Skip to content
2000
Volume 21, Issue 6
  • ISSN: 1570-1646
  • E-ISSN: 1875-6247

Abstract

Background

Coronavirus disease 2019 (COVID-19), which emerged in 2019, has caused millions of deaths worldwide. Although effective vaccines have been developed to mitigate severe symptoms, certain populations, particularly the elderly and those with comorbidities, remain at high risk for severe outcomes and increased mortality. Consequently, early identification of the severity and clinical outcomes of the disease in these patients is vital to prevent adverse prognoses. Although traditional machine learning and deep learning models have been widely employed in this area, the potential of large language models (LLMs) remains largely unexplored.

Objective

Our research study focused primarily on constructing specialized prompts and adopting multi-objective learning strategies.

Methods

We started by selecting serological indicators that significantly correlate with clinical outcomes and disease severity to serve as input data for the model. Blood test samples often contain numerous missing values, and traditional models generally rely on imputation to handle these gaps in the data. In contrast, LLMs offer the advantage of robust language processing capability and certain semantic understanding. By setting prompts, we can explicitly inform the model when a feature’s value is missing, without the need for imputation. For the multi-objective learning strategy, the model was designed to first predict disease severity and then clinical outcomes. Given that LLMs utilize both the input text and the generated tokens as input for generating the next token, the predicted severity was used as a basis for generating the clinical outcome. During the fine-tuning of the LLM, the two objectives influenced and improved each other. Our experiments were implemented based on the ChatGLM model.

Results

CovidLLM demonstrated superior performance compared to other traditional models in predicting disease severity and clinical outcomes.

Conclusion

The results demonstrated the effectiveness of LLMs in this task, suggesting promising potential for further development.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cp/10.2174/0115701646366019250304064012
2025-03-15
2025-10-01

