Kule nqaku zophando wabhaliswe kwi-Nanotechnology Perceptions, i-Tulasi Naga Subhash Polineni ibonise i-visio engundoqo ye-integration ye-generative neural networks kunye ne-artificial intelligence kwi-trauma kunye ne-acute care systems. Le nqaku esebenzayo ibonise njani ingasetyenziswa ukunciphisa imiphumela emzimbeni, ukunciphisa i-emergency diagnosis, kunye nokusetyenziswa kwe-operations ye-healthcare kumaziko emzimbeni ezibalulekileyo. I-AI-driven Decision Support Systems (DSS) I-AI-driven Decision Support Systems (DSS) Ngexesha elide le-Oracle Technologies, i-cloud computing, kunye ne-enterprise integration, i-Polineni inesibho olufanelekileyo yokuphendula iingxaki zonyango zehlabathi zokusetyenziswa kwinkqubo yobugcisa. Nge-research yayo, uye wabelane i-potential ye-generative neural networks, i-real-time data integration, kunye ne-predictive analytics ekuphuculeni ukhetho lwe-clinical kwi-trauma environments. Emergency Care nge AI I-Emergency Departments (EDs) ziquka iiyunithi ezininzi ze-resource-intensive ezaziwayo ezisebenza phantsi komthamo emangalisayo, ukutyelela izigulane ezininzi kunye neengxaki ezininzi ezikhoyo okanye iinkcukacha zonyango. Abaculi kwiiyunithi ezininzi ziquka ukuthatha izixazululo zokuguqulwa kwezilwanyana kwimeko ezincinane zokusekelwe kwimfuneko ezincinane. Yonke inefficiency kwi-screening okanye ukuhlaziywa kwe-diagnosis ingathanda kakhulu iziphumo ze-patient kwiimeko ezininzi. Ngexesha lokuphendula kwayo, uPolineni wabhalisela ukuguqulwa kwindlela yokwakha izakhiwo zokuxheshela zokuxheshela zokuxheshela. Uyaziyaza ukuba ukutshintsha kwezisombululo ze-clinical kunokunyuswa nge-data-driven insights ngokuvumela izixhobo ze-AI. Lezi zindlela ziya kubandakanya iinkombululo ezininzi zeendaba ixesha elifanelekileyo, kuquka i-patient histories, i-environmental variables, iziphumo ze-imaging, kunye needatha ze-physiological ukuvelisa ukubaluleka kwe-severity kunye ne-risk. Ngokufumana iimeko ezinzima ngaphambi kokuphumelela i-symptoms, i-AI ivumela iinkombululo zok Iingcebiso ze-diagnostic kunye ne-screening zinokufumaneka nge-AI, okunciphisa i-error yabantu kunye ne-subjectivity. Oku kunokwenzeka kakhulu kwimeko ezincinane kunye neengcebiso eziluncedo apho ukufikelela kwizilwanyana eziqeqeshiweyo kunokwenzeka. Ukunciphisa iingcebiso ezincinane, ukwandisa i-efficiency, kunye nokukhuthaza ubomi, iimveliso ze-intelligent zihlanganisa kwi-workflow yayo ye-emergency response. Early Diagnosis and Prognosis I-concept core ye-Polineni's research ibamba malunga nemodeli eziphambili ze-generative neural networks ezifana ne-Generative Adversarial Networks (GANs) kunye ne-Variational Autoencoders (VAEs). Lezi zibonelelo zithembisa kakhulu kwi-diagnosis yokuqala kunye ne-modeling ye-prognostic, ezimbini zophando ezininzi kwi-trauma care. Lezi zibonelelo zenzululululwe ngokufanelekileyo ukufumana iingxaki ezisebenzayo ezifana ne-variability kwi-patient presentations, i-data scarcity, kunye nokunciphisa i-labelling ye-expert efanelekileyo kwiidatha ze-clinical. I-Generative Adversarial Networks (GANs) inokukwazi ukuvelisa iinkcukacha ze-patient ze-synthetic ezibonisa iimodeli zonyango okanye i-real-world imaging, ebonakalisa iinkcukacha ze-training kwiimeko ezincinane okanye ezincinane. Ngaphandle kwalokho, i-Variational AutoCoders (VAEs) inokukwazi ukuxhaswa kunye nokuguqula iimifanekiso ze-medical ngempumelelo. Oku kunceda ukucacisa iimpawu kunye neemodeli ezinokufuneka ubomi. Iimodeli ezininzi ziquka iindidi ezincinane zeengxaki ezifana ne-sepsis, i-hemorrhage ye-intracranial, kunye ne-multi-organ failure emangalisayo kwimijikelezo ezincinane. Lezi zixhobo ziye ziye ziye ziye ziye zithembisa izixazululo ze-trauma ngokuthe ngqo kunye ne-long-term. Real-Time Decision Support Systems Umzekelo we-Decision Support Systems isetyenziswa kwimveliso ye-healthcare iminyaka emininzi. Nangona kunjalo, i-Polineni ibonisa ukuba ukuhlanganiswa kwimveliso ye-health Ukusebenza njengeengomatshini ezinxulumeneyo, le nkqubo inokufunda, ukuxhaswa, kunye nokuthintela idatha yabasetyhini ngokuthe ngexesha elandelayo kubasebenzi we-emergency medical by providing immediate and actionable recommendations. Real-time I-AI-Driven DSS Real-time I-AI-Driven DSS I-Adaptability yinkompatibility yinkqubo eziqhelekileyo. Ukuba isetyenziswe kwi-hospital ekhulwini okanye kwi-trauma center ye-metropolitan, i-DSS ye-real-time inokufumaneka ngokutsho imfuneko. Zisa ku-assistive yokuquka kwezilwanyana, ukutshintshwa kweempawu ngexesha lokuphuma, kwaye kwakhona ukutshintshwa kwe-care handover phakathi kwezakhiwo. I-AI ekuphuculeni i-triage kunye ne-emergency response Njengoko i-step esisiseko kwi-emergency care, nayiphi na ukuhlaziywa okanye ukuhlaziywa kwisixesha le-triage kungenza iziphumo ezinzima. Kwi-studi yayo, i-Polineni ibhalwe iimodeli ze-AI-enhanced triage ezinokufanelekanga kuphela ukuhlaziywa kwe-patient, kodwa ukwandisa ukucacileyo ngokufanelekileyo kwi-indicators ezincinci eyenza ukuba ama-indicators emzimbeni. Ukusebenzisa iinkcukacha ezininzi ukuqeqesha iimodeli ze-generative, iinkqubo ze-AI ziyafumaneka iimeko ezininzi zokusetyenziswa kwexesha, ezifana ne-traumatic brain injury, i-internal bleeding, okanye iimpawu ezininzi ze-sepsis. Ngokunceda ukucacisa umdla we-patient, i-AI ibonelela ukuxhaswa kwizimo ze-pre-hospital ukuya kwiindawo ze-treatment ezizodwa. Izicelo ze-Field Polineni yandisa umphumela we-AI-driven DSS nge-series yeengxaki zokusetyenziswa kwihlabathi. 
 
