2, Pp 1-

2022 Volume 13

Abstract: Abstract Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. ... ...

Abstract	Abstract Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning. Methods We analyzed a cohort of critical care patients from the Medical Information Mart for Intensive Care (MIMIC)-III database. Disparities in social determinants, including race, sex, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots with 95% confidence intervals. Sepsis patients were then identified by the Sepsis-3 criteria. Sixteen machine learning classifiers were trained to predict in-hospital mortality for sepsis patients on a training set constructed by random selection. The performance was measured by area under the receiver operating characteristic curve (AUC). The performance of the trained model was tested on the entire randomly conducted test set and each sub-population built based on each of the following social determinants: race, sex, marital status, insurance type, and language. The fluctuations in performances were further examined by permutation tests. Results We analyzed a total of 11,791 critical care patients from the MIMIC-III database. Within the population identified by each sepsis identification method, significant differences were observed among sub-populations regarding race, marital status, insurance type, and language. On the 5783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients, as well as the Spanish-speaking patients. With pairwise comparison, we detected performance discrepancies in mortality prediction between Asian and White patients, Asians and ...
Keywords	Sepsis ; Machine learning ; Social determinants ; Disparity ; Mortality prediction ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	610
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The impact of micropolymorphism in Anpl-UAA on structural stability and peptide presentation.

Tang, Ziche / Wang, Suqiu / Du, Liubao / Hu, Dongmei / Chen, Xiaoming / Zheng, Hanyin / Ding, Han / Chen, Shiwen / Zhang, Lin / Zhang, Nianzhi

International journal of biological macromolecules

2024 Volume 267, Issue Pt 2, Page(s) 131665

Abstract: Micropolymorphism significantly shapes the peptide-binding characteristics of major histocompatibility complex class I (MHC-I) molecules, affecting the host's resistance to pathogens, which is particularly pronounced in avian species displaying the " ... ...

Abstract	Micropolymorphism significantly shapes the peptide-binding characteristics of major histocompatibility complex class I (MHC-I) molecules, affecting the host's resistance to pathogens, which is particularly pronounced in avian species displaying the "minimal essential MHC" expression pattern. In this study, we compared two duck MHC-I alleles, Anpl-UAA77 and Anpl-UAA78, that exhibit markedly different peptide binding properties despite their high sequence homology. Through mutagenesis experiments and crystallographic analysis of complexes with the influenza virus-derived peptide AEAIIVAMV (AEV9), we identified a critical role for the residue at position 62 in regulating hydrogen-bonding interactions between the peptide backbone and the peptide-binding groove. This modulation affects the characteristics of the B pocket and the stability of the loop region between the 3
Language	English
Publishing date	2024-04-16
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	282732-3
ISSN	1879-0003 ; 0141-8130
ISSN (online)	1879-0003
ISSN	0141-8130
DOI	10.1016/j.ijbiomac.2024.131665
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Article ; Online: Improving Fairness in the Prediction of Heart Failure Length of Stay and Mortality by Integrating Social Determinants of Health.

Li, Yikuan / Wang, Hanyin / Luo, Yuan

Circulation. Heart failure

2022 Volume 15, Issue 11, Page(s) e009473

Abstract: Background: Machine learning (ML) approaches have been broadly applied to the prediction of length of stay and mortality in hospitalized patients. ML may also reduce societal health burdens, assist in health resources planning and improve health ... ...