  • From This Site

    /content/journals/cp/10.2174/0115701646366019250304064012
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Fernández-de-las-PeñasC. Palacios-CeñaD. Gómez-MayordomoV. FlorencioL.L. CuadradoM.L. Plaza-ManzanoG. Navarro-SantanaM. Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis.Eur. J. Intern. Med.202192557010.1016/j.ejim.2021.06.00934167876
     [Google Scholar]
  2. WengL.M. SuX. WangX.Q. Pain symptoms in patients with coronavirus disease (COVID-19): A literature review.J. Pain Res.20211414715910.2147/JPR.S26920633531833
     [Google Scholar]
  3. Fernández-de-las-PeñasC. Navarro-SantanaM. Gómez-MayordomoV. CuadradoM.L. García-AzorínD. Arendt-NielsenL. Plaza-ManzanoG. Headache as an acute and post‐COVID‐19 symptom in COVID‐19 survivors: A meta‐analysis of the current literature.Eur. J. Neurol.202128113820382510.1111/ene.1504034327787
     [Google Scholar]
  4. CirulliE.T. Schiabor BarrettK.M. RiffleS. BolzeA. NeveuxI. DabeS. GrzymskiJ.J. LuJ.T. WashingtonN.L. Long-term covid-19 symptoms in a large unselected populationmedrxiv202010.1101/2020.10.07.20208702
     [Google Scholar]
  5. MahaseE. COVID-19: Sore throat, fatigue, and myalgia are more common with new uk variant.BMJ.2021372n28810.1136/bmj.n28833514508
     [Google Scholar]
  6. Cebeci KahramanF. ÇaşkurluH. Mucosal involvement in a COVID‐19‐positive patient: A case report.Dermatol. Ther.2020334e1379710.1111/dth.1379732520428
     [Google Scholar]
  7. SugumarS. SankaralingamS. ParthasarathyS. NandamuriC.J.S. RamanathanS. Identification of potential immunogenic epitopes against SARS-COV-2 using in-silico method: An immunoinformatics study.Curr. Proteom.202219435736910.2174/1570164619666220401115509
     [Google Scholar]
  8. SaniasiayaJ. KulasegarahJ. Dizziness and COVID-19.Ear Nose Throat J.20211001293010.1177/014556132095957332931322
     [Google Scholar]
  9. Drogalis-KimD. KramerC. DuranS. Ongoing dizziness following acute COVID-19 infection: A single center pediatric case series.Pediatrics20221502e202205686010.1542/peds.2022‑05686035642018
     [Google Scholar]
  10. KorresG. KitsosD.K. KaskiD. TsogkaA. GiannopoulosS. GiannopoulosS. GiannopapasV. SiderisG. TyrellisS. VoumvourakisK. The prevalence of dizziness and vertigo in COVID-19 patients: A systematic review.Brain Sci.202212794810.3390/brainsci1207094835884754
     [Google Scholar]
  11. AlihosseiniS. ZaliH. MajdA. MovahediM. AbdollahiH. Comparative clinical, proteomic, and serologic evaluation in non-hospitalized COVID-19 patients and healthy individuals.Curr. Proteomics20242111710.2174/0115701646312544240924204420
     [Google Scholar]
  12. BerlinD.A. GulickR.M. MartinezF.J. Severe COVID-19.N. Engl. J. Med.2020383252451246010.1056/NEJMcp200957532412710
     [Google Scholar]
  13. HeX. ChengX. FengX. WanH. ChenS. XiongM. Clinical symptom differences between mild and severe COVID-19 patients in china: A meta-analysis.Front. Public Health2021856126410.3389/fpubh.2020.56126433520906
     [Google Scholar]
  14. ShenF. LiY. GuW. YuX. WuY. SuoG. ZhengY. LiH. HaoC. Proteome profiling of serum exosomes from newborns with lung injury after perinatal asphyxia.Curr. Proteom.202320213614410.2174/1570164620666230714115822
     [Google Scholar]
  15. loganathanS. KuppusamyM. WankharW. GurugubelliK.R. MahadevappaV.H. LepchaL. ChoudharyA. Angiotensin-converting enzyme 2 (ACE2): COVID-19 gate way to multiple organ failure syndromes.Respir. Physiol. Neurobiol.202128310354810.1016/j.resp.2020.10354832956843
     [Google Scholar]
  16. HuB. HuangS. YinL. The cytokine storm and COVID‐19.J. Med. Virol.202193125025610.1002/jmv.2623232592501
     [Google Scholar]