 I-Pediatric Trauma Triage: Ngokusebenza izixhobo ze-AI kwi-pediatric-specific datasets, i-clinicists ingasetyenziselwa ukucacisa i-patterns ye-vital sign atypical. Oku kunceda ukucacisa izifo ezininzi ezifana ne-concussion okanye i-hemorragia ye-internal ngexesha elide. 
 
 I-Emergency Radiology Interpretation: I-AI algorithms ezidlulileyo kwiiyure ezininzi ze-anotated iifoto zinokufumana ngokufanelekileyo i-anomali kwi-MRI okanye i-CT scans. Ngokucacisa iindawo ezininzi ezininzi ezininzi ezininzi, ezi zixhobo zithumela i-radiologists ukunciphisa ixesha le-interpretation kwiiyure ze-peak kunye nokufunda iimeko ezininzi. 
 
 Ukusabela kwe-Prehospital Trauma: Izixhobo ze-AI ezisetyenzisiweyo ze-mobile, i-responders ze-emergency zithunyelwe kwi-patient base-site. Ngokusekelwe kwezi data, i-system ikhiqiza i-indice ye-gravity kwaye ibonise izakhiwo ezifanelekileyo. 
 
 Ukucaciswa kwe-burn and wound severity: Ngokusekelwe kwimiphumo embalwa, i-AI-enhanced image analysis inokufumana umdlavuza, ukucacisa ububanzi we-burn, kunye nokuncedisa iiprotocols zonyango. Final Thoughts Ukuhlolwa kweTulasi Naga Subhash Polineni iye yenza i- blueprint epheleleyo enokufuneka elandelayo ye-trauma kunye ne-acute care enokusetyenziswa kwi-AI-driven decision support systems. "Ukuhlinzeka kuphela ukwandisa ukuhlaziywa kwe-diagnostic, kodwa ukwandisa ngokufanelekileyo indlela yokusebenza kwe-trauma kunye ne-acute care - ngokukhawuleza, ngokukhawuleza kunye ne-personalized," umzekelo uTulasi Naga Subhash Polineni. "i-AI-driven decision support systems zibonisa iingxaki ze-care critical, ukunciphisa iingxaki ze-diagnostic, kwaye ekugqibeleni ukhuseleko kwimeko ze-time-sensitive. Nge-integrating real-time insights into existing clinical workflows, sinokukwazi ukunceda abaculi ukuba zenza ngokugqithisileyo xa kuxhomekeke kakhulu."

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Ukuphucula i-Trauma kunye ne-Acute Care nge-AI-Driven Decision Support Systems

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