Abstract	Background: Machine learning (ML) approaches have been broadly applied to the prediction of length of stay and mortality in hospitalized patients. ML may also reduce societal health burdens, assist in health resources planning and improve health outcomes. However, the fairness of these ML models across ethnoracial or socioeconomic subgroups is rarely assessed or discussed. In this study, we aim (1) to quantify the algorithmic bias of ML models when predicting the probability of long-term hospitalization or in-hospital mortality for different heart failure (HF) subpopulations, and (2) to propose a novel method that can improve the fairness of our models without compromising predictive power. Methods: We built 5 ML classifiers to predict the composite outcome of hospitalization length-of-stay and in-hospital mortality for 210 368 HF patients extracted from the Get With The Guidelines-Heart Failure registry data set. We integrated 15 social determinants of health variables, including the Social Deprivation Index and the Area Deprivation Index, into the feature space of ML models based on patients' geographies to mitigate the algorithmic bias. Results: The best-performing random forest model demonstrated modest predictive power but selectively underdiagnosed underserved subpopulations, for example, female, Black, and socioeconomically disadvantaged patients. The integration of social determinants of health variables can significantly improve fairness without compromising model performance. Conclusions: We quantified algorithmic bias against underserved subpopulations in the prediction of the composite outcome for HF patients. We provide a potential direction to reduce disparities of ML-based predictive models by integrating social determinants of health variables. We urge fellow researchers to strongly consider ML fairness when developing predictive models for HF patients.
MeSH term(s)	Humans ; Female ; Heart Failure/diagnosis ; Heart Failure/therapy ; Length of Stay ; Social Determinants of Health ; Hospitalization ; Hospital Mortality
Language	English
Publishing date	2022-11-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2429459-7
ISSN	1941-3297 ; 1941-3289
ISSN (online)	1941-3297
ISSN	1941-3289
DOI	10.1161/CIRCHEARTFAILURE.122.009473
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Article ; Online: Natural language processing of pediatric progress notes for the identification of food allergy.

Bilaver, Lucy A / Wang, Hanyin / Naidech, Andrew M / Luo, Yuan / Das, Rajeshree / Sehgal, Shruti / Gupta, Ruchi

The journal of allergy and clinical immunology. In practice

2023 Volume 11, Issue 7, Page(s) 2242–2244.e2

MeSH term(s)	Humans ; Child ; Natural Language Processing ; Food Hypersensitivity/diagnosis ; Electronic Health Records
Language	English
Publishing date	2023-04-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2843237-X
ISSN	2213-2201 ; 2213-2198
ISSN (online)	2213-2201
ISSN	2213-2198
DOI	10.1016/j.jaip.2023.04.014
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Zs.A 7086: Show issues

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Article ; Online: Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants.

Wang, Hanyin / Li, Yikuan / Naidech, Andrew / Luo, Yuan

BMC medical informatics and decision making

2022 Volume 22, Issue Suppl 2, Page(s) 156

Abstract: Background: Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities ... ...

Abstract	Background: Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning. Methods: We analyzed a cohort of critical care patients from the Medical Information Mart for Intensive Care (MIMIC)-III database. Disparities in social determinants, including race, sex, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots with 95% confidence intervals. Sepsis patients were then identified by the Sepsis-3 criteria. Sixteen machine learning classifiers were trained to predict in-hospital mortality for sepsis patients on a training set constructed by random selection. The performance was measured by area under the receiver operating characteristic curve (AUC). The performance of the trained model was tested on the entire randomly conducted test set and each sub-population built based on each of the following social determinants: race, sex, marital status, insurance type, and language. The fluctuations in performances were further examined by permutation tests. Results: We analyzed a total of 11,791 critical care patients from the MIMIC-III database. Within the population identified by each sepsis identification method, significant differences were observed among sub-populations regarding race, marital status, insurance type, and language. On the 5783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients, as well as the Spanish-speaking patients. With pairwise comparison, we detected performance discrepancies in mortality prediction between Asian and White patients, Asians and patients of other races, as well as English-speaking and Spanish-speaking patients. Conclusions: Disparities in proportions of patients identified by various sepsis criteria were detected among the different social determinant groups. The performances of mortality prediction for sepsis patients can be compromised when applying a universally trained model for each subpopulation. To achieve accurate diagnosis, a versatile diagnostic system for sepsis is needed to overcome the social determinant disparities of patients.
MeSH term(s)	Critical Illness ; Hospital Mortality ; Humans ; Machine Learning ; Retrospective Studies ; Sepsis/diagnosis ; Social Determinants of Health
Language	English
Publishing date	2022-06-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-022-01871-0
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Article ; Online: Left Ventricular Function in Patients on Maintenance Hemodialysis: A Three-Dimensional Speckle-Tracking Imaging Study.