  17. ShemeshA. LevinA. KatzenellV. ItzhakJ. AvrahamZ. Root anatomy and root canal morphology of mandibular canines in israeli population.Refuat. Hapeh. Vehashinayim.2016331192327295928
     [Google Scholar]
  18. LiuW. LiH. Orf7a palsies macrophage to worsen diabetes by smb/bpi/abc domains and parp/cap/cyclin enzyme system.Curr. Proteom.2023201193810.2174/1570164620666230314102530
     [Google Scholar]
  19. WangZ. DengH. OuC. LiangJ. WangY. JiangM. LiS. Clinical symptoms, comorbidities and complications in severe and non-severe patients with COVID-19.Medicine (Baltimore)20209948e2332710.1097/MD.000000000002332733235096
     [Google Scholar]
  20. LongB. BradyW.J. KoyfmanA. GottliebM. Cardiovascular complications in COVID-19.Am. J. Emerg. Med.20203871504150710.1016/j.ajem.2020.04.04832317203
     [Google Scholar]
  21. ScalaE. AbeniD. TedeschiA. ManzottiG. YangB. BorrelliP. MarraA. GianiM. SgadariA. SaltalamacchiaF. AseroR. Atopic status protects from severe complications of COVID‐19.Allergy202176389990210.1111/all.1455132799364
     [Google Scholar]
  22. AlballaN. Al-TuraikiI. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review.Inform. Med. Unlock.20212410056410.1016/j.imu.2021.10056433842685
     [Google Scholar]
  23. ChieregatoM. FrangiamoreF. MorassiM. BaresiC. NiciS. BassettiC. BnàC. GalelliM. A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.Sci. Rep.2022121432910.1038/s41598‑022‑07890‑135288579
     [Google Scholar]
  24. WuH. RuanW. WangJ. ZhengD. LiuB. GengY. ChaiX. ChenJ. LiK. LiS. HelalS. Interpretable machine learning for COVID-19: An empirical study on severity prediction task.IEEE Trans. Artif. Intell.20234476477710.1109/TAI.2021.309269837954545
     [Google Scholar]
  25. CaiJ. ZhuH. LiuS. QiY. ChenR. Lung image segmentation via generative adversarial networks.Front. Physiol.202415140883210.3389/fphys.2024.140883239219839
     [Google Scholar]
  26. PattersonB.K. Guevara-CotoJ. YogendraR. FranciscoE.B. LongE. PiseA. RodriguesH. ParikhP. MoraJ. Mora-RodríguezR.A. Immune-based prediction of COVID-19 severity and chronicity decoded using machine learning.Front. Immunol.20211270078210.3389/fimmu.2021.70078234262570
     [Google Scholar]
  27. CaiJ. ChenT. QiY. LiuS. ChenR. Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.Sci. Rep.20251511110.1038/s41598‑024‑84695‑439747413
     [Google Scholar]
  28. RamanG. AshrafB. DemirY.K. KershawC.D. CherukuS. AtisM. AtisA. AtarM. ChenW. IbrahimI. BatT. MeteM. Machine learning prediction for COVID-19 disease severity at hospital admission.BMC Med. Inform. Decis. Mak.20232314610.1186/s12911‑023‑02132‑436882829
     [Google Scholar]
  29. HuangH. ZhengO. WangD. YinJ. WangZ. DingS. YinH. XuC. YangR. ZhengQ. ShiB. ChatGPT for shaping the future of dentistry: The potential of multi-modal large language model.Int. J. Oral Sci.20231512910.1038/s41368‑023‑00239‑y37507396
     [Google Scholar]
  30. JiangL.Y. LiuX.C. NejatianN.P. Nasir-MoinM. WangD. AbidinA. EatonK. RiinaH.A. LauferI. PunjabiP. MiceliM. KimN.C. OrillacC. SchnurmanZ. LiviaC. WeissH. KurlandD. NeifertS. DastagirzadaY. KondziolkaD. CheungA.T.M. YangG. CaoM. FloresM. CostaA.B. AphinyanaphongsY. ChoK. OermannE.K. Health system-scale language models are all-purpose prediction engines.Nature2023619796935736210.1038/s41586‑023‑06160‑y37286606
     [Google Scholar]
  31. ZaretckiiM. BuslaevP. KozlovskiiI. MorozovA. PopovP. Approaching optimal ph enzyme prediction with large language models.ACS Synth. Biol.20241393013302110.1021/acssynbio.4c0046539197156
     [Google Scholar]
  32. XueH. SalimF.D. Promptcast: A new prompt-based learning paradigm for time series forecasting.IEEE Trans. Knowl. Data Eng.202336116851686410.1109/TKDE.2023.3342137
     [Google Scholar]
  33. LiangY. LiuY. WangX. ZhaoZ. Exploring large language models for human mobility prediction under public events.Comput. Environ. Urban Syst.202411210215310.1016/j.compenvurbsys.2024.102153
     [Google Scholar]
  34. LanZ. LiuL. FanB. LvY. RenY. CuiZ. Traj-llm: A new exploration for empowering trajectory prediction with pre-trained large language models.IEEE Trans. Intell. Veh.202411410.1109/TIV.2024.3418522
     [Google Scholar]
  35. JinM. WangS. MaL. ChuZ. ZhangJ.Y. ShiX. ChenP-Y. LiangY. LiY-F. PanS. Time-llm: Time series forecasting by reprogramming large language model.Mach. Learn.202320172810.48550/arXiv.2310.01728
     [Google Scholar]
  36. ChenJ. QuC. Design and implementation of cpp dictionary based on chatglm2-6b.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)Yichang, China, 20-22 Sept. 2024, pp. 1-5.10.1109/AICIT62434.2024.10730464
     [Google Scholar]
  37. LiuX. JiK. FuY. TamW. DuZ. YangZ. TangJ. Ptuning: Prompt tuning can be comparable to fine-tuning across scales and tasks.Proceedings of the 60th Annual Meeting of the Association for Computational LinguisticsDublin, Ireland, May, 2022, pp. 61-68.10.18653/v1/2022.acl‑short.8
     [Google Scholar]
  38. WangJ. WangD. ZhangF. YooC. LiuH. Soft sensor for predicting indoor PM2.5 concentration in subway with adaptive boosting deep learning model.J. Hazard. Mater.202446513307410.1016/j.jhazmat.2023.13307438029591
     [Google Scholar]
  39. BentéjacC. CsörgőA. Martínez-MuñozG. A comparative analysis of gradient boosting algorithms.Artif. Intell. Rev.20215431937196710.1007/s10462‑020‑09896‑5
     [Google Scholar]
  40. HuJ. SzymczakS. A review on longitudinal data analysis with random forest.Brief. Bioinform.2023242bbad00210.1093/bib/bbad00236653905
     [Google Scholar]
  41. CunninghamP. DelanyS.J. K-nearest neighbour classifiers: A tutorial.ACM Comput. Surv (CSUR)202154612510.1145/3459665
     [Google Scholar]
  42. ZhangW. ZhangZ. YeY. LuoY. PanS. QiH. YuZ. QuJ. Lymphocyte percentage and hemoglobin as a joint parameter for the prediction of severe and nonsevere COVID-19: A preliminary study.Ann. Transl. Med.2020819123110.21037/atm‑20‑600133178763
     [Google Scholar]
  43. TanL. WangQ. ZhangD. DingJ. HuangQ. TangY.Q. WangQ. MiaoH. Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study.Signal Transduct. Target. Ther.2020513310.1038/s41392‑020‑0148‑432296069
     [Google Scholar]
  44. WeiP.-F. New coronavirus pneumonia diagnosis and treatment plan (trial version 7).Chin. Med. J.202013391087109510.1097/CM9.000000000000081932358325
     [Google Scholar]
  45. MiyasakaM. The lymphatic system and COVID-19 vaccines.Front. Immunol.202213104102510.3389/fimmu.2022.104102536341444
     [Google Scholar]
  46. RostamiM. MansouritorghabehH. D-dimer level in COVID-19 infection: A systematic review.Expert Rev. Hematol.202013111265127510.1080/17474086.2020.183138332997543
     [Google Scholar]
  47. YuH.H. QinC. ChenM. WangW. TianD.S. D-dimer level is associated with the severity of COVID-19.Thromb. Res.202019521922510.1016/j.thromres.2020.07.04732777639
     [Google Scholar]
  48. AhirwarA.K. TakhelmayumR. SakardeA. RathodB.D. JhaP.K. KumawatR. GopalN. The study of serum hsCRP, ferritin, IL-6 and plasma D-dimer in COVID-19: A retrospective study.Horm. Mol. Biol. Clin. Investig.202243333734410.1515/hmbci‑2021‑008835357792
     [Google Scholar]
  49. KrishnamoorthyS. DamayanthiD. GopalaS. PaulR. SylajaP.N. High-sensitivity c-reactive protein and lipoprotein-associated phospholipase a2 in predicting recurrence and severity of stenosis in symptomatic intracranial atherosclerotic disease.Curr. Proteomics202118223123610.2174/1570164617666200414123848
     [Google Scholar]