Chen, Meihua / Chen, Xiaojuan / Huang, Hanyin / Wei, Yunpeng / Wang, Lehua / Huang, Xuning

Cardiorenal medicine

2023 Volume 13, Issue 1, Page(s) 248–258

Abstract: Introduction: Although maintenance hemodialysis (MHD) in end-stage renal disease (ESRD) appears to induce some risk factors and strengthen cardiac function, the morbidity of ESRD patients receiving hemodialysis remains high. This study aimed to identify ...

Abstract	Introduction: Although maintenance hemodialysis (MHD) in end-stage renal disease (ESRD) appears to induce some risk factors and strengthen cardiac function, the morbidity of ESRD patients receiving hemodialysis remains high. This study aimed to identify left ventricular (LV) structural and functional abnormalities in ESRD patients on MHD using three-dimensional speckle-tracking imaging (3D-STI). Methods: Eighty-five ESRD patients with normal LV ejection fraction (LVEF >50%) participated in this study, including 55 MHD patients comprising the chronic kidney disease (CKD) V-D group and 30 nondialysis patients comprising the CKD V-ND group. Thirty age- and sex-matched control participants who had normal kidney function were enrolled as the N group. Conventional echocardiography and 3D-STI were conducted, and global longitudinal strain (GLS), global circumferential strain (GCS), global area strain (GAS), and global radial strain (GRS) values were measured. Results: No substantial differences in two-dimensional LVEF were observed among the three groups, and LV hypertrophy was the most common abnormality in patients with ESRD, irrespective of whether they had received or not received MHD. There were no significant differences in the 3D LV mass index between the CKD V-ND and N groups (p > 0.05). Conversely, the 3D LV mass index was considerably higher in the CKD V-D group than in both the N and CKD V-ND groups. The GLS, GAS, and GRS values were significantly lower in the CKD V-ND group than in the N group (p < 0.05). Furthermore, the CKD V-D group had significantly lower GLS, GCS, GAS, and GRS values than the N and CKD V-ND groups (p < 0.05). The interventricular septal thickness and E/e' ratio were independently associated with LV strain values in all patients with ESRD. Conclusions: MHD can exacerbate LV deformation and dysfunction in ESRD patients with preserved LVEF, and 3D-STI can be potentially useful for detecting these asymptomatic preclinical abnormalities.
MeSH term(s)	Humans ; Ventricular Function, Left ; Ventricular Dysfunction, Left/complications ; Ventricular Dysfunction, Left/diagnostic imaging ; Echocardiography, Three-Dimensional/adverse effects ; Echocardiography, Three-Dimensional/methods ; Renal Dialysis/adverse effects ; Kidney Failure, Chronic/complications ; Kidney Failure, Chronic/therapy
Language	English
Publishing date	2023-08-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2595659-0
ISSN	1664-5502 ; 1664-3828
ISSN (online)	1664-5502
ISSN	1664-3828
DOI	10.1159/000531711
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Article: A Case of Primary Refractory Immune Thrombocytopenia: Challenges in Choice of Therapies.

Wang, Hanyin / Tuncer, Hande

Case reports in hematology

2018 Volume 2018, Page(s) 8207017

Abstract: The value of combination therapy for refractory ITP is not well defined. We present the case of a 29-year-old male with severe ITP refractory to initial standard therapy including steroids, IVIG, and subsequent splenectomy, who was treated with the ... ...

Abstract	The value of combination therapy for refractory ITP is not well defined. We present the case of a 29-year-old male with severe ITP refractory to initial standard therapy including steroids, IVIG, and subsequent splenectomy, who was treated with the combination therapy of rituximab, romiplostim, and mycophenolate and eventually developed thrombocytosis requiring plateletpheresis. Our case highlights the importance of the need to understand predictors of response to standard upfront treatment of acute ITP.
Language	English
Publishing date	2018-07-03
Publishing country	United States
Document type	Case Reports
ZDB-ID	2627639-2
ISSN	2090-6579 ; 2090-6560
ISSN (online)	2090-6579
ISSN	2090-6560
DOI	10.1155/2018/8207017
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