  50. DaviesN.G. KlepacP. LiuY. PremK. JitM. PearsonC.A.B. QuiltyB.J. KucharskiA.J. GibbsH. CliffordS. GimmaA. van ZandvoortK. MundayJ.D. DiamondC. EdmundsW.J. HoubenR.M.G.J. HellewellJ. RussellT.W. AbbottS. FunkS. BosseN.I. SunY.F. FlascheS. RoselloA. JarvisC.I. EggoR.M. Age-dependent effects in the transmission and control of COVID-19 epidemics.Nat. Med.20202681205121110.1038/s41591‑020‑0962‑932546824
     [Google Scholar]
  51. IqbalA. KarobariM.I. AlamM.K. KhattakO. AlshammariS.M. AdilA.H. NooraniT.Y. AlgaraniH.A. AlonaziM.A. SirivastavaK.C. IssraniR. Evaluation of root canal morphology in permanent maxillary and mandibular anterior teeth in Saudi subpopulation using two classification systems: A CBCT study.BMC Oral Health202222117110.1186/s12903‑022‑02187‑135538514
     [Google Scholar]
  52. KarakostaC. TzamalisA. AivaliotisM. TsinopoulosI. Pathogenesis of age-related cataract: A systematic review of proteomic studies.Curr. Proteomics202118445846610.2174/1570164617999201020205100
     [Google Scholar]
  53. AyhanB. TuranS.K. BarkanN.P. DalvaK. BeksaçM. DemiralpD.Ö. A bottom-up proteomic approach in bone marrow plasma cells of newly diagnosed multiple myeloma patients.Curr. Proteomics202118573074110.2174/1570164617999201124142232
     [Google Scholar]
  54. PengF. LeiS. WuC. YuB. ZhongY. WuS. Neutrophil percentage and neutrophil-to-monocyte ratio as independent risk factors in the severity of COVID-19.Res. Squ.202015262210.21203/rs.3.rs‑52622/v1
     [Google Scholar]
  55. FerroM. BabăD.F. CobelliO. MusiG. LucarelliG. TerraccianoD. PorrecaA. BusettoG.M. GiudiceF.D. SoriaF. GonteroP. CantielloF. DamianoR. RoccoP. ScarpaR.M. Abu FarhanA.R. AutorinoR. BresciaA. MarchioniM. MariA. MinerviniA. LongoN. ChianconeF. Perdona’S. BaroneB. PlacidoP.D. CatellaniM. BotteroD. DitonnoP. BattagliaM. ZamboniS. AntonelliA. GrecoF. RussoG.I. SmelzoS. HurleR. CrisanN. ManfrediM. PorpigliaF. CrocettoF. BuonerbaC. DanilescoA. VartolomeiM.D. Neutrophil percentage-to-albumin ratio predicts mortality in bladder cancer patients treated with neoadjuvant chemotherapy followed by radical cystectomy.Future Sci. OA202177FSO70910.2144/fsoa‑2021‑000834258022
     [Google Scholar]
  56. MorrisseyS.M. GellerA.E. HuX. TieriD. DingC. KlaesC.K. CookeE.A. WoesteM.R. MartinZ.C. ChenO. BushS.E. ZhangH. CavallazziR. CliffordS.P. ChenJ. GhareS. BarveS.S. CaiL. KongM. RouchkaE.C. McLeishK.R. UriarteS.M. WatsonC.T. HuangJ. YanJ. A specific low-density neutrophil population correlates with hypercoagulation and disease severity in hospitalized COVID-19 patients.JCI Insight202169e14843510.1172/jci.insight.14843533986193
     [Google Scholar]
  57. PengM. HeJ. XueY. YangX. LiuS. GongZ. Role of hypertension on the severity of covid-19: A review.J. Cardiovasc. Pharmacol.2021785e648e65510.1097/FJC.000000000000111634321401
     [Google Scholar]
  58. DuY. ZhouN. ZhaW. LvY. Hypertension is a clinically important risk factor for critical illness and mortality in COVID-19: A meta-analysis.Nutr. Metab. Cardiovasc. Dis.202131374575510.1016/j.numecd.2020.12.00933549450
     [Google Scholar]
  59. WangY. ChenB. LiY. ZhangL. WangY. YangS. XiaoX. QinQ. The use of renin–angiotensin–aldosterone system (RAAS) inhibitors is associated with a lower risk of mortality in hypertensive COVID‐19 patients: A systematic review and meta‐analysis.J. Med. Virol.20219331370137710.1002/jmv.2662533095513
     [Google Scholar]
  60. Escobedo-de la Pe˜naJ. Rasc’on-PachecoR.A. de Jes’us AscencioMontielI. Gonz’alez-FigueroaE. Fern’andez-G’arateJ.E. MedinaG’omezO.S. Borja-BustamanteP. Santill’an-OropezaJ.A. BorjaAburtoV.H. Hypertension, diabetes and obesity, major risk factors for death inpatients with COVID-19 in Mexico.Arch. Med. Res.202152444344933380361
     [Google Scholar]
  61. AlfanoG. FerrariA. FontanaF. MoriG. LigabueG. GiovanellaS. MagistroniR. MeschiariM. FranceschiniE. MenozziM. CuomoG. OrlandoG. SantoroA. Di GaetanoM. PuzzolanteC. CarliF. BediniA. MilicJ. MussiniC. CappelliG. GuaraldiG. Twenty-four-hour serum creatinine variation is associated with poor outcome in the novel coronavirus disease 2019 (COVID-19) patients.Kidney Res. Clin. Pract.202140223124010.23876/j.krcp.20.17734162049
     [Google Scholar]
  62. HuangW. LiC. WangZ. WangH. ZhouN. JiangJ. NiL. ZhangX.A. WangD.W. Decreased serum albumin level indicates poor prognosis of COVID-19 patients: Hepatic injury analysis from 2,623 hospitalized cases.Sci. China Life Sci.202063111678168710.1007/s11427‑020‑1733‑432567003
     [Google Scholar]
  63. EfatA. ShoeibS. ElKholyA. Hussein AboelelaO.S. ElshamyD. Blood phenotype O and indirect bilirubin are associated with lower, early COVID-19—related mortality: A retrospective study.Int. J. Immunopathol. Pharmacol.2022360394632022113395210.1177/0394632022113395236221310
     [Google Scholar]
  64. SolimandoA.G. SuscaN. BorrelliP. PreteM. LaulettaG. PappagalloF. BuonoR. IngleseG. ForinaB.M. BochicchioD. CapobiancoM. CarrieriV. CiccoS. LeoneP. SilvestrisN. SaracinoA. RiaR. ProcacciV. MiglioreG. VaccaA. RacanelliV. Shortterm variations in neutrophil-to-lymphocyte and urea-to-creatinine ratios anticipate intensive care unit admission of covid-19 patients in the emergency department.Front. Med. (Lausanne)2021762517610.3389/fmed.2020.62517633553217
     [Google Scholar]
  65. EnghardP. HardenbergJ.H. StockmannH. HinzeC. EckardtK.U. Schmidt-OttK.M. Long-term effects of COVID-19 on kidney function.Lancet2021397102871806180710.1016/S0140‑6736(21)00880‑133992141
     [Google Scholar]
  66. WangM. ChenD. XiaY. ZhouT. JiangS.W. A refined framework for precision and translational proteomics in clinical research.Curr. Proteomics202118443644610.2174/1570164617999201110122901
     [Google Scholar]
  67. GargS. GargG. PatelP. GuptaG.D. KurmiB.D. A complete sojourn of monoclonal antibodies: Ai, rare diseases/disorders and immunotoxic effects.Curr. Proteom.2024212587810.2174/0115701646313765240610062419
     [Google Scholar]
  68. McGrowderD.A. MillerF. Anderson CrossM. Anderson-JacksonL. BryanS. DilworthL. Abnormal liver biochemistry tests and acute liver injury in covid-19 patients: Current evidence and potential pathogenesis.Diseases2021935010.3390/diseases903005034287285
     [Google Scholar]
  69. SalıkF. UzundereO. BıçakM. AkelmaH. AkgündüzM. KorhanZ. KandemirD. KaçarC.K. Liver function as a predictor of mortality in COVID-19: A retrospective study.Ann. Hepatol.20212610055310.1016/j.aohep.2021.10055334624543
     [Google Scholar]
  70. ValentinoM. FreitasA. Reasoning with natural language explanations.Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts LiJ. LiuF. Miami, Florida, USAAssociation for Computational Linguistics20242531
     [Google Scholar]
  71. PrabhakarA. GriffithsT.L. McCoyR.T. Deciphering the factors influencing the efficacy of chain-of-thought: Probability, memorization, and noisy reasoning.Findings of the Association for Computational Linguistics: EMNLP 2024Miami, Florida, USA, Nov 2024, pp. 3710-3724.10.18653/v1/2024.findings‑emnlp.212
     [Google Scholar]
  72. SunB. FengY. MoX. ZhengP. WangQ. LiP. PengP. LiuX. ChenZ. HuangH. ZhangF. LuoW. NiuX. HuP. WangL. PengH. HuangZ. FengL. LiF. ZhangF. LiF. ZhongN. ChenL. Kinetics of SARS-CoV-2 specific IgM and IgG responses in COVID-19 patients.Emerg. Microbes Infect.20209194094810.1080/22221751.2020.176251532357808
     [Google Scholar]
  73. PostN. EddyD. HuntleyC. SchalkwykM.C.I.V. ShrotriM. LeemanD. RigbyS. WilliamsS.V. BerminghamW.H. KellamP.A. Antibody response to SARS-COV-2 infection in humans: A systematic review.PLOS ONE20201512e024412610.1371/journal.pone.024412633382764
     [Google Scholar]
  74. Van ElslandeJ. HoubenE. DepypereM. BrackenierA. DesmetS. AndréE. Van RanstM. LagrouK. VermeerschP. Diagnostic performance of seven rapid IgG/IgM antibody tests and the Euroimmun IgA/IgG ELISA in COVID-19 patients.Clin. Microbiol. Infect.20202681082108710.1016/j.cmi.2020.05.02332473953
     [Google Scholar]
  75. GaiteriC. DingY. FrenchB. TsengG.C. SibilleE. Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.Genes Brain Behav.2014131132410.1111/gbb.1210624320616
     [Google Scholar]
  76. PatilS. EallaK.K.R. PantaP. VeeraraghavanV.P. RavulaN.R. DurgaC.S. RamaniP. SahuV. PoolaP.K. Silk hydrogel for tissue engineering: A review.J. Contemp. Dent. Pract.202223446747710.5005/jp‑journals‑10024‑332235945843
     [Google Scholar]
  77. AdelS.M. VaidN.R. El-HarouniN. KassemH. ZaherA.R. Tip, torque & rotations: How accurately do digital superimposition software packages quantify tooth movement?Prog. Orthod.2022231810.1186/s40510‑022‑00402‑x35284950
     [Google Scholar]
  78. CaiJ. LiY. LiuB. WuZ. ZhuS. ChenQ. LeiQ. HouH. GuoZ. JiangH. GuoS. WangF. HuangS. ZhuS. FanX. TaoS. Developing deep lstms with later temporal attention for predicting covid-19 severity, clinical outcome, and antibody level by screening serological indicators over time.IEEE J. Biomed. Heal. Inform.20242874204421510.1109/JBHI.2024.338433338564357
     [Google Scholar]
  79. KravitzN.D. DalloulB. ZaidY.A. ShahC. VaidN.R. What percentage of patients switch from invisalign to braces? a retrospective study evaluating the conversion rate, number of refinement scans, and length of treatment.Am. J. Orthod. Dentofaci. Orthop.2023163452653010.1016/j.ajodo.2022.03.01636539316
     [Google Scholar]
  80. DayanI. RothH.R. ZhongA. HarouniA. GentiliA. AbidinA.Z. LiuA. CostaA.B. WoodB.J. TsaiC.S. WangC.H. HsuC.N. LeeC.K. RuanP. XuD. WuD. HuangE. KitamuraF.C. LaceyG. de Antônio CorradiG.C. NinoG. ShinH.H. ObinataH. RenH. CraneJ.C. TetreaultJ. GuanJ. GarrettJ.W. KaggieJ.D. ParkJ.G. DreyerK. JuluruK. KerstenK. RockenbachM.A.B.C. LinguraruM.G. HaiderM.A. AbdelMaseehM. RiekeN. DamascenoP.F. e SilvaP.M.C. WangP. XuS. KawanoS. SriswasdiS. ParkS.Y. GristT.M. BuchV. JantarabenjakulW. WangW. TakW.Y. LiX. LinX. KwonY.J. QurainiA. FengA. PriestA.N. TurkbeyB. GlicksbergB. BizzoB. KimB.S. Tor-DíezC. LeeC.C. HsuC.J. LinC. LaiC.L. HessC.P. CompasC. BhatiaD. OermannE.K. LeibovitzE. SasakiH. MoriH. YangI. SohnJ.H. MurthyK.N.K. FuL.C. de MendonçaM.R.F. FralickM. KangM.K. AdilM. GangaiN. VateekulP. ElnajjarP. HickmanS. MajumdarS. McLeodS.L. ReedS. GräfS. HarmonS. KodamaT. PuthanakitT. MazzulliT. de LavorV.L. RakvongthaiY. LeeY.R. WenY. GilbertF.J. FloresM.G. LiQ. Federated learning for predicting clinical outcomes in patients with COVID-19.Nat. Med.202127101735174310.1038/s41591‑021‑01506‑334526699
     [Google Scholar]
  81. ZhouH.Y. YuY. WangC. ZhangS. GaoY. PanJ. ShaoJ. LuG. ZhangK. LiW. A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.Nat. Biomed. Eng.20237674375510.1038/s41551‑023‑01045‑x37308585
     [Google Scholar]
/content/journals/cp/10.2174/0115701646366019250304064012
Loading
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients
Curr. Proteomics 21, 591 (2024); https://doi.org/10.2174/0115701646366019250304064012
/content/journals/cp/10.2174/0115701646366019250304064012
/content/journals/cp/10.2174/0115701646366019250304064012
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): clinical outcomes; COVID-19; Large language models; multi-objective prediction; SARS-CoV-2; severity prediction

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test